wineAnalysis.ipynb

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   "source": [
    "# Tugas Besar Probailitas dan Statistika IF2220\n",
    "\n",
    "## Contributors\n",
    "1. Ditra Rizqa Amadia (13521019)\n",
    "2. Kenny Benaya Nathan (13521023)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Setup**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv(\"./dataset/anggur.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Soal 1**\n",
    "Deskripsi Statisika (Descriptive Statistics) yang terdiri dari mean, median, modus, standar deviasi, variansi, range, nilai minimum, maksimum, kuartil, IQR, skewness dan kurtosis <br>\n",
    "<br>\n",
    "- **Mean** atau **rataan** adalah indikator statisik yang dapat digunakan untuk mengukur rata-rata dari sebuah data. <br>\n",
    "- **Median** adalah nilai di tengah deret nilai yang disusu dengan data urut dari terkecil sampai terbesar. <br>\n",
    "- **Modus** adalah nilai data yang paling sering muncil atau nilai data yang memiliki frekuensi paling besar. <br>\n",
    "- **Standar deviasi** adalah nilai untuk menentukan seberapa dekat data dari suatu sampel statisik dengan mean data tersebut. <br>\n",
    "- **Variansi** didefinisikan sebagai nilai yang mendeskripsikan seberapa besar data tersebar dari nilai reratanya. <br>\n",
    "- **Range** adalah perbedaan antara nilai tertinggi dengan nilai terendah pada data. <br>\n",
    "- **Nilai minimum** adalah nilai terendah pada data. <br>\n",
    "- **Nilai maksimum** adalah nilai tertinggi pada data. <br>\n",
    "- **Kuartil** adalah nilai yang membagi data yang berurutan menjadi empat bagian yang sama banyak. <br>\n",
    "- **Interquartil** atau **IQR** adalah jangkauan kuartil sebagai selisih antara Q1 dengan Q3. <br>\n",
    "- **Skewness** atau **kemiringan** adalah ukuran simetri dari suatu fungsi distribusi peluang. <br>\n",
    "- **Kurtosis** atau **keruncingan** adalah ukuran lancip dari suatu fungsi distribusi peluang. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Modus</th>\n",
       "      <th>Std</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Range</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fixed acidity</td>\n",
       "      <td>7.152530</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>1.201598</td>\n",
       "      <td>1.443837</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>11.490000</td>\n",
       "      <td>6.377500</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.622500</td>\n",
       "      <td>0.006317</td>\n",
       "      <td>-0.019292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>volatile acidity</td>\n",
       "      <td>0.520839</td>\n",
       "      <td>0.524850</td>\n",
       "      <td>0.554600</td>\n",
       "      <td>0.095848</td>\n",
       "      <td>0.009187</td>\n",
       "      <td>0.665200</td>\n",
       "      <td>0.139900</td>\n",
       "      <td>0.805100</td>\n",
       "      <td>0.456100</td>\n",
       "      <td>0.524850</td>\n",
       "      <td>0.585375</td>\n",
       "      <td>0.129275</td>\n",
       "      <td>-0.125558</td>\n",
       "      <td>0.161853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citric acid</td>\n",
       "      <td>0.270517</td>\n",
       "      <td>0.272200</td>\n",
       "      <td>0.301900</td>\n",
       "      <td>0.049098</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.292900</td>\n",
       "      <td>0.116700</td>\n",
       "      <td>0.409600</td>\n",
       "      <td>0.237800</td>\n",
       "      <td>0.272200</td>\n",
       "      <td>0.302325</td>\n",
       "      <td>0.064525</td>\n",
       "      <td>-0.102834</td>\n",
       "      <td>-0.104679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>residual sugar</td>\n",
       "      <td>2.567104</td>\n",
       "      <td>2.519430</td>\n",
       "      <td>0.032555</td>\n",
       "      <td>0.987915</td>\n",
       "      <td>0.975977</td>\n",
       "      <td>5.518200</td>\n",
       "      <td>0.032555</td>\n",
       "      <td>5.550755</td>\n",
       "      <td>1.896330</td>\n",
       "      <td>2.519430</td>\n",
       "      <td>3.220873</td>\n",
       "      <td>1.324544</td>\n",
       "      <td>0.144770</td>\n",
       "      <td>-0.042980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chlorides</td>\n",
       "      <td>0.081195</td>\n",
       "      <td>0.082167</td>\n",
       "      <td>0.015122</td>\n",
       "      <td>0.020111</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.125635</td>\n",
       "      <td>0.015122</td>\n",
       "      <td>0.140758</td>\n",
       "      <td>0.066574</td>\n",
       "      <td>0.082167</td>\n",
       "      <td>0.095312</td>\n",
       "      <td>0.028738</td>\n",
       "      <td>-0.144960</td>\n",
       "      <td>-0.246508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>free sulfur dioxide</td>\n",
       "      <td>14.907679</td>\n",
       "      <td>14.860346</td>\n",
       "      <td>0.194679</td>\n",
       "      <td>4.888100</td>\n",
       "      <td>23.893519</td>\n",
       "      <td>27.267847</td>\n",
       "      <td>0.194679</td>\n",
       "      <td>27.462525</td>\n",
       "      <td>11.426717</td>\n",
       "      <td>14.860346</td>\n",
       "      <td>18.313098</td>\n",
       "      <td>6.886381</td>\n",
       "      <td>0.029050</td>\n",
       "      <td>-0.364964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total sulfur dioxide</td>\n",
       "      <td>40.290150</td>\n",
       "      <td>40.190000</td>\n",
       "      <td>35.200000</td>\n",
       "      <td>9.965767</td>\n",
       "      <td>99.316519</td>\n",
       "      <td>66.810000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>69.960000</td>\n",
       "      <td>33.785000</td>\n",
       "      <td>40.190000</td>\n",
       "      <td>47.022500</td>\n",
       "      <td>13.237500</td>\n",
       "      <td>0.030148</td>\n",
       "      <td>0.063950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>density</td>\n",
       "      <td>0.995925</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.995900</td>\n",
       "      <td>0.002020</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.013800</td>\n",
       "      <td>0.988800</td>\n",
       "      <td>1.002600</td>\n",
       "      <td>0.994600</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.997200</td>\n",
       "      <td>0.002600</td>\n",
       "      <td>-0.110931</td>\n",
       "      <td>0.016366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pH</td>\n",
       "      <td>3.303610</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>0.104875</td>\n",
       "      <td>0.010999</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.970000</td>\n",
       "      <td>3.710000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.370000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.103265</td>\n",
       "      <td>0.080910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sulphates</td>\n",
       "      <td>0.598390</td>\n",
       "      <td>0.595000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.100819</td>\n",
       "      <td>0.010164</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.595000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.100874</td>\n",
       "      <td>0.064819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>alcohol</td>\n",
       "      <td>10.592280</td>\n",
       "      <td>10.610000</td>\n",
       "      <td>9.860000</td>\n",
       "      <td>1.510706</td>\n",
       "      <td>2.282233</td>\n",
       "      <td>8.990000</td>\n",
       "      <td>6.030000</td>\n",
       "      <td>15.020000</td>\n",
       "      <td>9.560000</td>\n",
       "      <td>10.610000</td>\n",
       "      <td>11.622500</td>\n",
       "      <td>2.062500</td>\n",
       "      <td>-0.035189</td>\n",
       "      <td>-0.131732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>quality</td>\n",
       "      <td>7.958000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.902802</td>\n",
       "      <td>0.815051</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-0.139566</td>\n",
       "      <td>0.108291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Mean     Median      Modus       Std   \n",
       "0          fixed acidity   7.152530   7.150000   6.540000  1.201598  \\\n",
       "1       volatile acidity   0.520839   0.524850   0.554600  0.095848   \n",
       "2            citric acid   0.270517   0.272200   0.301900  0.049098   \n",
       "3         residual sugar   2.567104   2.519430   0.032555  0.987915   \n",
       "4              chlorides   0.081195   0.082167   0.015122  0.020111   \n",
       "5    free sulfur dioxide  14.907679  14.860346   0.194679  4.888100   \n",
       "6   total sulfur dioxide  40.290150  40.190000  35.200000  9.965767   \n",
       "7                density   0.995925   0.996000   0.995900  0.002020   \n",
       "8                     pH   3.303610   3.300000   3.340000  0.104875   \n",
       "9              sulphates   0.598390   0.595000   0.590000  0.100819   \n",
       "10               alcohol  10.592280  10.610000   9.860000  1.510706   \n",
       "11               quality   7.958000   8.000000   8.000000  0.902802   \n",
       "\n",
       "     Variance      Range       Min        Max         Q1         Q2   \n",
       "0    1.443837   8.170000  3.320000  11.490000   6.377500   7.150000  \\\n",
       "1    0.009187   0.665200  0.139900   0.805100   0.456100   0.524850   \n",
       "2    0.002411   0.292900  0.116700   0.409600   0.237800   0.272200   \n",
       "3    0.975977   5.518200  0.032555   5.550755   1.896330   2.519430   \n",
       "4    0.000404   0.125635  0.015122   0.140758   0.066574   0.082167   \n",
       "5   23.893519  27.267847  0.194679  27.462525  11.426717  14.860346   \n",
       "6   99.316519  66.810000  3.150000  69.960000  33.785000  40.190000   \n",
       "7    0.000004   0.013800  0.988800   1.002600   0.994600   0.996000   \n",
       "8    0.010999   0.740000  2.970000   3.710000   3.230000   3.300000   \n",
       "9    0.010164   0.670000  0.290000   0.960000   0.530000   0.595000   \n",
       "10   2.282233   8.990000  6.030000  15.020000   9.560000  10.610000   \n",
       "11   0.815051   5.000000  5.000000  10.000000   7.000000   8.000000   \n",
       "\n",
       "           Q3        IQR  Skewness  Kurtosis  \n",
       "0    8.000000   1.622500  0.006317 -0.019292  \n",
       "1    0.585375   0.129275 -0.125558  0.161853  \n",
       "2    0.302325   0.064525 -0.102834 -0.104679  \n",
       "3    3.220873   1.324544  0.144770 -0.042980  \n",
       "4    0.095312   0.028738 -0.144960 -0.246508  \n",
       "5   18.313098   6.886381  0.029050 -0.364964  \n",
       "6   47.022500  13.237500  0.030148  0.063950  \n",
       "7    0.997200   0.002600 -0.110931  0.016366  \n",
       "8    3.370000   0.140000  0.103265  0.080910  \n",
       "9    0.670000   0.140000  0.100874  0.064819  \n",
       "10  11.622500   2.062500 -0.035189 -0.131732  \n",
       "11   9.000000   2.000000 -0.139566  0.108291  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initiate data schema\n",
    "desc = {}\n",
    "desc[\"\"] = []\n",
    "desc[\"Mean\"] = []\n",
    "desc[\"Median\"] = []\n",
    "desc[\"Modus\"] = []\n",
    "desc[\"Std\"] = []\n",
    "desc[\"Variance\"] = []\n",
    "desc[\"Range\"] = []\n",
    "desc[\"Min\"] = []\n",
    "desc[\"Max\"] = []\n",
    "desc[\"Q1\"] = []\n",
    "desc[\"Q2\"] = []\n",
    "desc[\"Q3\"] = []\n",
    "desc[\"IQR\"] = []\n",
    "desc[\"Skewness\"] = []\n",
    "desc[\"Kurtosis\"] = []\n",
    "\n",
    "# Insert data\n",
    "for column in data:\n",
    "    desc[\"\"].append(column)\n",
    "    desc[\"Mean\"].append(data[column].mean())\n",
    "    desc[\"Median\"].append(data[column].median())\n",
    "    desc[\"Modus\"].append(data[column].mode(\"index\")[0])\n",
    "    desc[\"Std\"].append(data[column].std())\n",
    "    desc[\"Variance\"].append(data[column].var())\n",
    "    desc[\"Range\"].append(data[column].max() - data[column].min())\n",
    "    desc[\"Min\"].append(data[column].min())\n",
    "    desc[\"Max\"].append(data[column].max())\n",
    "    desc[\"Q1\"].append(data[column].quantile(0.25))\n",
    "    desc[\"Q2\"].append(data[column].quantile(0.5))\n",
    "    desc[\"Q3\"].append(data[column].quantile(0.75))\n",
    "    desc[\"IQR\"].append(data[column].quantile(0.75) - data[column].quantile(0.25))\n",
    "    desc[\"Skewness\"].append((3 * (data[column].mean() - data[column].median()))/data[column].std())\n",
    "    desc[\"Kurtosis\"].append(data[column].kurtosis())\n",
    "    \n",
    "# Show schema\n",
    "descDataFrame = pd.DataFrame(data=desc)\n",
    "descDataFrame"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### **Soal 2**\n",
    "Visualisasi plot distribusi, dalam bentuk histogram dan boxplot"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fixed Acidity\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "data": {
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       "<function matplotlib.pyplot.show(close=None, block=None)>"
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixedAcidity = data[['fixed acidity']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(fixedAcidity, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(fixedAcidity, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Fixed Acidity\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Volatile Acidity\n",
    "Pada histogram, distribusi terlihat membentuk puncak namun tidak simetris. Dari boxplot terlihat terdapat 2 outlier.<br>\n",
    "Sehingga distribusi tidak bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volatileAcidity = data[['volatile acidity']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(volatileAcidity, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(volatileAcidity, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Volatile Acidity\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Citric Acid\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "citricAcid = data[['citric acid']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(citricAcid, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(citricAcid, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Citric Acid\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Residual Sugar\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residualSugar = data[['residual sugar']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(residualSugar, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(residualSugar, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Residual Sugar\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chlorides\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUoR2AAAAAABMitAOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUoR2AAAAAABMitAOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUoR2AAAAAABMitAOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUm7OLsAMSkpKdPToUdWrV08Wi8XZ5QAAajnDMPT7778rMDBQLi78/3VH4LMeAGA2l/t5T2iXdPToUQUHBzu7DAAA7Bw+fFhBQUHOLuOKwGc9AMCsLvV5T2iXVK9ePUnnvlj169d3cjUAgNouNzdXwcHBts8nVB6f9QAAs7ncz3tCu2S7TK5+/fp8kAMATIPLuB2Hz3oAgFld6vOeG+UAAAAAADApQjsAAAAAACZFaAcAAAAAwKS4px0AariSkhKdOXPG2WWgnDw8PHicGwAAuCRCOwDUYGfOnNGBAwdUUlLi7FJQTi4uLgoLC5OHh4ezSwEAACZGaAeAGsowDP3yyy9ydXVVcHAwZ21rkJKSEh09elS//PKLQkJCWCUeAACUidAOADXU2bNnlZ+fr8DAQPn4+Di7HJTT1VdfraNHj+rs2bNyd3d3djkAAMCkOC0DADVUcXGxJHF5dQ11/vt2/vsIAABQGkI7ANRwXFpdM/F9AwAAl8Opof2zzz7T7bffrsDAQFksFq1du9a2raioSBMmTFCHDh1Up04dBQYGatiwYTp69KjdGMePH9fQoUNVv359NWjQQCNGjFBeXl41zwQAAAAAAMdz6j3tp06dUqdOnfTQQw/pzjvvtNuWn5+v9PR0TZkyRZ06ddJvv/2msWPHauDAgfrqq69s/YYOHapffvlFGzZsUFFRkWJiYjRq1CitXLmyuqcDAKZgtVqVk5NTbcfz8/NTSEhItR0PAOA8W7du1bx58zR27FhFREQ4uxygVnBqaO/fv7/69+9f6jZfX19t2LDBrm3hwoXq3r27rFarQkJClJGRoeTkZO3YsUNdu3aVJC1YsEC33nqr5syZo8DAwCqfAwCYidVqVdvwcJ3Oz6+2Y3r7+GhPRoZDgvvBgwcVFhamr7/+Wp07dy61z7JlyzRu3DidOHGiUsfavHmzbrzxRv32229q0KBBlR4LAK4EBQUFSkxMVE5OjhITE3XdddfJy8vL2WUBV7watXr8yZMnZbFYbH9cpaamqkGDBrbALkmRkZFycXHRl19+qcGDB5c6TmFhoQoLC23vc3Nzq7RuAKguOTk5Op2fr6ETZss/pEWVHy/Lul8rZo1XTk5OjTvbHhERoV9++UW+vr7OLgUAaoQVK1bo119/lST9+uuvWrlypR566CEnVwVc+WpMaC8oKNCECRN03333qX79+pKkzMxMNW7c2K6fm5ubGjZsqMzMzDLHSkhI0PTp06u0XgBwJv+QFgpq1d7ZZZhWUVGRPDw8FBAQ4OxSrkiLFi3S7NmzlZmZqU6dOmnBggXq3r17qX2///57TZ06VWlpaTp06JDmzp2rcePGlTn2zJkzFR8fr7FjxyopKalqJgDgAkeOHNHKlStlGIYkyTAMrVy5UrfccouCgoKcXB1wZasRq8cXFRXp7rvvlmEYevXVVys9Xnx8vE6ePGl7HT582AFVAgAuV0lJiV566SW1bNlSnp6eCgkJ0Ysvvmjb/tNPP+nGG2+Uj4+POnXqpNTU1IuO9+qrr6pFixby8PBQmzZt9M9//tNuu8Vi0auvvqqBAweqTp06evHFF7V582ZZLBa7S9+XLVumkJAQ+fj4aPDgwbYzSn/0/vvv2y4Jbd68uaZPn66zZ89KOvdH7HPPPaeQkBB5enoqMDBQY8aMqcRXquZZvXq14uLiNG3aNKWnp6tTp06KiopSdnZ2qf3z8/PVvHlzzZw585L/E2XHjh167bXX1LFjx6ooHUAZDMPQvHnzymw/H+QBVA3Tn2k/H9gPHTqkjRs32s6yS1JAQMAFfwScPXtWx48fv+gHv6enpzw9PausZgDm4IgF2VhkrWrEx8fr9ddf19y5c9W7d2/98ssv2rNnj237s88+qzlz5qhVq1Z69tlndd999+nHH3+Um9uFH1tr1qyxnXWNjIzUBx98oJiYGAUFBenGG2+09Xvuuec0c+ZMJSUlyc3NTT/99JPdOF9++aVGjBihhIQEDRo0SMnJyZo2bZpdn88//1zDhg3T/Pnz9Ze//EX79+/XqFGjJEnTpk3Tv//9b82dO1erVq1S+/btlZmZqW+++caRXzrTS0xM1MiRIxUTEyNJWrx4sT788EO98cYbmjhx4gX9u3Xrpm7duklSqdvPy8vL09ChQ/X666/rhRdeqJriAZTKarVqx44dF7QXFxdrx44dslqtatasmRMqA2oHU4f284F937592rRpkxo1amS3vWfPnjpx4oTS0tLUpUsXSdLGjRtVUlKiHj16OKNkACbhqAXZHLnIGs75/fffNW/ePC1cuFDR0dGSpBYtWqh37946ePCgJOnpp5/WgAEDJEnTp09X+/bt9eOPP6pt27YXjDdnzhwNHz5cjz/+uCQpLi5O27Zt05w5c+xC+/33328LkpIuCO3z5s1Tv3799Mwzz0iSWrdura1btyo5OdnWZ/r06Zo4caKt7ubNm+v555/XM888o2nTpslqtSogIECRkZFyd3dXSEhImZeFX4nOnDmjtLQ0xcfH29pcXFwUGRl5yaslLmX06NEaMGCAIiMjLyu0s34N4DghISHq1q2b0tPTVVxcbGt3dXVVly5d+IwEqphTQ3teXp5+/PFH2/sDBw5o586datiwoZo0aaK//e1vSk9P1wcffKDi4mLbfeoNGzaUh4eHwsPD1a9fP40cOVKLFy9WUVGRYmNjde+997JyPFDLOWJBtpq8yJqZZWRkqLCwUDfffHOZff54+XOTJk0kSdnZ2aWG9oyMDNvZ7vN69ep1waWcf1y0tKy6/ryAac+ePe1C+zfffKMvvvjC7lL+4uJiFRQUKD8/X0OGDFFSUpKaN2+ufv366dZbb9Xtt99e6hUCV6KcnBwVFxfL39/frt3f39/uSoryWrVqldLT00s901cW1q8BHMdisWjs2LG2/2H553aLxeKkyoDawal/RXz11Vd2Z0Hi4uIkSdHR0Xruuee0bt06SbrgsT+bNm1S3759JZ1bxTI2NlY333yzXFxcdNddd2n+/PnVUj8A82NBNvPx9va+ZB93d3fbv8//MVhSUlKp49apU6dS+0vn/mfz9OnTdeedd16wzcvLS8HBwdq7d68+/fRTbdiwQY8//rhmz56tLVu22M0Jl+/w4cMaO3asNmzYUK5HS8XHx9v+rpDOnWkPDg6uihKBWiEoKEj333+/3nrrLRmGIYvFovvvv19NmzZ1dmnAFc+pob1v374XXbjicha1aNiwoVauXOnIsgAAVahVq1by9vZWSkqKHn744UqPFx4eri+++MLuDNAXX3yhdu3alXucL7/80q5t27Ztdu+vu+467d27Vy1btixzHG9vb91+++26/fbbNXr0aLVt21bfffedrrvuunLVUxP5+fnJ1dVVWVlZdu1ZWVkVXqk/LS1N2dnZdl+/4uJiffbZZ1q4cKEKCwvl6up6wX6sXwM43tChQ7V+/Xrl5OTIz89P999/v7NLAmqF2nG9HgDUMlnW/aY9jpeXlyZMmKBnnnlGHh4e6tWrl44dO6bvv//+opfMl2X8+PG6++67de211yoyMlL/+c9/9N577+nTTz8t1zhjxoxRr169NGfOHN1xxx36+OOP7S6Nl6SpU6fqtttuU0hIiP72t7/JxcVF33zzjXbt2qUXXnhBy5YtU3FxsXr06CEfHx+99dZb8vb2rjULNHl4eKhLly5KSUnRoEGDJJ27QiIlJUWxsbEVGvPmm2/Wd999Z9cWExOjtm3basKECaUGdgBVw8vLS3FxcZo3b57Gjh1brqtfAFQcoR0AriB+fn7y9vHRilnjq+2Y3j4+8vPzK9c+U6ZMkZubm6ZOnaqjR4+qSZMmevTRRyt0/EGDBmnevHmaM2eOxo4dq7CwMC1dutR2G9Xluv766/X6669r2rRpmjp1qiIjIzV58mQ9//zztj5RUVH64IMPNGPGDM2aNUvu7u5q27at7YqBBg0aaObMmYqLi1NxcbE6dOig//znPxcspHoli4uLU3R0tLp27aru3bsrKSlJp06dsi0COGzYMDVt2lQJCQmSzi1et3v3btu/f/75Z+3cuVN169ZVy5YtVa9ePV1zzTV2x6hTp44aNWp0QTuAqhcREaGIiAhnlwHUKhaDBysqNzdXvr6+OnnypN0j5QDUXOnp6erSpYviFr1X4Xvaj+z7Xomj71RaWpopL20uKCjQgQMHFBYWZne2wxGPuisPHotXMWV9/6Sa/7m0cOFCzZ49W5mZmercubPmz59ve6pL3759FRoaqmXLlkmSDh48qLCwsAvG6NOnjzZv3lzq+H379lXnzp2VlJR02TXV9K8pAODKc7mfTZxpB4ArTEhICCEaThUbG1vm5fB/DuKhoaGXtYbNxcYAAOBK5uLsAgAAAAAAQOkI7QAAAAAAmBShHQAAAAAAk+KedgCo4VhPtGbi+wagKhmGoYKCAoePWVhYKEny9PSUxWJx6PheXl4OHxO4EhDaAaCGcnd3l8Vi0bFjx3T11Vfzh04NYhiGjh07JovFInd3d2eXA+AKVFBQoP79+zu7jHJZv369vL29nV0GYDqEdgCooVxdXRUUFKQjR47o4MGDzi4H5WSxWBQUFCRXV1dnlwIAAEyM0A4ANVjdunXVqlUrFRUVObsUlJO7uzuBHUCV8fLy0vr16x06ZkFBgQYPHixJWrNmjby8vBw6vqPHA64UhHYAqOFcXV0JfwAAOxaLpUovNffy8uJSdqCasHo8AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJiUm7MLAHBlsVqtysnJqdQYfn5+CgkJcVBFAAAAQM1FaAfgMFarVW3Dw3U6P79S43j7+GhPRgbBHQAAALUeoR2Aw+Tk5Oh0fr6GTpgt/5AWFRojy7pfK2aNV05ODqEdqKEWLVqk2bNnKzMzU506ddKCBQvUvXv3Uvt+//33mjp1qtLS0nTo0CHNnTtX48aNs+uTkJCg9957T3v27JG3t7ciIiI0a9YstWnTphpmAwCAczn1nvbPPvtMt99+uwIDA2WxWLR27Vq77YZhaOrUqWrSpIm8vb0VGRmpffv22fU5fvy4hg4dqvr166tBgwYaMWKE8vLyqnEWAP7MP6SFglq1r9CromEfgDmsXr1acXFxmjZtmtLT09WpUydFRUUpOzu71P75+flq3ry5Zs6cqYCAgFL7bNmyRaNHj9a2bdu0YcMGFRUV6ZZbbtGpU6eqcioAAJiCU0P7qVOn1KlTJy1atKjU7S+99JLmz5+vxYsX68svv1SdOnUUFRWlgoICW5+hQ4fq+++/14YNG/TBBx/os88+06hRo6prCgAA4A8SExM1cuRIxcTEqF27dlq8eLF8fHz0xhtvlNq/W7dumj17tu699155enqW2ic5OVnDhw9X+/bt1alTJy1btkxWq1VpaWlVORUAAEzBqZfH9+/fX/379y91m2EYSkpK0uTJk3XHHXdIkt588035+/tr7dq1uvfee5WRkaHk5GTt2LFDXbt2lSQtWLBAt956q+bMmaPAwMBqmwsAALXdmTNnlJaWpvj4eFubi4uLIiMjlZqa6rDjnDx5UpLUsGHDMvsUFhaqsLDQ9j43N9dhxwcAoDqZ9pFvBw4cUGZmpiIjI21tvr6+6tGjh+2DPzU1VQ0aNLAFdkmKjIyUi4uLvvzyyzLHLiwsVG5urt0LAABUTk5OjoqLi+Xv72/X7u/vr8zMTIcco6SkROPGjVOvXr10zTXXlNkvISFBvr6+tldwcLBDjg8AQHUzbWg//+F+sQ/+zMxMNW7c2G67m5ubGjZseNE/DvggBwCgZho9erR27dqlVatWXbRffHy8Tp48aXsdPny4mioEAMCxTBvaqxIf5AAAOJ6fn59cXV2VlZVl156VlVXmInPlERsbqw8++ECbNm1SUFDQRft6enqqfv36di8AAGoi0z7y7fyHe1ZWlpo0aWJrz8rKUufOnW19/rwa7dmzZ3X8+PGL/nHg6elZ5mI3QE1ktVqVk5NT6XH8/Px4zBqACvPw8FCXLl2UkpKiQYMGSTp3OXtKSopiY2MrPK5hGHriiSe0Zs0abd68WWFhYQ6qGAAA8zNtaA8LC1NAQIBSUlJsIT03N1dffvmlHnvsMUlSz549deLECaWlpalLly6SpI0bN6qkpEQ9evRwVulAtbJarWobHq7T+fmVHsvbx0d7MjII7gAqLC4uTtHR0eratau6d++upKQknTp1SjExMZKkYcOGqWnTpkpISJB0bvG63bt32/79888/a+fOnapbt65atmwp6dwl8StXrtT777+vevXq2W6B8/X1lbe3txNmCQBA9XFqaM/Ly9OPP/5oe3/gwAHt3LlTDRs2VEhIiMaNG6cXXnhBrVq1UlhYmKZMmaLAwEDb/70PDw9Xv379NHLkSC1evFhFRUWKjY3Vvffey8rxqDVycnJ0Oj9fQyfMrtQzzrOs+7Vi1njl5OQQ2gFU2D333KNjx45p6tSpyszMVOfOnZWcnGxbo8ZqtcrF5X935x09elTXXnut7f2cOXM0Z84c9enTR5s3b5Ykvfrqq5Kkvn372h1r6dKlGj58eJXOBwAAZ3NqaP/qq69044032t7HxcVJkqKjo7Vs2TI988wzOnXqlEaNGqUTJ06od+/eSk5OlpeXl22fFStWKDY2VjfffLNcXFx01113af78+dU+F8DZ/ENaKKhVe2eXAQCKjY0t83L480H8vNDQUBmGcdHxLrUdAIArmVNDe9++fS/6QWyxWDRjxgzNmDGjzD4NGzbUypUrq6I8AAAAAACcqlauHg8AAAAAQE1AaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkzLtc9oB1G4ZGRlO3R8AAAAwA0I7AFPJPX5MkvTAAw84ZLy8vDyHjAMAAAA4A6EdgKmczsuVJA145Fm16dilwuNkbN+i9cvnqaCgwFGlAQAAANWO0A7AlBoFNlNQq/YV3j/Lut+B1QAAAADOwUJ0AAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYlJuzCwAAs8vIyKj0GH5+fgoJCXFANQAAAKhNCO0AUIbc48ckSQ888EClx/L28dGejAyCOwAAAMqF0A4AZTidlytJGvDIs2rTsUuFx8my7teKWeOVk5NDaAcAAEC5ENoB4BIaBTZTUKv2zi4DAAAAtRAL0QEAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKTdnFwDUdlarVTk5ORXePyMjw4HVAAAAADATQjvgRFarVW3Dw3U6P7/SY+Xl5TmgIgAAAABmQmgHnCgnJ0en8/M1dMJs+Ye0qNAYGdu3aP3yeSooKHBwdQAAAACcjXvaARPwD2mhoFbtK/RqGBDk7PIBwM6iRYsUGhoqLy8v9ejRQ9u3by+z7/fff6+77rpLoaGhslgsSkpKqvSYAABcSQjtAADAYVavXq24uDhNmzZN6enp6tSpk6KiopSdnV1q//z8fDVv3lwzZ85UQECAQ8YEAOBKQmgHAAAOk5iYqJEjRyomJkbt2rXT4sWL5ePjozfeeKPU/t26ddPs2bN17733ytPT0yFjSlJhYaFyc3PtXgAA1ESEdgAA4BBnzpxRWlqaIiMjbW0uLi6KjIxUampqtY6ZkJAgX19f2ys4OLhCxwcAwNlYiA6Anco8Qo7HzwG1W05OjoqLi+Xv72/X7u/vrz179lTrmPHx8YqLi7O9z83NJbgDAGokQjsASVLu8WOSpAceeKDSY/H4OQDO5unpWebl9gAA1CSEdgCSpNN55+73HPDIs2rTsUuFxuDxc0Dt5ufnJ1dXV2VlZdm1Z2VllbnInDPGBACgJjH1Pe3FxcWaMmWKwsLC5O3trRYtWuj555+XYRi2PoZhaOrUqWrSpIm8vb0VGRmpffv2ObFqoGZrFNiMx88BqBAPDw916dJFKSkptraSkhKlpKSoZ8+ephkTAICaxNRn2mfNmqVXX31Vy5cvV/v27fXVV18pJiZGvr6+GjNmjCTppZde0vz587V8+XKFhYVpypQpioqK0u7du+Xl5eXkGQAAULvExcUpOjpaXbt2Vffu3ZWUlKRTp04pJiZGkjRs2DA1bdpUCQkJks4tNLd7927bv3/++Wft3LlTdevWVcuWLS9rTAAArmSmDu1bt27VHXfcoQEDBkiSQkND9a9//Uvbt2+XdO4se1JSkiZPnqw77rhDkvTmm2/K399fa9eu1b333uu02gEAqI3uueceHTt2TFOnTlVmZqY6d+6s5ORk20JyVqtVLi7/u9Dv6NGjuvbaa23v58yZozlz5qhPnz7avHnzZY0JAMCVzNShPSIiQkuWLNEPP/yg1q1b65tvvtF///tfJSYmSpIOHDigzMxMu8fA+Pr6qkePHkpNTS0ztBcWFqqwsND2nme3AgDgOLGxsYqNjS112/kgfl5oaKjdbW8VGRMAgCuZqUP7xIkTlZubq7Zt28rV1VXFxcV68cUXNXToUElSZmamJJX6GJjz20qTkJCg6dOnV13hAAAAAAA4gKkXonv77be1YsUKrVy5Uunp6Vq+fLnmzJmj5cuXV2rc+Ph4nTx50vY6fPiwgyoGAAAAAMBxTH2mffz48Zo4caLtMvcOHTro0KFDSkhIUHR0tO1RL1lZWWrSpIltv6ysLHXu3LnMcXl2KwAAAACgJjB1aM/Pz7dbrEaSXF1dVVJSIkkKCwtTQECAUlJSbCE9NzdXX375pR577LHqLhcAAAAoN8MwVFBQ4OwyLumPNdaEes/z8vKSxWJxdhlAhZk6tN9+++168cUXFRISovbt2+vrr79WYmKiHnroIUmSxWLRuHHj9MILL6hVq1a2R74FBgZq0KBBzi0eAAAAuAwFBQXq37+/s8sol8GDBzu7hMu2fv16eXt7O7sMoMJMHdoXLFigKVOm6PHHH1d2drYCAwP1yCOPaOrUqbY+zzzzjE6dOqVRo0bpxIkT6t27t5KTk3lGOwAAAACgxjN1aK9Xr56SkpKUlJRUZh+LxaIZM2ZoxowZ1VcYAAAAUAXyOt8nw8Wkf6IbhlRy9ty/XdwkE19ybik5q7o7/+XsMgCHMOlvBAAAAKD2MVzcJFd3Z5dxER7OLuCyGM4uAHAgUz/yDQAAAACA2ozQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmVaHQ3rx5c/36668XtJ84cULNmzevdFEAAAAAAKCCof3gwYMqLi6+oL2wsFA///xzpYsCAAAAAACSW3k6r1u3zvbvjz/+WL6+vrb3xcXFSklJUWhoqMOKAwAAAACgNitXaB80aJAkyWKxKDo62m6bu7u7QkND9fLLLzusOAAAAAAAarNyhfaSkhJJUlhYmHbs2CE/P78qKQoAAACoLQzD+N+b4iLnFXIl+cPX0e7rC9RA5Qrt5x04cMDRdQAAAAC1UmFhoe3f9b5Z5cRKrkyFhYXy8fFxdhlAhVUotEtSSkqKUlJSlJ2dbTsDf94bb7xR6cIAAAAAAKjtKhTap0+frhkzZqhr165q0qSJLBaLo+sCAAAAagVPT0/bv3/vdK/k6u7Eaq4QxUW2qxb++PUFaqIKPfJt8eLFWrZsmb788kutXbtWa9assXsBAIDaa9GiRQoNDZWXl5d69Oih7du3X7T/O++8o7Zt28rLy0sdOnTQRx99ZLc9Ly9PsbGxCgoKkre3t9q1a6fFixdX5RSAamV3AszVnZejXqV9fYEaqEJn2s+cOaOIiAhH1wIAV7SMjIxK7e/n56eQkBAHVQNUjdWrVysuLk6LFy9Wjx49lJSUpKioKO3du1eNGze+oP/WrVt13333KSEhQbfddptWrlypQYMGKT09Xddcc40kKS4uThs3btRbb72l0NBQffLJJ3r88ccVGBiogQMHVvcUAQCoVhUK7Q8//LBWrlypKVOmOLoeALji5B4/Jkl64IEHKjWOt4+P9mRkENxhaomJiRo5cqRiYmIknbs678MPP9Qbb7yhiRMnXtB/3rx56tevn8aPHy9Jev7557VhwwYtXLjQdjZ969atio6OVt++fSVJo0aN0muvvabt27cT2gEAV7wKhfaCggItWbJEn376qTp27Ch3d/v7bhITEx1SHABcCU7n5UqSBjzyrNp07FKhMbKs+7Vi1njl5OQQ2mFaZ86cUVpamuLj421tLi4uioyMVGpqaqn7pKamKi4uzq4tKipKa9eutb2PiIjQunXr9NBDDykwMFCbN2/WDz/8oLlz55ZZS2Fhod2K3Lm5uRWcFQAAzlWh0P7tt9+qc+fOkqRdu3bZbeOeEQAoXaPAZgpq1d7ZZQBVJicnR8XFxfL397dr9/f31549e0rdJzMzs9T+mZmZtvcLFizQqFGjFBQUJDc3N7m4uOj111/XDTfcUGYtCQkJmj59eiVmAwCAOVQotG/atMnRdQAAAJRqwYIF2rZtm9atW6dmzZrps88+0+jRoxUYGKjIyMhS94mPj7c7g5+bm6vg4ODqKhkAAIep8HPaAQAA/sjPz0+urq7Kysqya8/KylJAQECp+wQEBFy0/+nTpzVp0iStWbNGAwYMkCR17NhRO3fu1Jw5c8oM7Z6enjzmCQBwRahQaL/xxhsvehn8xo0bK1wQUFNYrVbl5ORUaozKriYOAGbi4eGhLl26KCUlRYMGDZIklZSUKCUlRbGxsaXu07NnT6WkpGjcuHG2tg0bNqhnz56SpKKiIhUVFcnFxf4pta6uriopKamSeQAAYCYVCu3n72c/r6ioSDt37tSuXbsUHR3tiLoAU7NarWobHq7T+fkOGS8vL88h4wCAs8XFxSk6Olpdu3ZV9+7dlZSUpFOnTtlWkx82bJiaNm2qhIQESdLYsWPVp08fvfzyyxowYIBWrVqlr776SkuWLJEk1a9fX3369NH48ePl7e2tZs2aacuWLXrzzTdZ+BYAUCtUKLSXtVrrc889R/hArZCTk6PT+fkaOmG2/ENaVHicjO1btH75PBUUFDiwOgBwnnvuuUfHjh3T1KlTlZmZqc6dOys5Odm22JzVarU7ax4REaGVK1dq8uTJmjRpklq1aqW1a9fantEuSatWrVJ8fLyGDh2q48ePq1mzZnrxxRf16KOPVvv8AACobg69p/2BBx5Q9+7dNWfOHEcOC5iWf0iLSq0GnmXd78BqAMAcYmNjy7wcfvPmzRe0DRkyREOGDClzvICAAC1dutRR5QEAUKO4XLrL5UtNTZWXl5cjhwQAAAAAoNaq0Jn2O++80+69YRj65Zdf9NVXX2nKlCkOKQwAAAAAgNquQqHd19fX7r2Li4vatGmjGTNm6JZbbnFIYQAAAAAA1HYVCu3VeV/Zzz//rAkTJmj9+vXKz89Xy5YttXTpUnXt2lXSubP806ZN0+uvv64TJ06oV69eevXVV9WqVatqqxEAAAAAgKpQqYXo0tLSbM+Zbt++va699lqHFHXeb7/9pl69eunGG2/U+vXrdfXVV2vfvn266qqrbH1eeuklzZ8/X8uXL1dYWJimTJmiqKgo7d69m/vrAQAAAAA1WoVCe3Z2tu69915t3rxZDRo0kCSdOHFCN954o1atWqWrr77aIcXNmjVLwcHBdmf2w8LCbP82DENJSUmaPHmy7rjjDknSm2++KX9/f61du1b33ntvqeMWFhaqsLDQ9j43N9ch9QIAAAAA4EgVWj3+iSee0O+//67vv/9ex48f1/Hjx7Vr1y7l5uZqzJgxDitu3bp16tq1q4YMGaLGjRvr2muv1euvv27bfuDAAWVmZioyMtLW5uvrqx49eig1NbXMcRMSEuTr62t7BQcHO6xmAAAAAAAcpUKhPTk5Wa+88orCw8Ntbe3atdOiRYu0fv16hxX3008/2e5P//jjj/XYY49pzJgxWr58uSQpMzNTkuTv72+3n7+/v21baeLj43Xy5Enb6/Dhww6rGQAAAAAAR6nQ5fElJSVyd3e/oN3d3V0lJSWVLuqPx+natav+/ve/S5KuvfZa7dq1S4sXL1Z0dHSFx/X09JSnp6ejygQAAAAAoEpU6Ez7TTfdpLFjx+ro0aO2tp9//llPPvmkbr75ZocV16RJE7Vr186uLTw8XFarVZIUEBAgScrKyrLrk5WVZdsGAAAAAEBNVaHQvnDhQuXm5io0NFQtWrRQixYtFBYWptzcXC1YsMBhxfXq1Ut79+61a/vhhx/UrFkzSecWpQsICFBKSopte25urr788kv17NnTYXUAAAAAAOAMFbo8Pjg4WOnp6fr000+1Z88eSefOgP9xQThHePLJJxUREaG///3vuvvuu7V9+3YtWbJES5YskSRZLBaNGzdOL7zwglq1amV75FtgYKAGDRrk0FoAAAAAAKhu5QrtGzduVGxsrLZt26b69evrr3/9q/76179Kkk6ePKn27dtr8eLF+stf/uKQ4rp166Y1a9YoPj5eM2bMUFhYmJKSkjR06FBbn2eeeUanTp3SqFGjdOLECfXu3VvJyck8ox0AAAAAUOOVK7QnJSVp5MiRql+//gXbfH199cgjjygxMdFhoV2SbrvtNt12221lbrdYLJoxY4ZmzJjhsGMCAAAAAGAG5bqn/ZtvvlG/fv3K3H7LLbcoLS2t0kUBAAAAAIByhvasrKxSH/V2npubm44dO1bpogAAAAAAQDlDe9OmTbVr164yt3/77bdq0qRJpYsCAAAAAADlDO233nqrpkyZooKCggu2nT59WtOmTbvo/ecAAAAAAODylWshusmTJ+u9995T69atFRsbqzZt2kiS9uzZo0WLFqm4uFjPPvtslRQKAAAAAEBtU67Q7u/vr61bt+qxxx5TfHy8DMOQdG4F96ioKC1atEj+/v5VUigAAAAAALVNuUK7JDVr1kwfffSRfvvtN/34448yDEOtWrXSVVddVRX1AQAAAABQa5U7tJ931VVXqVu3bo6sBQAAAAAA/EG5FqIDAAAAAADVh9AOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmFSFH/kGAAAAwLEsJWdlOLuIshiGVHL23L9d3CSLxbn1XITlfJ3AFYDQDgAAAJhE3Z3/cnYJAEyGy+MBAAAAADApzrQDAAAATuTl5aX169c7u4xLKigo0ODBgyVJa9askZeXl5Mrujw1pU6gLIR2AAAAwIksFou8vb2dXUa5eHl51biagZqKy+MBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUoR2AAAAAABMitAOAAAAAIBJEdoBAAAAADApN2cXADiD1WpVTk5OhffPyMhwYDUAAAAAUDpCO2odq9WqtuHhOp2fX+mx8vLyHFARAAAAAJSO0I5aJycnR6fz8zV0wmz5h7So0BgZ27do/fJ5KigocHB1AAAAAPA/3NOOWss/pIWCWrWv0KthQJCzywcA01q0aJFCQ0Pl5eWlHj16aPv27Rft/84776ht27by8vJShw4d9NFHH13QJyMjQwMHDpSvr6/q1Kmjbt26yWq1VtUUAAAwDUI7AABwmNWrVysuLk7Tpk1Tenq6OnXqpKioKGVnZ5faf+vWrbrvvvs0YsQIff311xo0aJAGDRqkXbt22frs379fvXv3Vtu2bbV582Z9++23mjJliry8vKprWgAAOA2hHQAAOExiYqJGjhypmJgYtWvXTosXL5aPj4/eeOONUvvPmzdP/fr10/jx4xUeHq7nn39e1113nRYuXGjr8+yzz+rWW2/VSy+9pGuvvVYtWrTQwIED1bhx4+qaFgAATkNoBwAADnHmzBmlpaUpMjLS1ubi4qLIyEilpqaWuk9qaqpdf0mKioqy9S8pKdGHH36o1q1bKyoqSo0bN1aPHj20du3ai9ZSWFio3NxcuxcAADURoR0AADhETk6OiouL5e/vb9fu7++vzMzMUvfJzMy8aP/s7Gzl5eVp5syZ6tevnz755BMNHjxYd955p7Zs2VJmLQkJCfL19bW9goODKzk7AACcg9AOAABMq6SkRJJ0xx136Mknn1Tnzp01ceJE3XbbbVq8eHGZ+8XHx+vkyZO21+HDh6urZAAAHIpHvgEAAIfw8/OTq6ursrKy7NqzsrIUEBBQ6j4BAQEX7e/n5yc3Nze1a9fOrk94eLj++9//llmLp6enPD09KzINAABMhTPtAADAITw8PNSlSxelpKTY2kpKSpSSkqKePXuWuk/Pnj3t+kvShg0bbP09PDzUrVs37d27167PDz/8oGbNmjl4BgAAmA9n2gEAgMPExcUpOjpaXbt2Vffu3ZWUlKRTp04pJiZGkjRs2DA1bdpUCQkJkqSxY8eqT58+evnllzVgwACtWrVKX331lZYsWWIbc/z48brnnnt0ww036MYbb1RycrL+85//aPPmzc6YIgAA1YrQDgAAHOaee+7RsWPHNHXqVGVmZqpz585KTk62LTZntVrl4vK/C/0iIiK0cuVKTZ48WZMmTVKrVq20du1aXXPNNbY+gwcP1uLFi5WQkKAxY8aoTZs2+ve//63evXtX+/wAAKhuhHYAAOBQsbGxio2NLXVbaWfHhwwZoiFDhlx0zIceekgPPfSQI8oDAKBG4Z52AAAAAABMitAOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUjUqtM+cOVMWi0Xjxo2ztRUUFGj06NFq1KiR6tatq7vuuktZWVnOKxIAAAAAAAepMaF9x44deu2119SxY0e79ieffFL/+c9/9M4772jLli06evSo7rzzTidVCQAAAACA49SI0J6Xl6ehQ4fq9ddf11VXXWVrP3nypP7xj38oMTFRN910k7p06aKlS5dq69at2rZtmxMrBgAAAACg8mpEaB89erQGDBigyMhIu/a0tDQVFRXZtbdt21YhISFKTU0tc7zCwkLl5ubavQAAAAAAMBs3ZxdwKatWrVJ6erp27NhxwbbMzEx5eHioQYMGdu3+/v7KzMwsc8yEhARNnz7d0aUCAAAAAOBQpj7TfvjwYY0dO1YrVqyQl5eXw8aNj4/XyZMnba/Dhw87bGwAAAAAABzF1KE9LS1N2dnZuu666+Tm5iY3Nzdt2bJF8+fPl5ubm/z9/XXmzBmdOHHCbr+srCwFBASUOa6np6fq169v9wIAAAAAwGxMfXn8zTffrO+++86uLSYmRm3bttWECRMUHBwsd3d3paSk6K677pIk7d27V1arVT179nRGyQAAAAAAOIypQ3u9evV0zTXX2LXVqVNHjRo1srWPGDFCcXFxatiwoerXr68nnnhCPXv21PXXX++MkgEAAAAAcBhTh/bLMXfuXLm4uOiuu+5SYWGhoqKi9Morrzi7LAAAAAAAKq3GhfbNmzfbvffy8tKiRYu0aNEi5xQEAAAAAEAVMfVCdAAAAAAA1GY17kw7ANRmGRkZlR7Dz89PISEhDqgGAAAAVY3QDgA1QO7xY5KkBx54oNJjefv4aE9GBsEdAACgBiC0A0ANcDovV5I04JFn1aZjlwqPk2XdrxWzxisnJ4fQDgAAUAMQ2gGgBmkU2ExBrdo7uwwAAABUExaiAwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AABxq0aJFCg0NlZeXl3r06KHt27dftP8777yjtm3bysvLSx06dNBHH31UZt9HH31UFotFSUlJDq4aAABzIrQDAACHWb16teLi4jRt2jSlp6erU6dOioqKUnZ2dqn9t27dqvvuu08jRozQ119/rUGDBmnQoEHatWvXBX3XrFmjbdu2KTAwsKqnAQCAaRDaAQCAwyQmJmrkyJGKiYlRu3bttHjxYvn4+OiNN94otf+8efPUr18/jR8/XuHh4Xr++ed13XXXaeHChXb9fv75Zz3xxBNasWKF3N3dL1lHYWGhcnNz7V4AANREhHYAAOAQZ86cUVpamiIjI21tLi4uioyMVGpqaqn7pKam2vWXpKioKLv+JSUlevDBBzV+/Hi1b9/+smpJSEiQr6+v7RUcHFyBGQEA4HyEdgAA4BA5OTkqLi6Wv7+/Xbu/v78yMzNL3SczM/OS/WfNmiU3NzeNGTPmsmuJj4/XyZMnba/Dhw+XYyYAAJiHm7MLAAAAKEtaWprmzZun9PR0WSyWy97P09NTnp6eVVgZAADVgzPtAADAIfz8/OTq6qqsrCy79qysLAUEBJS6T0BAwEX7f/7558rOzlZISIjc3Nzk5uamQ4cO6amnnlJoaGiVzAMAADMhtAMAAIfw8PBQly5dlJKSYmsrKSlRSkqKevbsWeo+PXv2tOsvSRs2bLD1f/DBB/Xtt99q586dtldgYKDGjx+vjz/+uOomAwCASXB5PAAAcJi4uDhFR0era9eu6t69u5KSknTq1CnFxMRIkoYNG6amTZsqISFBkjR27Fj16dNHL7/8sgYMGKBVq1bpq6++0pIlSyRJjRo1UqNGjeyO4e7uroCAALVp06Z6JwcAgBMQ2gEAgMPcc889OnbsmKZOnarMzEx17txZycnJtsXmrFarXFz+d6FfRESEVq5cqcmTJ2vSpElq1aqV1q5dq2uuucZZUwAAwFQI7QAAwKFiY2MVGxtb6rbNmzdf0DZkyBANGTLkssc/ePBgBSsDAKDm4Z52AAAAAABMijPtqFGsVqtycnIqNUZGRoaDqgEAAACAqkVoR41htVrVNjxcp/PzHTJeXl6eQ8YBAAAAgKpCaEeNkZOTo9P5+Ro6Ybb8Q1pUeJyM7Vu0fvk8FRQUOLA6AAAAAHA8QjtqHP+QFgpq1b7C+2dZ9zuwGqB2csStKpLk5+enkJAQB1QEAABwZSK0AwDKxZG3qnj7+GhPRgbBHQAAoAyEdgBAuTjqVpUs636tmDVeOTk5hHYAAIAyENoBABVS2VtVAAAAcGk8px0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmZepHviUkJOi9997Tnj175O3trYiICM2aNUtt2rSx9SkoKNBTTz2lVatWqbCwUFFRUXrllVfk7+/vxMoBwNwyMjKcsi8AAADKx9ShfcuWLRo9erS6deums2fPatKkSbrlllu0e/du1alTR5L05JNP6sMPP9Q777wjX19fxcbG6s4779QXX3zh5OoBwHxyjx+TJD3wwAOVHisvL6/SYwAAAODiTB3ak5OT7d4vW7ZMjRs3Vlpamm644QadPHlS//jHP7Ry5UrddNNNkqSlS5cqPDxc27Zt0/XXX++MsgHAtE7n5UqSBjzyrNp07FKhMTK2b9H65fNUUFDgyNIAAABQClOH9j87efKkJKlhw4aSpLS0NBUVFSkyMtLWp23btgoJCVFqamqZob2wsFCFhYW297m5uVVYNQCYT6PAZgpq1b5C+2ZZ9zu4GgAAAJSlxixEV1JSonHjxqlXr1665pprJEmZmZny8PBQgwYN7Pr6+/srMzOzzLESEhLk6+trewUHB1dl6QAAAAAAVEiNCe2jR4/Wrl27tGrVqkqPFR8fr5MnT9pehw8fdkCFAAAAAAA4Vo24PD42NlYffPCBPvvsMwUFBdnaAwICdObMGZ04ccLubHtWVpYCAgLKHM/T01Oenp5VWTIAAAAAAJVm6jPthmEoNjZWa9as0caNGxUWFma3vUuXLnJ3d1dKSoqtbe/evbJarerZs2d1lwsAAAAAgEOZ+kz76NGjtXLlSr3//vuqV6+e7T51X19feXt7y9fXVyNGjFBcXJwaNmyo+vXr64knnlDPnj1ZOR4AAAAAUOOZOrS/+uqrkqS+ffvatS9dulTDhw+XJM2dO1cuLi666667VFhYqKioKL3yyivVXCkAAAAAAI5n6tBuGMYl+3h5eWnRokVatGhRNVQEAAAAAED1MfU97QAAAAAA1GamPtOOK4vValVOTk6F98/IyHBgNQAAAABgfoR2VAur1aq24eE6nZ9f6bHy8vIcUBEAAAAAmB+hHdUiJydHp/PzNXTCbPmHtKjQGBnbt2j98nkqKChwcHUAAAAAYE6EdlQr/5AWCmrVvkL7Zln3O7gaAAAAADA3FqIDAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFIsRAcAAABcYQzDcPgTd/44XlU8zcfLy0sWi8Xh4wI1HaEdAAAAuMIUFBSof//+VTb+4MGDHT7m+vXr5e3t7fBxgZqOy+MBAAAAADApzrQDAAAAVxgvLy+tX7/eoWMahqHCwkJJkqenp8MvZffy8nLoeMCVgtAOAAAAXGEsFkuVXGru4+Pj8DEBXByXxwMAAAAAYFKEdgAAAAAATIrQDgAAAOCybN26Vffcc4+2bt3q7FKAWoPQDgAAAOCSCgoKlJiYqKysLCUmJlbJs9oBXIjQDgAAAOCSVqxYoV9//VWS9Ouvv2rlypVOrgioHQjtAADAoRYtWqTQ0FB5eXmpR48e2r59+0X7v/POO2rbtq28vLzUoUMHffTRR7ZtRUVFmjBhgjp06KA6deooMDBQw4YN09GjR6t6GgD+4MiRI1q5cqUMw5B07vFvK1eu1JEjR5xcGXDlI7QDAACHWb16teLi4jRt2jSlp6erU6dOioqKUnZ2dqn9t27dqvvuu08jRozQ119/rUGDBmnQoEHatWuXJCk/P1/p6emaMmWK0tPT9d5772nv3r0aOHBgdU4LqNUMw9C8efPKbD8f5AFUDUI7AABwmMTERI0cOVIxMTFq166dFi9eLB8fH73xxhul9p83b5769eun8ePHKzw8XM8//7yuu+46LVy4UJLk6+urDRs26O6771abNm10/fXXa+HChUpLS5PVaq3OqQG1ltVq1Y4dO1RcXGzXXlxcrB07dvDfIlDFCO0AAMAhzpw5o7S0NEVGRtraXFxcFBkZqdTU1FL3SU1NtesvSVFRUWX2l6STJ0/KYrGoQYMGZfYpLCxUbm6u3QtAxYSEhKhbt25ydXW1a3d1dVX37t0VEhLipMqA2oHQDgAAHCInJ0fFxcXy9/e3a/f391dmZmap+2RmZparf0FBgSZMmKD77rtP9evXL7OWhIQE+fr62l7BwcHlnA2A8ywWi8aOHVtmu8VicUJVQO1BaAcAADVCUVGR7r77bhmGoVdfffWifePj43Xy5Enb6/Dhw9VUJXBlCgoK0v33328L6BaLRffff7+aNm3q5MqAKx+hHQAAOISfn59cXV2VlZVl156VlaWAgIBS9wkICLis/ucD+6FDh7Rhw4aLnmWXJE9PT9WvX9/uBaByhg4dqkaNGkk699/7/fff7+SKgNqB0A4AABzCw8NDXbp0UUpKiq2tpKREKSkp6tmzZ6n79OzZ066/JG3YsMGu//nAvm/fPn366ae20ACgenl5eSkuLk7+/v568skn5eXl5eySgFrBzdkFAACAK0dcXJyio6PVtWtXde/eXUlJSTp16pRiYmIkScOGDVPTpk2VkJAgSRo7dqz69Omjl19+WQMGDNCqVav01VdfacmSJZLOBfa//e1vSk9P1wcffKDi4mLb/e4NGzaUh4eHcyYK1FIRERGKiIhwdhlArUJoBwAADnPPPffo2LFjmjp1qjIzM9W5c2clJyfbFpuzWq1ycfnfhX4RERFauXKlJk+erEmTJqlVq1Zau3atrrnmGknSzz//rHXr1kmSOnfubHesTZs2qW/fvtUyLwAAnIXQjkuyWq3Kycmp1BgZGRkOqgbAlaayvx/8/Px43JDJxMbGKjY2ttRtmzdvvqBtyJAhGjJkSKn9Q0NDZRiGI8sDAKBGIbTjoqxWq9qGh+t0fr5DxsvLy3PIOABqvtzjxyRJDzzwQKXG8fbx0Z6MDII7AAC4IhHacVE5OTk6nZ+voRNmyz+kRYXHydi+ReuXz1NBQYEDqwNQk53Oy5UkDXjkWbXp2KVCY2RZ92vFrPHKyckhtAMAgCsSoR2XxT+khYJata/w/lnW/Q6sBsCVpFFgs0r9fgEAVJ+tW7dq3rx5Gjt2LAvSAdWER74BAAAAuKSCggIlJiYqKytLiYmJXEEJVBNCOwAAAIBLWrFihX799VdJ0q+//qqVK1c6uSKgdiC0AwAAALioI0eOaOXKlbanORiGoZUrV+rIkSNOrgy48hHaAQAAAJTJMAzNmzevzHYeywhULUI7AAAAgDJZrVbt2LFDxcXFdu3FxcXasWOHrFarkyoDagdCOwAAAIAyhYSEqFu3bnJ1dbVrd3V1Vffu3XnkJlDFCO0AAAAAymSxWDR27Ngy2y0WixOqAmoPQjsAAACAiwoKCtL9999vC+gWi0X333+/mjZt6uTKgCsfoR0AAADAJQ0dOlSNGjWSJPn5+en+++93ckVA7UBoBwAAAHBJXl5eiouLk7+/v5588kl5eXk5uySgVnBzdgEAAAAAaoaIiAhFREQ4uwygViG0AwCgc480ysnJqdQYfn5+rKIMAAAcitAOAKj1rFar2oaH63R+fqXG8fbx0Z6MDII7AABwGEI7AKDWy8nJ0en8fA2dMFv+IS0qNEaWdb9WzBqvnJwcQjsAAHAYQnsVMNMllpWtJSMjo9I1AEBVq+zvqvP7+4e0UFCr9o4oCQAAwCEI7Q5mpkssHVWLJOXl5VV6DABwtNzjxyRJDzzwgEPG43cdAAAwG0K7g5npEktH1JKxfYvWL5+ngoKCCtcBAFXldF6uJGnAI8+qTccuFR6H33UAAMCsCO1VxBGXWJrhcs8s6/5K1QAA1aFRYLNK/c7ldx0AADCrKya0L1q0SLNnz1ZmZqY6deqkBQsWqHv37s4uq0K43BMAAAAAIF0hoX316tWKi4vT4sWL1aNHDyUlJSkqKkp79+5V48aNnV1euXG5JwAAAABAukJCe2JiokaOHKmYmBhJ0uLFi/Xhhx/qjTfe0MSJEy/oX1hYqMLCQtv7kydPSpJyc3MrXcv5s9pH9n2vwtMVWwDu/GWaRWcKKzzG+f0lKfPgD9pfx6dStVRmDEeNQy21o5YrbT7UUntqOXbkgKRznwOV/Tw5v79hGJUaB/9z/mvpiM96AAAc4XI/7y1GDf+L4MyZM/Lx8dG7776rQYMG2dqjo6N14sQJvf/++xfs89xzz2n69OnVWCUAAOV3+PBhBQUFObuMK8KRI0cUHBzs7DIAALjApT7va/yZ9pycHBUXF8vf39+u3d/fX3v27Cl1n/j4eMXFxdnel5SU6Pjx42rUqJEsFkuV1nsxubm5Cg4O1uHDh1W/fn2n1VGdmDNzvlLVxjlLtXPeVTFnwzD0+++/KzAw0CHjQQoMDNThw4dVr149p37WAzVdbfw9D1SVy/28r/GhvSI8PT3l6elp19agQQPnFFOK+vXr17pfgsy5dmDOtUdtnLej5+zr6+uwsSC5uLhw1QLgQLXx9zxQFS7n896lGuqoUn5+fnJ1dVVWVpZde1ZWlgICApxUFQAAAAAAlVfjQ7uHh4e6dOmilJQUW1tJSYlSUlLUs2dPJ1YGAAAAAEDlXBGXx8fFxSk6Olpdu3ZV9+7dlZSUpFOnTtlWk68pPD09NW3atAsu3b+SMefagTnXHrVx3rVxzgBqL37nAdWvxq8ef97ChQs1e/ZsZWZmqnPnzpo/f7569Ojh7LIAAAAAAKiwKya0AwAAAABwpanx97QDAAAAAHClIrQDAAAAAGBShHYAAAAAAEyK0A4AAABcYQ4ePCiLxaKdO3eW2WfZsmVq0KBBpY+1efNmWSwWnThxosqPBdRGhPYqtGjRIoWGhsrLy0s9evTQ9u3bL9r/nXfeUdu2beXl5aUOHTroo48+sm0rKirShAkT1KFDB9WpU0eBgYEaNmyYjh49WtXTKDdHzvvPHn30UVksFiUlJTm46sqpijlnZGRo4MCB8vX1VZ06ddStWzdZrdaqmkK5OXrOeXl5io2NVVBQkLy9vdWuXTstXry4KqdQbuWZ8/fff6+77rpLoaGhF/2ZLe/Xsbo5es4JCQnq1q2b6tWrp8aNG2vQoEHau3dvFc6g/Kri+3zezJkzZbFYNG7cOMcWDQBOFBERoV9++UW+vr7OLgW4IhHaq8jq1asVFxenadOmKT09XZ06dVJUVJSys7NL7b9161bdd999GjFihL7++msNGjRIgwYN0q5duyRJ+fn5Sk9P15QpU5Senq733ntPe/fu1cCBA6tzWpfk6Hn/0Zo1a7Rt2zYFBgZW9TTKpSrmvH//fvXu3Vtt27bV5s2b9e2332rKlCny8vKqrmldVFXMOS4uTsnJyXrrrbeUkZGhcePGKTY2VuvWrauuaV1Ueeecn5+v5s2ba+bMmQoICHDImNWtKua8ZcsWjR49Wtu2bdOGDRtUVFSkW265RadOnarKqVy2qpjzeTt27NBrr72mjh07VkXpAOAURUVF8vDwUEBAgCwWi7PLAa5MBqpE9+7djdGjR9veFxcXG4GBgUZCQkKp/e+++25jwIABdm09evQwHnnkkTKPsX37dkOScejQIccU7QBVNe8jR44YTZs2NXbt2mU0a9bMmDt3rsNrr6iqmPM999xjPPDAA1VTsANUxZzbt29vzJgxw67PddddZzz77LMOrLziyjvnPyrrZ7YyY1aHqpjzn2VnZxuSjC1btlSmVIepqjn//vvvRqtWrYwNGzYYffr0McaOHeugigHUdsXFxcasWbOMFi1aGB4eHkZwcLDxwgsvGAcOHDAkGf/+97+Nvn37Gt7e3kbHjh2NrVu32vZdunSp4evrazfeK6+8YjRv3txwd3c3Wrdubbz55pt22yUZr7zyinH77bcbPj4+xrRp04xNmzYZkozffvvNbuzg4GDD29vbGDRokDFnzpwLjrV27Vrj2muvNTw9PY2wsDDjueeeM4qKigzDMIySkhJj2rRpRnBwsOHh4WE0adLEeOKJJxz6tQNqCs60V4EzZ84oLS1NkZGRtjYXFxdFRkYqNTW11H1SU1Pt+ktSVFRUmf0l6eTJk7JYLKa5P6iq5l1SUqIHH3xQ48ePV/v27aum+AqqijmXlJToww8/VOvWrRUVFaXGjRurR48eWrt2bZXNozyq6vscERGhdevW6eeff5ZhGNq0aZN++OEH3XLLLVUzkXKoyJydMaYjVVd9J0+elCQ1bNjQYWNWVFXOefTo0RowYMAF/x0AQGXFx8dr5syZmjJlinbv3q2VK1fK39/ftv3ZZ5/V008/rZ07d6p169a67777dPbs2VLHWrNmjcaOHaunnnpKu3bt0iOPPKKYmBht2rTJrt9zzz2nwYMH67vvvtNDDz10wThffvmlRowYodjYWO3cuVM33nijXnjhBbs+n3/+uYYNG6axY8dq9+7deu2117Rs2TK9+OKLkqR///vfmjt3rl577TXt27dPa9euVYcOHSr75QJqJEJ7FcjJyVFxcbHdL0xJ8vf3V2ZmZqn7ZGZmlqt/QUGBJkyYoPvuu0/169d3TOGVVFXznjVrltzc3DRmzBjHF11JVTHn7Oxs5eXlaebMmerXr58++eQTDR48WHfeeae2bNlSNRMph6r6Pi9YsEDt2rVTUFCQPDw81K9fPy1atEg33HCD4ydRThWZszPGdKTqqK+kpETjxo1Tr169dM011zhkzMqoqjmvWrVK6enpSkhIqGyJAGDn999/17x58/TSSy8pOjpaLVq0UO/evfXwww/b+jz99NMaMGCAWrdurenTp+vQoUP68ccfSx1vzpw5Gj58uB5//HG1bt1acXFxuvPOOzVnzhy7fvfff79iYmLUvHlzhYSEXDDOvHnz1K9fPz3zzDNq3bq1xowZo6ioKLs+06dP18SJExUdHa3mzZvrr3/9q55//nm99tprkiSr1aqAgABFRkYqJCRE3bt318iRIyv7JQNqJEJ7DVRUVKS7775bhmHo1VdfdXY5VSotLU3z5s3TsmXLas19UiUlJZKkO+64Q08++aQ6d+6siRMn6rbbbjPdwmyOtGDBAm3btk3r1q1TWlqaXn75ZY0ePVqffvqps0tDFRk9erR27dqlVatWObuUKnP48GGNHTtWK1asMM2aFACuHBkZGSosLNTNN99cZp8/rqPRpEkTSSpznY6MjAz16tXLrq1Xr17KyMiwa+vatesl6+rRo4ddW8+ePe3ef/PNN5oxY4bq1q1re40cOVK//PKL8vPzNWTIEJ0+fVrNmzfXyJEjtWbNmjKvEACudG7OLuBK5OfnJ1dXV2VlZdm1Z2VllblQUUBAwGX1Px/YDx06pI0bN5rmLLtUNfP+/PPPlZ2dbfd/cYuLi/XUU08pKSlJBw8edOwkyqkq5uzn5yc3Nze1a9fOrk94eLj++9//OrD6iqmKOZ8+fVqTJk3SmjVrNGDAAEnn/sjYuXOn5syZ4/RLiisyZ2eM6UhVXV9sbKw++OADffbZZwoKCqr0eI5QFXNOS0tTdna2rrvuOltbcXGxPvvsMy1cuFCFhYVydXWtVN0Aai9vb+9L9nF3d7f9+/wJkPMnCCqqTp06ldpfOvfUmOnTp+vOO++8YJuXl5eCg4O1d+9effrpp9qwYYMef/xxzZ49W1u2bLGbE1AbcKa9Cnh4eKhLly5KSUmxtZWUlCglJeWC/8t4Xs+ePe36S9KGDRvs+p8P7Pv27dOnn36qRo0aVc0EKqgq5v3ggw/q22+/1c6dO22vwMBAjR8/Xh9//HHVTeYyVcWcPTw81K1btwseg/XDDz+oWbNmDp5B+VXFnIuKilRUVCQXF/tfSa6urpX+w8IRKjJnZ4zpSFVVn2EYio2N1Zo1a7Rx40aFhYU5olyHqIo533zzzfruu+/sfod17dpVQ4cO1c6dOwnsACqlVatW8vb2vuAztqLCw8P1xRdf2LV98cUXF5xIuJxxvvzyS7u2bdu22b2/7rrrtHfvXrVs2fKC1/m/B7y9vXX77bdr/vz52rx5s1JTU/Xdd99VYGZADefkhfCuWKtWrTI8PT2NZcuWGbt37zZGjRplNGjQwMjMzDQMwzAefPBBY+LEibb+X3zxheHm5mbMmTPHyMjIMKZNm2a4u7sb3333nWEYhnHmzBlj4MCBRlBQkLFz507jl19+sb0KCwudMsfSOHrepTHb6vFVMef33nvPcHd3N5YsWWLs27fPWLBggeHq6mp8/vnn1T6/0lTFnPv06WO0b9/e2LRpk/HTTz8ZS5cuNby8vIxXXnml2udXmvLOubCw0Pj666+Nr7/+2mjSpInx9NNPG19//bWxb9++yx7T2apizo899pjh6+trbN682e73WH5+frXPrzRVMec/Y/V4AI703HPPGVdddZWxfPly48cffzRSU1ON//u//7OtHv/111/b+v7222+GJGPTpk2GYVy4evyaNWsMd3d345VXXjF++OEH4+WXXzZcXV1t/Q3j3Orxa9assavhz6vHp6amGi4uLsbs2bONH374wViwYIHRoEEDu2MlJycbbm5uxnPPPWfs2rXL2L17t/Gvf/3L9tSYpUuXGv/3f/9nfPfdd8b+/fuNyZMnG97e3kZOTo4Dv3pAzUBor0ILFiwwQkJCDA8PD6N79+7Gtm3bbNv69OljREdH2/V/++23jdatWxseHh5G+/btjQ8//NC27fwv3tJef/xFagaOnHdpzBbaDaNq5vyPf/zDaNmypeHl5WV06tTJWLt2bVVPo1wcPedffvnFGD58uBEYGGh4eXkZbdq0MV5++WWjpKSkOqZzWcoz57L+m+3Tp89lj2kGjp5zWb/Hli5dWn2TuoSq+D7/EaEdgCMVFxcbL7zwgtGsWTPD3d3dCAkJMf7+979XKLQbxuU98u1Sod0wzv0dExQUZHh7exu33357qY98S05ONiIiIgxvb2+jfv36Rvfu3Y0lS5YYhnHufyD06NHDqF+/vlGnTh3j+uuvNz799NPKfKmAGstiGIZRxSfzAQAAAABABXBPOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACY1P8DkGqVhNLwAMcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chlorides = data[['chlorides']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(chlorides, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(chlorides, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Chlorides\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Free Sulfur Dioxide\n",
    "Pada histogram, distribusi terlihat membentuk puncak namun tidak simetris. Pada boxplot terlihat terdapat outlier <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat bukan normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freeSulfurDioxide = data[['free sulfur dioxide']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(freeSulfurDioxide, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(freeSulfurDioxide, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Free Sulfur Dioxide\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Sulfur Dioxide\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "totalSulfurDioxide = data[['total sulfur dioxide']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(totalSulfurDioxide, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(totalSulfurDioxide, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Total Sulfur Dioxide\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Density\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "density = data[['density']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(density, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(density, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Density\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pH\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pH = data[['pH']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(pH, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(pH, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of pH\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sulphates\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sulphates = data[['sulphates']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(sulphates, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(sulphates, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Sulphates\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alcohol\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alcohol = data[['alcohol']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(alcohol, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(alcohol, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Alcohol\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quality\n",
    "Pada histogram, distribusi terlihat membentuk puncak simetris. <br>\n",
    "Pada boxplot juga terlihat garis Q2 berada di tengah IQR. <br>\n",
    "Sehingga distribusi bersifat normal secara grafis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quality = data[['quality']]\n",
    "\n",
    "# Create figure\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(quality, ax=axs[0])\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(quality, ax=axs[1], orient='v', width=0.2)\n",
    "\n",
    "plt.suptitle(\"Distribution of Quality\")\n",
    "plt.show"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Soal 3**\n",
    "Menentukan setiap kolom numerik berdistribusi normal atau tidak. <br>\n",
    "Cara menentukan suatu distribusi normal atau tidak dapat menggunakan 2 cara. Cara pertama yaitu cara graphical yaitu membandingkan sampel data dengan garis normal pada histogram. Cara kedua yaitu cara analytical yaitu mencari p-value menggunakan normality test seperti Kolmogorov–Smirnov, Shapiro–Wilk, dan D’Agostino–Pearson tests. Apabila p-value > alpha maka suatu distribusi bersifat normal. Alpha yang sering digunakan pada normality test yaitu 0.05.  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fixed Acidity\n",
    "Distribusi Fixed Acidity bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Fixed Acidity juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 635.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(fixedAcidity, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Fixed Acidity\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.9308584274486692\n",
      "Skewness pada distribusi = 0.006316590636063725\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['fixed acidity'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['fixed acidity'].mean() - data['fixed acidity'].median()))/data['fixed acidity'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Volatile Acidity\n",
    "Distribusi Volatile Acidity tidak bersifat normal secara graphical. Distribusi pada histogram membentuk puncak namun tidak simetris. <br>\n",
    "Distribusi Volatile Acidity juga tidak bersifat normal secara analytical dengan p-value < 0.05. Distribusi ini menggambarkan skewness negatif."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 652x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(volatileAcidity, kde=True)\n",
    "plt.suptitle(\"Distribution of Volatile Acidity\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bukan Distribusi Normal dengan p-value = 0.022581461594113835\n",
      "Skewness pada distribusi = -0.1255578164407037\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['volatile acidity'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['volatile acidity'].mean() - data['volatile acidity'].median()))/data['volatile acidity'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Citric Acid\n",
    "Distribusi Citric Acid bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Citric Acid juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nTxb9/PxEAI6/xwstydLcdURRFNevXy9edtllopeXl+jv7y8OGzZM/OSTT1qs+7rrrhN1Op1YVVXV4jEzZswQ1Wq1aDAYRFEUxdLSUnH27NlidHS0qNFoxJiYGHH69OmO55tbSkMURfHLL78UU1NTRa1WK/bu3Vtcu3Ztsz8LRNR23FsT9unzXISWiIiI5EDSMWfbtm3Dddddh6ioKAiCgHXr1jmeq6urw7x589CvXz/4+PggKioKd999N/Lz8xtdo6ysDFOnToW/vz8CAwNxzz33sFmdiIiIXJak4ayqqgoDBgzAsmXLmjxnNpuxf/9+LFy4EPv378fatWtx7NixJmMppk6diiNHjmDjxo349ttvsW3bNtx///1ddQtERERETiWbbk1BEPDVV19hypQpLR6zZ88eDBs2DKdOnUJcXBzS09PRu3dv7NmzB0OGDAEAbNiwAddccw1yc3MRFRXVqtdmtyYRERHJhUstpVFRUQFBEBxb4ezYsQOBgYGOYAbYt4JRKBTYtWtXi9exWCwwGo2NvoiIiIjkwGXCWU1NDebNm4c77rjD0bpVWFiIsLCwRsepVCoEBwejsLCwxWstXrwYAQEBjq/Y2NhOrZ2IiIiotVwinNXV1eHWW2+FKIp49913O3y9+fPno6KiwvF1/oKbRERERFKR/SK0DcHs1KlT2Lx5c6MxYRERESguLm50fH19PcrKyhAREdHiNbVaLbRabafVTERERNResm45awhmmZmZ+PnnnxESEtLo+REjRqC8vBz79u1zPLZ582bYbDYMHz68q8slIiIi6jBJW85MJhNOnDjh+D47OxtpaWkIDg5GZGQkbrnlFuzfvx/ffvstrFarYxxZcHAwNBoNUlNTcfXVV+O+++7D8uXLUVdXh9mzZ+P2229v9UxNIiIiIjmRdCmNLVu2YOzYsU0enz59Op555pkW9xT85ZdfMGbMGAD2RWhnz56Nb775BgqFAjfffDPeeust+Pr6troOLqVBREREciGbdc6kxHBG5Fx6vR4Gg6Hd54eGhiIuLs6JFRERuQ7ZTwggItei1+uRkpqKarO53dfw8vZGRno6AxoReSSGMyJyKoPBgGqzGVPnvYrwuKQ2n1+kz8Kqlx+DwWBgOCMij8RwRkSdIjwuCTHJfaQug4jI5ch6KQ0iIiIiT8NwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjDGdEREREMsJwRkRERCQjKqkLICL50ev1MBgM7To3PT3dydUQEXkWhjMiakSv1yMlNRXVZnOHrmMymRx/ttpEFBprABHQqRUI8tFAIQgdLZWIyC0xnBFRIwaDAdVmM6bOexXhcUltPj9991b8sPJN1NTUwFxbj99OGJBVXIVaq81xjL9Ohb7RARgQEwiNiqMriIjOxXBGRM0Kj0tCTHKfNp9XpM8CAJRatfh4px7VdVYAgJdaCa1agSpLPYw19dieVYoj+UZc3ScCEQE6p9ZOROTKGM6IyOm8kobhsCUYgBUhPhqM7RWGqEAdBEFAvdWG40Um7DhZiorqOny27zQmpIYjNdJf6rKJiGSB4YyInMoIHUJveByAgF4RfhifEgaV8s+uS5VSgd5R/ujezQebM4qRWWzCT0eLYLWJ6BsdIF3hREQywcEeROQ0NXVWpCMWCrUOwYoaTEgNbxTMzqVTKzGpbwT6nw1kmzKKkVlU2ZXlEhHJEsMZETnNtswS1EGFutLT6K09A6XiwjMyBUHAmF7d0D/GHtB+PFqEMgtncRKRZ2M4IyKnyDFUIb2gEoAIw/dLoBTEVp0nCAJG9+yGhBBvWG0idpSooPAO7NRaiYjkjOGMiDrMahOx5XgJACAKZajNP9am8xWCgEl9IxHio0GNTUDo5IdhE1sX7oiI3A3DGRF1WHqBERXVdfBSKxGPknZdQ6NSYFLfCCgFEV7dB2P9sSonV0lE5BoYzoioQ+ptNuzKLgMADE0IghLtb/EK8dWif5B9XbTVhytxnBMEiMgDMZwRUYcczjPCZKmHr1aFfk5YCiPRxwZz5i7U24DHPj+I+nN2FiAi8gQMZ0TUbjZRxAH9GQD2VrOWls1oC0EAyn56B95qAQdzK/Df37I7fE0iIlfCcEZE7ZZtqIKxph46lQK9nbjCv9VUipkD7dd74+fjyD3TsU3YiYhcCcMZEbVbmr4cANA3OsAprWbnujLBC8MTg1FTZ8Oz3xx16rWJiOSM4YyI2qWk0oLc8moIAhyLyDqTIAh4bkpfqBQCNh4twuaMIqe/BhGRHDGcEVG7HMqrAAD06OYLP526U16jZ7gf7hmVCAB4/tt01HFyABF5AG58TkRtVm+14djZZS46e7Py2Vf2wBf7cnHSUIU1u/W4a0TCRc/R6/UwGAztfs3Q0FDExcW1+3wioo5gOCOiNssqqUJtvQ1+OhVig7w69bX8dGrMGZ+MhV8fwZKfMzFlUPQFW+r0ej1SUlNRbW7/JAIvb29kpKczoBGRJBjOiKjNjhYYAQCpEf4QhM7fqPz2YXH44PccnDRU4b1tJ/HIVb1aPNZgMKDabMbUea8iPC6pza9VpM/Cqpcfg8FgYDgjIkkwnBFRm1TW1EFfZm+V6h3lvOUzLkStVODxq1Pwfx/vw/u/nsTU4fGICNBd8JzwuCTEJPfpkvqIiJxJ0gkB27Ztw3XXXYeoqCgIgoB169Y1el4URTz11FOIjIyEl5cXxo8fj8zMzEbHlJWVYerUqfD390dgYCDuuecemEymLrwLIs/SMNYsOtALAV6dMxGgORP7hGNIfBBq6mx4fWPbNlYnInIlkoazqqoqDBgwAMuWLWv2+VdeeQVvvfUWli9fjl27dsHHxwcTJ05ETU2N45ipU6fiyJEj2LhxI7799lts27YN999/f1fdApHHySyy/+enV7hfl76uIAiYf00qAODzfbk4Vsh9N4nIPUkaziZNmoTnn38eN954Y5PnRFHEkiVL8OSTT+KGG25A//798eGHHyI/P9/Rwpaeno4NGzbgP//5D4YPH45Ro0Zh6dKlWLNmDfLz87v4bojcX7m5FsWVFggC0CPMt8tff3B8ECb1jYAoAm9tzrz4CURELki265xlZ2ejsLAQ48ePdzwWEBCA4cOHY8eOHQCAHTt2IDAwEEOGDHEcM378eCgUCuzatavFa1ssFhiNxkZfRHRxx8+2msUGecNLo5Skhn+MTwYAfH+oACeKOYSBiNyPbMNZYWEhACA8PLzR4+Hh4Y7nCgsLERYW1uh5lUqF4OBgxzHNWbx4MQICAhxfsbGxTq6eyD0dL7Z3JfYM7/pWswYpEf64qnc4RBF455cTktVBRNRZZBvOOtP8+fNRUVHh+Dp9+rTUJRHJXllVLUpNtVAIQFI36cIZYF+YFgC+PpiPU6VVktZCRORssg1nERERAICiosb76RUVFTmei4iIQHFxcaPn6+vrUVZW5jimOVqtFv7+/o2+iOjCskrOdmkGe0OnlqZLs0H/mECM7tkNVpuI5VuzJK2FiMjZZBvOEhMTERERgU2bNjkeMxqN2LVrF0aMGAEAGDFiBMrLy7Fv3z7HMZs3b4bNZsPw4cO7vGYid9YQzqRuNWvw97OtZ1/sy0V+ebXE1RAROY+k4cxkMiEtLQ1paWkA7JMA0tLSoNfrIQgC5syZg+effx7r16/HoUOHcPfddyMqKgpTpkwBAKSmpuLqq6/Gfffdh927d+P333/H7NmzcfvttyMqKkq6GyNyM5U1dSgyWgAA3UN9JK7GbkhCMC7tHow6q4j3tp2UuhwiIqeRNJzt3bsXgwYNwqBBgwAAc+fOxaBBg/DUU08BAB5//HH8/e9/x/3334+hQ4fCZDJhw4YN0On+XBl81apVSElJwbhx43DNNddg1KhReO+99yS5HyJ3ddJgH9cVGaCDj1Y+G4s8dKV95uYnu/UoqbRIXA0RkXNI+lt2zJgxEEWxxecFQcCiRYuwaNGiFo8JDg7G6tWrO6M8IjpLbl2aDUYkhWBAbCAOni7Hql2nMGd8T6lLIiLqMNmOOSMiebDUWZF3xj6mK6mbPLo0GwiCgHtGJQIAPt55CjV1VokrIiLqOIYzIrogfZkZNhEI9tYg0FsjdTlNTOobgcgAHQymWnxzkDuDEJHrYzgjogvKKTUDABJCvSWupHlqpQJ3j0gAAPz3t+wLDpUgInIFDGdE1CJRFJFzdpHX+BB5dWme685hcdCpFcgorESGoU7qcoiIOoThjIhaVGKywFxrhVopICpQd/ETJBLgrcb1A+zL5/yYxR0DiMi1MZwRUYsaujRjg7yhUsj718W0S+MBANtza6Dw4q4fROS65P3blogkders+mYJMu7SbNA/JhD9YwJQbwN8+02QuhwionZjOCOiZtXagIKKGgBAvEwnA5xv2nB765nvwIngvAAiclUMZ0TUrOJqBUQAwT4a+OvUUpfTKtcOiISXSoA6KAoGiyB1OURE7cJwRkTNKqyxh5uEENdoNQMAb40Kl8XaJy6cquKvNyJyTfztRUTNEFBYbf/14Arjzc51ZaI9TOaaFaitt0lcDRFR2zGcEVETmvDusNiEs0toeEldTpukhKhRV5oLqyggs7hS6nKIiNqM4YyImtB1HwwAiAv2hlLhWmO3BEGA6fAmAEB6AcMZEbkehjMiasKr+xAA8t4V4EKqjvwCAMgrr0ZlDXcMICLXwnBGRI1UWmzQRvUC4FqTAc5lrTQgVGsfb3a8yCRxNUREbcNwRkSNHCyyQFAo4a+2wc9FltBoTqy3PZwdK2LXJhG5FoYzImpkf4EFABChc+1VXKO9bVAIQEmlBWVVtVKXQ0TUagxnRORgs4k4UGgPZ+Ferr0MhVZpn9AAsPWMiFwLwxkRORzJN6LCYoPNYkao1rVbzgCgV7gfAOBEMcedEZHrYDgjIodfjhUDAGpy0uBiK2g0KzHUBwoBKKuqZdcmEbkMhjMicthyNpxVZ++TuBLn0KqViD3btcnWMyJyFSqpCyAieThTVYu00+UAgOqTewE8IGk96enpTjmvR5gvTpWacaLYhGGJwc4ojYioUzGcEREAYFtmCWwiEB+gwqnKUsnqMJaVAACmTZvWoeuYTPaWsqRQX2wWilFisqDcXItAb02HayQi6kwMZ0QEANh6zB6KBkVosU3COqpNRgDA5AcWoFf/wW0+P333Vvyw8k3U1NQAALw0SsQEeuH0mWpklVRhcDzDGRHJG8MZEcFmE7H1uD2cXRKplbgau5CoeMQk92nzeUX6rCaPJXXzxekz1ThpMGFwfJAzyiMi6jScEEBEOJRXgdKqWvhqVUgJdb+WpcRQ+x6hBeU1qK6zSlwNEdGFMZwREbac7dIc1SMUKndYQ+M8/l5qhPhqIAI4ZaiSuhwiogtiOCMibDluX0JjTK9uElfSebqfbT07yXBGRDLHcEbk4crOWUJjtBuHs4auzVOlZlhtrr/7ARG5L4YzIg/3a2YJRBFIifBDZICX1OV0mgh/HbzUStRabcgrr5a6HCKiFjGcEXm4hvFmY3qFSVxJ5xIEAQmh9t0CTpWya5OI5IvhjMiDnbuEhjuPN2sQH3y2a7PMLHElREQtYzgj8mB/5FWgrKoWflqVR6z/FRdibzkrNdXCVFMvcTVERM1jOCPyYJsz7LM0RyWHQq10/18HXmolIvx1AIBTZezaJCJ5cv/fxkTUos0ZRQCAK1Pce7zZueJDGsadsWuTiOSJ4YzIQxVW1OBwnhGCAIz1wHCmLzPDxiU1iEiGGM6IPFRDl+bA2ECE+spjP82uEO6vg1algKXehqLKGqnLISJqguGMyEM1dGmO86BWMwBQCAJiguzruZ0+w/XOiEh+GM6IPFBNnRW/nTAAAMalhktcTdeLDbJ3beZySQ0ikiGGMyIPtD3LgJo6G6ICdEiJ8JO6nC7X0HKWX1GDeptN4mqIiBpjOCPyQD+n28ebjUsNhyAIElfT9YJ9NPDWKGG1iSis4LgzIpIXhjMiDyOKIjafDWdXpnrWeLMGwjnjznI57oyIZIbhjMjDHMk3otBYAy+1EiO6h0hdjmQaxp2dPsNxZ0QkLwxnRB7m3F0BdGqlxNVIp6HlrLCiBnVWjjsjIvlgOCPyMJvSPXMJjfMFeKnhq1XBJgL55ezaJCL5YDgj8iDFxhoczK0A4FlbNjVHEATEctwZEckQwxmRB/nxqL3VbEBsIMLObgDuyWKCz653xnBGRDLCcEbkQX44VAAAuKZvhMSVyEPDuLOiyhpY6q0SV0NEZMdwRuQhSk0W7DxZCgCY1DdS4mrkwV+nRoCXGqII5JdzvTMikgeGMyIPsfFoEWwi0DfaH3Eh3lKXIxt/7rPJJTWISB4Yzog8xPeHCwGw1ex8f+6zyXFnRCQPDGdEHqDcXIvtZzc6n8TxZo00tJyVmCwcd0ZEssBwRuQBNh4tQr1NREqEH7p385W6HFnx0aoQ4KUGAO6zSUSywHBG5AE2sEvzgiID7MuKcFIAEckBwxmRmzPW1OHXTHuX5jX92KXZnKhAe9dmfgXHnRGR9BjOiNzc5vRi1FptSOrmg+RwP6nLkaWosy1nhRU1sIkSF0NEHo/hjMjNfd+w8Gw/dmm2JNhHA61KgXqbiPJaQepyiMjDMZwRubGK6jpsOV4CgOPNLkQQBMe4s1ILwxkRSYvhjMiNfX+oALX1NvQK90NqJLs0L6Rh3Fmphb8WiUha/C1E5MbW7s8FANx0STQEgS1CFxIV0BDO+PdERNJSSV0AEXUOfakZe3LOQBCAGwZGS12O7IX7a6EUBNTYAFUgZ7USkXRk3XJmtVqxcOFCJCYmwsvLC0lJSXjuuecgin9OpxJFEU899RQiIyPh5eWF8ePHIzMzU8KqieThqwN5AIBRPUIRcXY8FbVMpVQgzF8LANBG95a4GiLyZLIOZy+//DLeffddvP3220hPT8fLL7+MV155BUuXLnUc88orr+Ctt97C8uXLsWvXLvj4+GDixImoqeFikuS5RFHE2gP2Ls0bB7HVrLUauja1MQxnRCQdWXdrbt++HTfccAMmT54MAEhISMAnn3yC3bt3A7B/AC1ZsgRPPvkkbrjhBgDAhx9+iPDwcKxbtw633367ZLUTSWm/vhynSs3w1igxsQ+76ForMlAH6AFtTKrUpRCRB5N1y9lll12GTZs24fjx4wCAgwcP4rfffsOkSZMAANnZ2SgsLMT48eMd5wQEBGD48OHYsWNHi9e1WCwwGo2NvojcScNEgKv7RMBHK+v/g8lKw3IamtB4VFpsEldDRJ5K1r+1n3jiCRiNRqSkpECpVMJqteKFF17A1KlTAQCFhfb9AsPDwxudFx4e7niuOYsXL8azzz7beYUTSchSb8W3f9gXnr3pkhiJq3Et3hoVfFUiTPUCjpXWYrTUBRGRR5J1y9lnn32GVatWYfXq1di/fz9WrlyJ1157DStXruzQdefPn4+KigrH1+nTp51UMZH0fskoRkV1HcL9tRiRFCJ1OS4nVGtvMUs31EpcCRF5Klm3nD322GN44oknHGPH+vXrh1OnTmHx4sWYPn06IiLsY2mKiooQGfnn6udFRUUYOHBgi9fVarXQarWdWjuRVD7fa+/SnDIwGkoF1+xqqxCtiJwq4HhpndSlEJGHknXLmdlshkLRuESlUgmbzf4/28TERERERGDTpk2O541GI3bt2oURI0Z0aa1EcpBXXo1fjhUDAG4dGitxNa4p+GzL2YmyOtRbOe6MiLqerFvOrrvuOrzwwguIi4tDnz59cODAAbz++uv461//CsC+H96cOXPw/PPPIzk5GYmJiVi4cCGioqIwZcoUaYsnksCnu/WwicCI7iFI6uYrdTkuyU8F2CxVsGh9cKyoEn2iAqQuiYg8jKzD2dKlS7Fw4UI8+OCDKC4uRlRUFB544AE89dRTjmMef/xxVFVV4f7770d5eTlGjRqFDRs2QKfjopvkWeqsNqzZYx8/OfXSOImrcV2CAFjyj8Er8RIc0JcznBFRl5N1OPPz88OSJUuwZMmSFo8RBAGLFi3CokWLuq4wok6k1+thMBjafN7O3BoUV1oQoFUg1Y+D2Tvi3HA27dJ4qcshIg8j63BG5Gn0ej1SUlNRbTa3+dywWxfBK/ES6Ld8in6vfY6M9HTExbEFrT0s+ccAAAdOn5G4EiLyRAxnRDJiMBhQbTZj6rxXER6X1OrzTPXAj/kaACLGjRiEL7ethMFgYDhrp9oC+8LXJ0uqUGGuQ4C3WuKKiMiTMJwRyVB4XBJikvu0+vjfThgAnEF8iA/ifRjIOspWbUSkrxIFJivScssxumc3qUsiIg8i66U0iOjirDYRR/PtW5D1i+bgdWfpGaIBABzQs2uTiLoWwxmRiztRbEJ1nRU+WiUSQ3ykLsdt9Ay2d2Ue0JdLWwgReRyGMyIXl3a6HADQLyoACu4I4DQNLWdpp8ths4kSV0NEnoThjMiFFVRUo9BYA6UgoF8MuzSdKT5QBa1KgYrqOmSXVkldDhF5EIYzIhfW0GrWM8IX3hrO73EmlUJwjOFj1yYRdSWGMyIXVVlTh8xiEwBgUGyQxNW4p0FxgQA4KYCIuhbDGZGL+iO3AqIIxAR6oZufVupy3NKgOHvobWihJCLqCgxnRC6ozmrD4bwKAMDAs6075HwNLWcZhZUw19ZLWwwReQyGMyIXlFFYiZp6G/x1KiSGcvmMzhIZ4IUIfx2sNhGHciukLoeIPATDGZGLEUXR0c02IDYQCoHLZ3SmgbGBAIAD7Nokoi7CcEbkYvRlZpRV1UKtFNAnyl/qctxeQ7fxQYYzIuoiDGdELqah1ax3pD+0KqW0xXiAhpYzTgogoq7CcEbkQs6Ya5FTagZg79KkztcvOgAKASioqEGRsUbqcojIAzCcEbmQhq61xFAfBHlrpC3GQ/hoVegZ7geArWdE1DUYzohchKXOiqMFRgB/drVR12DXJhF1JYYzIhdxJN+IOquIEB8NYoO8pC7HozR0IadxGyci6gIMZ0QuwGYTkZZbDsDeiiNw+Ywu1dBy9kduOaw2UdpiiMjtMZwRuYCThipU1tRDp1YgJcJP6nI8Ts9wP3hrlKiqtSKrxCR1OUTk5lRSF0DkbvR6PQwGQ7vOTU9Pb/bxhrFO/aIDoFK27v9ULV2rs85zZ0qFgH7RAdiVXYY0fbljggARUWdgOCNyIr1ej5TUVFSbzR26jsn0Z+tMSaUFeeXVEAR7OLsYY1kJAGDatGlOq4HsXZu7sstw4HQ5bh0aK3U5ROTGGM6InMhgMKDabMbUea8iPC6pzeen796KH1a+iZqaP9fT+uPsWLMe3Xzhp1Nf9BrVJvuMzskPLECv/oOdUgP9Oe6MOwUQUWdjOCPqBOFxSYhJ7tPm84r0WY2+r6mzIqOwEgAwICawTdcKiYp3Sg1k17CN07GiSlTXWuGl4e4MRNQ5OCGASMaOFhhRbxMR4qtBVKBO6nI8WoS/DmF+WlhtIg7lVUhdDhG5MYYzIpkSRRF/5NpDwIAYLp8hNUEQ2LVJRF2C4YxIpk6VmlFRXQeNistnyEVD1yZ3CiCizsRwRiRTB89OBOgT6Q91K5fPoM7FbZyIqCvwNz6RDFXblMgptS/H0T/m4stnUNfoFx0AQQDyyqtRXMnZrETUORjOiGQov94HABAf4o1Ab43E1VADP50ayWG+AICDpzkpgIg6B8MZkdwoVSiqt29s3r8Vi85S1/qza/OMtIUQkdtiOCOSGe8ew1EHJXy0SiSE+EhdDp1nAMedEVEnYzgjkhnfARMBAL0j/aFQcPkMuWloOfvjdAVsNlHaYojILTGcEclIDdTwSrwEANAnil2actQr3A9eaiUqLfU4aeD+o0TkfAxnRDJShEAAQKDCggCvi++jSV1PpVQ4NqA/oC+XthgicksMZ0QyYbOJKIL9Qz9SVSVxNXQhA2Lt71PDWnRERM7EcEYkEzmlVaiFGlZzBUKVXENLzgbGBgHgpAAi6hwMZ0QycSTfCACoOrwZnAcgbw3bOGUUVKKmziptMUTkdhjOiGTAZKlHdqm9K7Pyjx8lroYuJipAh1BfLeptIo7kczFaInKudoWz7t27o7S0tMnj5eXl6N69e4eLIvI0xworIYqAH8yoL82Vuhy6CEEQHEtqcFIAETlbu8JZTk4OrNamTfkWiwV5eXkdLorIk4iiiPQCe5dmONgK4yoGne3a5LgzInI2VVsOXr9+vePPP/74IwIC/lyHyWq1YtOmTUhISHBacUSewGCqRWlVLZQKAaE2o9TlUCs1tJxxxiYROVubwtmUKVMA2Jv0p0+f3ug5tVqNhIQE/Otf/3JacUSeIL3QHsgSQ32gKrZJXA21Vr+YAAgCcLqsGqUmC0J8tVKXRERuok3dmjabDTabDXFxcSguLnZ8b7PZYLFYcOzYMVx77bWdVSuR27HZRBwrrAQApEb4SVwNtYW/To2kbr4A2LVJRM7VrjFn2dnZCA0NdXYtRB5Hf8YMc60VXmol4rnJucsZyE3QiagTtKlb81ybNm3Cpk2bHC1o5/rf//7X4cKIPEFGgb3VrGe4L5Rc3MzlDIgNxBf7chnOiMip2hXOnn32WSxatAhDhgxBZGQkBIEfKkRtVVtvQ1aJfePslEh/iauh9hjUMCngdDlsNhEKBmwicoJ2hbPly5djxYoVuOuuu5xdD5HHOFFsQr1NRJC3GuF+HEzuinpF+EGrUsBYY19EuGEMGhFRR7RrzFltbS0uu+wyZ9dC5FEaZmmmRPiz9dlFqZUK9Is+uwk6uzaJyEnaFc7uvfderF692tm1EHkMk6UeuWeqAQApnKXp0gZwUgAROVm7ujVramrw3nvv4eeff0b//v2hVqsbPf/66687pTgid5VZZJ8IEBmgg7+X+iJHk5xxxiYROVu7wtkff/yBgQMHAgAOHz7c6Dl2zxBd3PEi+0SAnuFsNXN1DeEsvcCImjordGqltAURkctrVzj75ZdfnF0HkccwVteh0FgDAEgO4wByVxcT5IVQXw0MplocLTDikrggqUsiIhfXrjFnRNR+mcX2VrOYQC/4aNu91CDJhCAIGBATCABI05dLWgsRuYd2fTKMHTv2gt2XmzdvbndBRO7ueFHDwrPs0nQXA2MDsSmjmJugE5FTtCucNYw3a1BXV4e0tDQcPny4yYboRPSncnMtiistEASgB7s03cbAuEAAnBRARM7RrnD2xhtvNPv4M888A5PJ1KGCiNxZw0SA2CBveGk4cFzO0tPTW39wrX0Lu1OlZmzZsRfdo8MQFxfXSZURkbtz6oCXadOmYdiwYXjttdeceVkit/FnlyZbzeTKWFYCwP77rC2i7n0X6pBYTJr2NwiFR5GRns6ARkTt4tRwtmPHDuh0OmdekshtlJosKK2qhUIAt/mRsWqTfeeGyQ8sQK/+g1t93p5SJfRVwJC/zMJvL8+EwWBgOCOidmlXOLvpppsafS+KIgoKCrB3714sXLjQKYURuZvjZ2dpxof4cC0sFxASFY+Y5D6tPr7Uqxz6YyWo1YV0YlVE5AnaFc4CAgIafa9QKNCrVy8sWrQIV111lVMKa5CXl4d58+bhhx9+gNlsRo8ePfDBBx9gyJAhAOzB8Omnn8b777+P8vJyjBw5Eu+++y6Sk5OdWgdRR4iiyC5NNxfhb+81OFPLhbip6+j1ehgMhi55rdDQUKe2Bufk5CAxMREHDhxoMtGwNQRBwFdffYUpU6Y4raa2SEhIwJw5czBnzpwWj2lvje0KZx988EF7TmuzM2fOYOTIkRg7dix++OEHdOvWDZmZmQgK+nORx1deeQVvvfUWVq5cicTERCxcuBATJ07E0aNH2cVKsmEw1aLcXAelQkD3UIYzdxTqq4VSIaDWBqgCI6UuhzyAXq9HSmoqqs3mLnk9L29vp46ljI2NRUFBAUJDQwEAW7ZswdixY3HmzBkEBgZe9PyCgoJGeaCr7dmzBz4+Pp1y7Q6NOdu3b59jRlOfPn0waNAgpxTV4OWXX0ZsbGyjMJiYmOj4syiKWLJkCZ588knccMMNAIAPP/wQ4eHhWLduHW6//Xan1kPUXg2tZgkh3tCouPazO1IqBIT5aVFQUQNtVC+pyyEPYDAYUG02Y+q8VxEel9Spr1Wkz8Kqlx9z6lhKpVKJiIiINp9XW1sLjUbTrnOdqVu3bp127XZ9ShQXF+PKK6/E0KFD8dBDD+Ghhx7C4MGDMW7cOJSUlDituPXr12PIkCH4y1/+grCwMAwaNAjvv/++4/ns7GwUFhZi/PjxjscCAgIwfPhw7Nixo8XrWiwWGI3GRl9EneXcLs1eXHjWrYWf7drUMJxRFwqPS0JMcp9O/Wpv+LPZbHjllVfQo0cPaLVaxMXF4YUXXgBg79YUBAFpaWnIycnB2LFjAQBBQUEQBAEzZswAAIwZMwazZ8/GnDlzEBoaiokTJwKwdxmuW7fO8Vq5ubm44447EBwcDB8fHwwZMgS7du1qsbZ58+ahZ8+e8Pb2Rvfu3bFw4ULU1dU1Ouabb77B0KFDodPpEBoaihtvvNHxXEJCApYsWeL4PjMzE1dccQV0Oh169+6NjRs3tuvvDGhnOPv73/+OyspKHDlyBGVlZSgrK8Phw4dhNBrx0EMPtbuY8508edIxfuzHH3/E3/72Nzz00ENYuXIlAKCwsBAAEB4e3ui88PBwx3PNWbx4MQICAhxfsbGxTquZ6HxFRguMNfVQKwUkhHZOEzjJQ2SAPZxpo1MlroRIHubPn4+XXnoJCxcuxNGjR7F69eomn9mAvYvzyy+/BAAcO3YMBQUFePPNNx3Pr1y5EhqNBr///juWL1/e5HyTyYTRo0cjLy8P69evx8GDB/H444/DZrO1WJufnx9WrFiBo0eP4s0338T777/faB3X7777DjfeeCOuueYaHDhwAJs2bcKwYcOavZbNZsNNN90EjUaDXbt2Yfny5Zg3b16r/57O165uzQ0bNuDnn39Gauqfv4B69+6NZcuWOXVCgM1mw5AhQ/Diiy8CAAYNGoTDhw9j+fLlHdqJYP78+Zg7d67je6PRyIBGneZ4sb3VLDHUB2oluzTdWUM404Qlorqu5Q8FIk9QWVmJN998E2+//bbjMzspKQmjRo1qcqxSqURwcDAAICwsrMmYs+TkZLzyyistvtbq1atRUlKCPXv2OK7To0ePC9b35JNPOv6ckJCARx99FGvWrMHjjz8OAHjhhRdw++2349lnn3UcN2DAgGav9fPPPyMjIwM//vgjoqKiAAAvvvgiJk2adMEaWtKuTwqbzQa1Wt3kcbVafcGU2laRkZHo3bt3o8dSU1Oh1+sBwNHfXFRU1OiYoqKiC/ZFa7Va+Pv7N/oi6gyiKCLz7K4A3EvT/fnp1PBWihAUSpwoq7v4CURuLD09HRaLBePGjevwtQYPvvCag2lpaRg0aJAjmLXGp59+ipEjRyIiIgK+vr548sknHfmi4ZqtrT09PR2xsbGOYAYAI0aMaHUt52tXOLvyyivxj3/8A/n5+Y7H8vLy8PDDDzvlTWgwcuRIHDt2rNFjx48fR3x8PAD75ICIiAhs2rTJ8bzRaMSuXbs69JdC5CwFFTUwWeqhUSoQH+wtdTnUBYK19v+gphtqJa6ESFpeXl5Ou9bFZkW29bV27NiBqVOn4pprrsG3336LAwcOYMGCBait/fPfrTPrb6t2hbO3334bRqMRCQkJSEpKQlJSEhITE2E0GrF06VKnFffwww9j586dePHFF3HixAmsXr0a7733HmbNmgXAPhhwzpw5eP7557F+/XocOnQId999N6KioiRb94ToXA2tZt27+UDFLk2PEKIVAQDHStlyRp4tOTkZXl5ejRpQLkSj0QAArFZrm1+rf//+SEtLQ1lZWauO3759O+Lj47FgwQIMGTIEycnJOHXqVJNrtrb21NRUnD59GgUFBY7Hdu7c2fobOE+7xpzFxsZi//79jj7WhsLOnTXpDEOHDsVXX32F+fPnY9GiRUhMTMSSJUswdepUxzGPP/44qqqqcP/996O8vByjRo3Chg0buMYZSc4misg8O94smQvPeoxQRzirhc0mQqHgorTkmXQ6HebNm4fHH38cGo0GI0eORElJCY4cOYJ77rmnyfHx8fEQBAHffvstrrnmGnh5ecHXt3W/O++44w68+OKLmDJlChYvXozIyEgcOHAAUVFRzfakJScnQ6/XY82aNRg6dCi+++47fPXVV42OefrppzFu3DgkJSXh9ttvR319Pb7//vtmB/qPHz8ePXv2xPTp0/Hqq6/CaDRiwYIFrfybaqpN4Wzz5s2YPXs2du7cCX9/f0yYMAETJkwAAFRUVKBPnz5Yvnw5Lr/88nYXdL5rr70W1157bYvPC4KARYsWYdGiRU57TSJnKCivQVWtFRqVAvHBnKXpKfzVImy11TDDC5nFJvSK4FhD6lxF+izZvsbChQuhUqnw1FNPIT8/H5GRkfi///u/Zo+Njo7Gs88+iyeeeAIzZ87E3XffjRUrVrTqdTQaDX766Sc88sgjuOaaa1BfX++YqNic66+/Hg8//DBmz54Ni8WCyZMnY+HChXjmmWccx4wZMwaff/45nnvuObz00kvw9/fHFVdc0ez1FAoFvvrqK9xzzz0YNmwYEhIS8NZbb+Hqq69uVf3na1M4W7JkCe67775mB9AHBATggQcewOuvv+7UcEbkqhrWNkvq5gMlW088hkIALPnH4JUwEHtPlTGcUacJDQ2Fl7c3Vr38WJe8npe3t2M1/9ZSKBRYsGBBs61ICQkJEEWx0WMLFy5sskf3li1bmr32+efGx8fjiy++aHVtr7zySpMZoOdvxXTTTTc12U+8QU5OTqPve/bsiV9//fWCNbZWm8LZwYMH8fLLL7f4/FVXXYXXXnutXYUQuRNRBDKLOUvTU1ny0uGVMBD7cs5g6vB4qcshNxUXF4eM9HSX3VuTWtamcFZUVNTsEhqOi6lUTt0hgMhVlVgEVNdZoVMpEBvEWZqexpJn39Zun/6MxJWQu4uLi2NgckNtmj4WHR2Nw4cPt/j8H3/8gchIbvhLlGu2/9NKCvNll6YHsuRlQABwqtSMkkqL1OUQkYtpUzi75pprsHDhQtTU1DR5rrq6Gk8//fQFB+8TeQRBgbyz4Sw5jLM0PZFYa0ZcgL1jYt8ptp4RUdu0qVvzySefxNq1a9GzZ0/Mnj0bvXrZN/fNyMjAsmXLYLVaOzR1lMgd6OIHoNYmwEutZJemB+sVosGpinrsO1WGq/u2vGMJEdH52hTOwsPDsX37dvztb3/D/PnzHbMQBEHAxIkTsWzZsmY3NCXyJN4p9n3jeoT5co0rD5YSqsZPJ4G9bDkjojZq8yK08fHx+P7773HmzBmcOHECoigiOTkZQUFBnVEfkUups4rw7nkZAHZperqUUPtq54fzKlBTZ4VOrZS4IiJyFe3aIQAAgoKCMHToUGfWQuTy/ii2QOnlB61CRHSQdPuykfTCfZQI9dXCYLLgUF4Fhia0fkNmIvJs3OyPyIm2n7ZPlon2tkEhsEvTkwmCgKEJ9h6F3dmt2++PiAhgOCNyGku9Fbvy7OEs1tsmcTUkB8MT7a1lO0+WSlwJEbkShjMiJ9lyrATmOhH1laUI0bZvyw5yL8O7hwCwL6dRZ2VgJ6LWYTgjcpKv9ucBAKqObgF7NAkAeoX7IcBLDXOtFUfyjVKXQ0QuguGMyAkqzHXYnFEMAKg6skXaYkg2FArBMRFgF7s2iaiVGM6InOC7QwWotdoQH6BCXUm21OWQjFza/Ww446QAImolhjMiJ1h3wN6leUU8l8+gxoYn2sed7ckug9XGsYhEdHEMZ0QddLrMjN05ZRAE4PI4hjNqrHeUP3y1KlRa6pFewHFnRHRx7V6Elojsvk6zt5qN6B6CUG+uAk926enpjj/3DFJif2E9vvz1D9T29LnouaGhoYiLi+vM8ohIxhjOiDpAFEV8dbZL88ZB0QBKpC2IJGcss/8MTJs2zfGY//CbETRmJpZ99iOe+eqFi17Dy9sbGenpDGhEHorhjKgDDucZkVVSBa1Kgav7RiDzKMOZp6s22bsuJz+wAL36DwYAlFoEbCkCAlMuxbS3115wqZUifRZWvfwYDAYDwxmRh2I4I+qAtQdyAQBX9YmAn04tcTUkJyFR8YhJ7gMAiLSJ+K0kC7U2wCuqB0J9tRJXR0RyxgkBRO1UW2/DNwfzAQA3DoqSuBqSM6VCQGSgDgCQd6Za4mqISO4Yzoja6ef0IhhMtQjz0+KK5G5Sl0MyFxPoDQDIK2c4I6ILYzgjaqdPdusBALcOiYVKyX9KdGHRgfZlVnLPVEMUud4ZEbWMnyhE7aAvNePXTAMEAbhtaKzU5ZALCA/QQqUQUF1nRVlVrdTlEJGMMZwRtcOaPfZWs8uTuyE22FviasgVqBQKRJ1tPdOXmSWuhojkjOGMqI3qrDZ8ttc+S/POYWw1o9aLOxvkGc6I6EIYzojaaFN6EQwmC0J9tRiXGi51OeRCYoPtLWd55dXcZ5OIWsRwRtRGq3efBgD8ZUgM1JwIQG3QzVcLL7USdVYRhcYaqcshIpniJwtRG5wuM+PXTPsuALdzIgC1kSAIiA2yt56dZtcmEbWA4YyoDdbs0UMUgVE9QhEfcvENrInOF8txZ0R0EQxnRK1UU2fFJ2e7NO8czj0PqX0aJgUUGmtgqbdKXA0RyRHDGVErfZ2Wh7KqWkQHeuGq3pwIQO3j76VGgJcaositnIioeQxnRK0giiI++D0HADD9snjuCEAdwiU1iOhC+AlD1Ao7skqRUVgJb40Stw1hlyZ1TMOSGqfL2HJGRE0xnBG1wr+3nQQA3DI4BgHeaomrIVcXG2RvOSsz18JUUy9xNUQkNwxnRBdxJL8CW4+XQCEA947qLnU55AZ0aiXC/bUAgNNn2LVJRI0xnBFdxPKt9laza/tHIS6E+2iSczS0nnHcGRGdj+GM6AL0pWZ890c+AOCB0Ww1I+dpmBRwqtQMUeRWTkT0J4YzogtYujkTNhEY3bMb+kQFSF0OuZGoQC9olApU11lRZLRIXQ4RyQjDGVELTpaYsPZAHgBgzvhkiashd6NUCI5u8mxDlcTVEJGcMJwRteDNTZmw2kRcmRKGQXFBUpdDbijx7BZgOaUMZ0T0J4YzomYcL6rE+oP2sWZzJ/SUuBpyV/FnW86KKy0wWbikBhHZMZwRNWPJz8chisDVfSLQN5pjzahz+GhVjiU1cti1SURnMZwRnedIfgW+P1QIQQAeZqsZdTJ2bRLR+RjOiM7zxsbjAIDr+kehV4SfxNWQu0sItYczfZkZ9TabxNUQkRwwnBGdY09OGX5OL4ZCAP7BGZrUBcL8tPDRKFFnFZF3hnttEhHDGZGDzSbi+W+PAgBuGxqLpG6+EldEnkAQBEfrWU4pdwsgIoYzIof1B/NxMLcCPhol5k7oJXU55EESzo47yzZUgZsFEBHDGRGA6lorXt6QAQB4cGwPdPPTSlwReZK4YG8oBKCiug4mrqhB5PEYzogA/OfXkyioqEF0oBfuGZUodTnkYTQqBaKDvAAABdX8tUzk6fhbgDxesbEG727NAgDMm5QCnVopcUXkiZJC7WMc8838tUzk6fhbgDzev346DnOtFYPiAnFd/0ipyyEPlRRmD2eltQoofUMkroaIpKSSugAiZ9Pr9TAYDK069uSZOny2137srT2UOHDgAEJDQxEXF9eZJRI14atVITJAh4KKGnj3HCF1OUQkIYYzcit6vR4pqamoNrduSYLwOxZDF9cPVUe34M6XXwMAeHl7IyM9nQGNulyPMF97OOs1UupSiEhCDGfkVgwGA6rNZkyd9yrC45IueGyuWcAugxpKQcQt4y+D99VrUaTPwqqXH4PBYGA4oy7Xo5svfs00QBvbB+U1VqnLISKJMJyRWwqPS0JMcp8Wn6+32rBx5ykA9RiSEIKe3TnGh6Tn76VGkMaGM7UK7MqrwZVSF0REkuCEAPJIB06Xw1hTD1+tCoPjg6Quh8gh2tu+v+aO3BqJKyEiqTCckcepstRjT04ZAGBkjxColfxnQPIR7WUPZ4eLa3GmqlbiaohICvxUIo/ze5YBdVYREf469Ar3k7ocokZ81UBt0UnYRGDj0SKpyyEiCTCckUcpMtYgvaASADC6ZzcIgiBxRURNmY/9DgD4/nCBxJUQkRRcKpy99NJLEAQBc+bMcTxWU1ODWbNmISQkBL6+vrj55ptRVMT/bVJToihi6/ESAEBKhB8iAnQSV0TUvKrj9nD2+wkDKqrrJK6GiLqay4SzPXv24N///jf69+/f6PGHH34Y33zzDT7//HNs3boV+fn5uOmmmySqkuTsRLEJBRU1UCkEjEwKlbocohbVl+Yi1l+FOquIDWw9I/I4LhHOTCYTpk6divfffx9BQX/OrKuoqMB///tfvP7667jyyisxePBgfPDBB9i+fTt27twpYcUkNzabiO0nSwEAg+OD4KvjKjIkb1fE2zdCX7s/T+JKiKiruUQ4mzVrFiZPnozx48c3enzfvn2oq6tr9HhKSgri4uKwY8eOFq9nsVhgNBobfZF7O1poRLm5Dl5qJS6J49IZJH9XxNnD2a7sMuSead2OF0TkHmQfztasWYP9+/dj8eLFTZ4rLCyERqNBYGBgo8fDw8NRWFjY4jUXL16MgIAAx1dsbKyzyyYZqbfasOukfemMIQlB0Khk/2NPhG4+SlzaPRgA8HVavsTVEFFXkvWn1OnTp/GPf/wDq1atgk7nvMHb8+fPR0VFhePr9OnTTrs2yc+hvAqYLPYFZ/tHB0hdDlGr3TQoBgCwdn8uRFGUuBoi6iqyDmf79u1DcXExLrnkEqhUKqhUKmzduhVvvfUWVCoVwsPDUVtbi/Ly8kbnFRUVISIiosXrarVa+Pv7N/oi91Rbb8OenDMAgOGJwVBxwVlyIZP6RUCrUiCrpAqH8zj8gshTyPqTaty4cTh06BDS0tIcX0OGDMHUqVMdf1ar1di0aZPjnGPHjkGv12PEiBESVk5yceD0GVTXWRHopUbvSIZwci1+OjUm9A4HAKw9kCtxNUTUVWQ9Zc3Pzw99+/Zt9JiPjw9CQkIcj99zzz2YO3cugoOD4e/vj7///e8YMWIELr30UilKJhmpqbNi/6lyAMCIpBAoFFxwllzPTZdE49s/CvDNwXwsuCaVrb9EHkDW4aw13njjDSgUCtx8882wWCyYOHEi3nnnHanLIhn4I7cCtVYbQn01SA7zlbocona5PLkbQnw0MJhq8WumAWNTwqQuiYg6mcuFsy1btjT6XqfTYdmyZVi2bJk0BZEs1duAtNPlAIAh8cFt3qYpPT29Xa/b3vOIWqJWKnDdgCis2J6DtQfyGM6IPIDLhTOi1sipUqC6zooAL3WbWs2MZfbtnaZNm9ah1zeZTB06n+hcN10SjRXbc/DTkUJUmOsQ4K2WuiQi6kQMZ+R+FEpkGpUAgEviAts01qzaZJ8RN/mBBejVf3CbXzp991b8sPJN1NTUtPlcopb0iw5ASoQfMgorsfZALmaOTJS6JCLqRAxn5HZ8Ui6H2SrAS61s9wzNkKh4xCT3afN5Rfqsdr0e0YUIgoCpw+Ow8OsjWL1LjxmXJbS5q56IXAen/ZBbsYki/C/9CwBgUFwgZ7aR27hhUDS81EpkFpuw99QZqcshok7ETy5yK/sLLNB0i4dKELkbALkVf50a1w2IBACs3qWXuBoi6kwMZ+RWvsqwD8Tv7muDVq2UuBoi57pzeDwA4LtDBSg310pcDRF1FoYzchtH8iuQbqiDaK1HDz+r1OUQOd2AmAD0jvRHbb0NX+7Pk7ocIuokDGfkNj7acQoAYD6+HV6c6kJuSBAE3Dk8DgCwetcpboZO5KYYzsgtVJjrsC7N3pJQuf87iash6jw3DIyCt0aJrJIq7M4uk7ocIuoEDGfkFj7fdxo1dTbEB6hgyT0idTlEncZPp8b1A6IAAB9zYgCRW2I4I5dns4n4eKe9S/PqHt4SV0PU+aZdap8Y8MOhAhRWcMFjInfDcEYu79cTBuSUmuGnVeGKOC+pyyHqdH2jAzAsMRj1NhEf7cyRuhwicjKGM3J5H+3IAQDcPDgGXmr+SJNn+OvZLZxW79Kjupazk4ncCT/JyKWdLjNjU0YxAOCuEfESV0PUdSb0DkdMkBfOnDMZhojcA8MZubRPdushisCoHqFI6uYrdTlEXUapEDDjsgQAwAe/Z3NZDSI3wnBGLqveasMX+3IBAFPPrv1E5EluHRoLH40Sx4tM+P1EqdTlEJGTMJyRy9qWWYLiSguCfTQYlxoudTlEXc5fp8Ytg2MAAP/7PVviaojIWbiOOsmOXq+HwWC46HHvbT8DALgsSo3Df6QBANLT0zuzNCLZmTEyESt3nMLmjGJklZjYvU/kBhjOSFb0ej1SUlNRbTZf8DiFlz9iZq2EoFTjvSdmYJnhVKPnTSZTZ5ZJJBuJoT4YlxKGTRnF+M+v2Vh8Uz+pSyKiDmI4I1kxGAyoNpsxdd6rCI9LavG4TKMCf5SrEKix4eZn33A8nr57K35Y+SZqargwJ3mO+6/ojk0Zxfhyfy4enpCMMD+d1CURUQcwnJEshcclISa5T7PPiaKILbv1AGoxMDEcMTGBjueK9FldUyCRjAxLDMaguEAc0Jfjg99zMO/qFKlLIqIO4IQAcjnFlRaUmmqhVAjoFe4ndTlEkhMEAf832t7S/PHOU6isqZO4IiLqCIYzcjlH840AgKRuPtCplRJXQyQPE1LDkdTNB5U19VjNDdGJXBrDGbmUeqsNx4oqAQC9I/0lroZIPhQKAQ9cYW89++9v2bDUc0snIlfFcEYu5aShCpZ6G3y1KsQGe0tdDpGs3DAoCuH+WhRXWrDuALd0InJVDGfkUjIK7a1mqZF+UAiCxNUQyYtWpcQ9o+wbov9720nYbNzSicgVMZyRy6iuteJUaRUAcCIAUQvuGBYHf50KJ0uqsOFIodTlEFE7MJyRyzheXAmbCIT5aRHiq5W6HCJZ8tOpHRuiL918ghuiE7kgrnNGLuPY2S7NXhFsNSP315GtyAb72aBTAukFRvz72x0YFt32RWlDQ0MRFxfX7hqIqP0YzsglVFTXoaCiBgKAnuzSJDdmLCsBAEybNq1D1wkcPQMBl96CZz/fhcKP5rb5fC9vb2SkpzOgEUmA4YxcQkahfW2zmGAv+Gr5Y0vuq9pk/1mf/MAC9Oo/uF3XSN+9FT9+vgKBw2+ENqon7nztK0R4tb57s0ifhVUvPwaDwcBwRiQBfsqR7Imi6JilmRLBtc3IM4RExbe4hdnFFOmzYDNXIEpdjbx6X5ys9cPgfjEQOMOZyCVwQgDJXnGlBeXmOqgUApK6+UhdDpHLiFWboFQIKKioQe6ZaqnLIaJWYjgj2WtoNese6gOtits1EbWWVrChT5S9tXl3TpnE1RBRazGckazZbCKOn92uqVckJwIQtdWQ+CAoBCD3TDXyy9l6RuQKGM5I1k6fMcNca4VOrUB8MLs0idrKT6d27EPL1jMi18BwRrLW0KXZM8wPSgUHMxO1x5CEYAgCcKrUjEJjjdTlENFFMJyRbNVZbcgqMQEAUtilSdRuAV5qpJxdH3B3NlvPiOSO4YxkK6vEhDqriAAvNSL8277CORH9aWhiMAQA2YYqFLH1jEjWGM5Ithq6NHuF+3F9JqIOCvLWOFqgd54slbgaIroQhjOSpRoroC8zAwBSuJcmkVMMOzv2LKfUjIIKztwkkiuGM5KlXLMCogiE+WkR5KORuhwitxDorUHq2V02dp7k2DMiuWI4I1k6XWX/0WSrGZFzDUsMhkKwt0zncd0zIlliOCPZUQVGoqxWAQFAz3CGMyJnCvD6c90zjj0jkieGM5Idnz5jAACxwd7w0aqkLYbIDQ0923qWe6YauWfMUpdDROdhOCNZEUURPr3HAGCXJlFn8dep0TcqAIB97JkoihJXRETnYjgjWTlRVgd1cDSUgoikbr5Sl0PktoYkBEGpEJBXXo3TZzj2jEhOGM5IVrbp7R8SUV42aFT88STqLH46Nfo5Ws9K2XpGJCP89CPZqLfa8JvevnJ5rI9N4mqI3F9D61lBRY1jXUEikh7DGcnGbycMqLDYYDVXIFzH/8UTdTYfrQr9o+2tZzvYekYkGwxnJBtfp+UDAKrSf4WCuzURdYnB8UFQKQQUGS3IKWXrGZEcMJyRLJhr6/HjkUIAQNXRLdIWQ+RBfLQqDIgJBMCxZ0RywXBGsrDxaBHMtVaE+yhRm58hdTlEHuWS+EColQKKKy3IKqmSuhwij8dwRrKw7kAeAOCKeC+JKyHyPN4aFQbFBgEAtmcZYGPjGZGkGM5IcgaTBdsyDQCAK+IYzoikcEl8IHRqBc6Y63Cqih8NRFLiv0CS3LcH82G1iegfE4Bof27XRCQFrUqJoQnBAID0CiUElUbiiog8F8MZSe7L/fYuzSkDoyWuhMiz9Y8OgJ9OhWqrAL9LrpO6HCKPxXBGksooNOJQXgXUSgFTBjGcEUlJpVTg0u4hAAD/EX+BqZaLQRNJgeGMJPXF3lwAwJUpYQj2YTcKkdRSIvzgr7ZBqfPFugyT1OUQeSSGM5JMndWGdWn2Ls2/DI6VuBoiAgCFIKBPgBUA8G1mFYqMNRJXROR5GM5IMluOlcBgqkWorwaje3WTuhwiOivSS0RN7hHUWoElP2dKXQ6Rx5F1OFu8eDGGDh0KPz8/hIWFYcqUKTh27FijY2pqajBr1iyEhITA19cXN998M4qKiiSqmNrii32nAdgnAqiVsv5RJPIoggCUb1kJAPhs72lklbB7k6gryfoTcevWrZg1axZ27tyJjRs3oq6uDldddRWqqv5cwfrhhx/GN998g88//xxbt25Ffn4+brrpJgmrptYoNVmwKb0YAHDLkBiJqyGi81nyjmJIpBZWm4jXfjx28ROIyGlkvajUhg0bGn2/YsUKhIWFYd++fbjiiitQUVGB//73v1i9ejWuvPJKAMAHH3yA1NRU7Ny5E5deeqkUZVMrfJ2Wj3qbiH7RAUiJ8Je6HCJqxtT+fthfaMEPhwuxO7sMwxKDpS6JyCPIOpydr6KiAgAQHGz/BbFv3z7U1dVh/PjxjmNSUlIQFxeHHTt2tBjOLBYLLBaL43uj0diJVXsevV4Pg8FwwWM++q0EAHBpmIj9+/c7Hk9PT+/U2oio9eID1Lh9WBxW79LjuW+P4utZI6FQCFKXReT2XCac2Ww2zJkzByNHjkTfvn0BAIWFhdBoNAgMDGx0bHh4OAoLC1u81uLFi/Hss892ZrkeS6/XIyU1FdVmc4vHqMO6I2rmWxDr6/DUjGvwZE3T8SwmE8e4EMnB3Ak98U1aPg7lVWDtgTzcMpjDEIg6m8uEs1mzZuHw4cP47bffOnyt+fPnY+7cuY7vjUYjYmO5lIMzGAwGVJvNmDrvVYTHJTV7zP4yJbJNQKy/Arf868NGz6Xv3oofVr6JmhpO3yeSg1BfLWZd2QMv/ZCBVzZkYFLfCPhoXeajg8glucS/sNmzZ+Pbb7/Ftm3bEBPz5//aIiIiUFtbi/Ly8katZ0VFRYiIiGjxelqtFlqttjNL9njhcUmISe7T5PHaehty804CEDGsVxxigr0bPV+kz+qiComotWaOTMDqXXroy8z499YszL2ql9QlEbk1Wc/WFEURs2fPxldffYXNmzcjMTGx0fODBw+GWq3Gpk2bHI8dO3YMer0eI0aM6OpyqRWOFVaizioi0FuNmCAvqcsholbQqpSYPykFAPDvbSeRV14tcUVE7k3W4WzWrFn4+OOPsXr1avj5+aGwsBCFhYWorrb/YggICMA999yDuXPn4pdffsG+ffswc+ZMjBgxgjM1ZUgURRzKt0/q6BcdAEHgwGIiV3F13wgMSwyGpd6GF7/jxB2iziTrcPbuu++ioqICY8aMQWRkpOPr008/dRzzxhtv4Nprr8XNN9+MK664AhEREVi7dq2EVVNLiowWlFRaoFQISI3k8hlErkQQBDxzXR8oBOC7QwXYdrxE6pKI3Jasw5kois1+zZgxw3GMTqfDsmXLUFZWhqqqKqxdu/aC481IOofy7K1myWG+8FIrJa6GiNqqd5Q/7h6RAAB4Zv0RWOqt0hZE5KZkHc7IfVjqrDheVAnA3qVJRK5p7lU9EeqrxUlDFf7za7bU5RC5JYYz6hLphZWot4kI8dEgMkAndTlE1E7+OjUWTLZPDli6ORO5Z1pe05CI2ofhjDqdKIo4nMeJAETuYsrAaAxPDEZNnQ2LvjkqdTlEbofhjDrd6TPVKK2qhVopICXCT+pyiKiDBEHAc1P6QqUQ8NPRImw8WiR1SURuheGMOt1+/RkAQO9If2g5EYDILfQM98O9l3cHADy57hAqquskrojIfTCcUacqNVlwqtQ+JmVgbKC0xRCRU80Zn4zEUB8UGS146QeufUbkLAxn1KnSTpcDAJK6+SDQWyNtMUTkVDq1Ei/d1A8A8Mnu09h+wiBxRUTugeGMOo25th7phfblMwbFBklcDRF1huHdQzDt0jgAwBNrD6G6lmufEXUUwxl1mkN5FbDaRIT5aREVyOUziNzVvKtTEBWgg77MjH/9dEzqcohcHsMZdQqrCBw8bV8+45K4IC6fQeTG/HRqvHCjvXvzv79nY9fJUokrInJtKqkLIPd0ukqB6jorfLUq9AjzlbocImqH9PTWD/IPADAu0Qubsqsx6+M9+PCOXuidnNh5xRG5MYYzcj5BgeNG+5IZA2IDoFSw1YzIlRjL7JuaT5s2rU3nCRovRM54CwZEYuxj72LPW7MRFxfXGSUSuTWGM3I675RRqKwXoFUpuI8mkQuqNhkBAJMfWIBe/Qe36dxSi4AtRSK8UkZj3f7TeIjhjKjNGM7IqWyiiIDLbgMADIoLhFbFRWeJXFVIVDxikvu06ZwYAIXVGcgwKvHvfRX4y9hqRAZ4dU6BRG6KEwLIqXbk1kATGg+1IHLRWSIPlRpghaXgOKrqRMxZk4Z6q03qkohcCsMZOY3NJuKzIyYAQA8/G1vNiDyUQgAM37wGnUrAruwyLPk5U+qSiFwKwxk5zY9HCnHaWA+bpQo9/LkQJZEnqz+TjweH2Mecvv3LCfxyrFjiiohcB8MZOYXNJuLNTfb/HRv3roeGP1lEHm9UnBfuujQeADD30zTkl1dLXBGRa+BHKDnFj0cKkVFYCS+VgMq9X0tdDhHJxJPXpqJfdADOmOswa/V+1NZz/BnRxTCcUYfV1tvw8oYMAMC1PX1gqzFJXBERyYVWpcSyOy+Bn06FA/pyPPftUalLIpI9hjPqsI93nkJOqRmhvlpM6eUjdTlEJDNxId5449aBEATgo52n8PHOU1KXRCRrDGfUIeXmWsdYs7kTesJLzR8pImpqfO9wPHpVLwDAM+uPYEcW998kagk/SalDlm4+gYrqOvQM98WtQ2KkLoeIZOzBMUm4fkAU6m0iHly1D/pSs9QlEckSwxm1W46hCh/uyAEALJjcGyolf5yIqGWCIOCVW/qjf4x9gsC9H+6BsaZO6rKIZIefptRuL2/IQJ1VxBU9u2F0z25Sl0NELkCnVuK9u4YgzE+L40Um/N9H+ziDk+g83FuT2uX3Ewb8cLgQCgFYcE2q1OUQkQylp6e3+Nxjl/ph4S+12J5Vinve+wX/GB4IhSA4ng8NDUUcN00nD8VwRm1WXWvF/LWHAADTLo1Hrwg/iSsiIjkxlpUAAKZNm3bB43QJAxF2yzP4VV+Dbz9fhvItHzie8/L2RkZ6OgMaeSSGM2qzJZuOQ19mRmSADo9N7CV1OUQkM9UmIwBg8gML0Kv/4Asee6oK2FsKBAy/GZdPvAHJ/jYU6bOw6uXHYDAYGM7IIzGcUZsczqvAf37NBgA8d0Nf+OnUEldERHIVEhWPmOQ+FzwmBoA2pwy/Z5Xij3IVukWGIZx5jDwcJwRQq9VbbXhi7R+w2kRM7h+J8b3DpS6JiNzA4PggXBIXCADYlF4MfRU/msiz8V8AtdoHv+fgcJ4R/joVnr6ut9TlEJGbEAQBo3qEol90AABgb6kSXj1HSFwVkXQYzqhVjhdV4rWfjgEAnpzcG2F+OokrIiJ3IggCxvbqhtRIP4gQ0O36x7E3v0bqsogkwXBGF2Wpt+KhTw7AUm/D6J7d8BfuBEBEnUAQBIxPDUeMtxWCUo1Xtp/BhsOFUpdF1OUYzuiiXtlwDBmFlQj20eDVv/SHcM5aREREzqQQBAwNsaLq6FbU24BZq/dj/cF8qcsi6lIMZ3RBGw4X4r+/2WdnvnJzf3ZnElGnUwiA4dt/YWyCF6w2EXPWHMAX+3KlLouoyzCcUYtOlVbhsc8PAgDuHZXI2ZlE1HVEG2YNDcAdw2JhE4FHPz+ID37Plroqoi7BcEbNMlnqcf+H+1Bpqcfg+CDMm5QidUlE5GEUgoAXb+yHGZclAACe/eYoXvohA6IoSlsYUSdjOKMmbDYRD3+ahmNFlejmp8Xbdw6CWskfFSLqeoIg4Onrejt2I1m+NQuPfH4QdVZulk7ui5+41MRLGzKw8WgRNCoF3rtrMCIDvKQuiYg8mCAImDW2B169pT+UCgFr9+fhnpV7UWWpl7o0ok7B7Zuokf/9lo33tp0EYJ8AMCguSOKKiMhTpaenN/o+SQE8MTIQr20vx7bjJbh+yWYsuDwIgTplk3NDQ0O5Lye5LIYzcli7PxfPfXcUAPDYxF6YMiha4oqIyBMZy0oAANOmTWv2eU1kT4Td8jSyEIC7Vh5EyZeLUFd6utExXt7eyEhPZ0Ajl8RwRgDsweyRzw9CFIG7R8TjwTFJUpdERB6q2mQEAEx+YAF69R/c7DGVdcDvJSKqgiIRd987GBZaj0gv+0SBIn0WVr38GAwGA8MZuSSGM8KX+3Lx6Bf2YDZ1eByeua4PF5olIsmFRMUjJrlPi88n1Frx3aEC5JVXY3uJGpf3CMWgsxuoE7kyhjM3o9frYTAYWn38LzlmvL27AiKAiUneuDGuFrm5p/m/TSKSPS+NEjcOisYvx4pxJN+IX08YUFpVi578ZCMXxx9hN6LX65GSmopqs7lVx/uPuBVBV9wNAKjc/x3ee3k53oPIsRpE5DKUCgHjUsIQ4qPBr5kGHC0wokirgsI7QOrSiNqN4cyNGAwGVJvNmDrvVYTHtTxmzCoC+0uV0JvtM5x6+FnR/4YJEKZM4FgNInI5giBgUFwQgnw0+OFQIUotQOSMt5BhqMUlUhdH1A4MZ24oPC6pxXEa5tp6fPtHAQrMNRAEYEzPbugfE9i1BRIRdYKEEB/cNjQW6/bmoNIvBAt/KYXFJxszLkvgOFpyKVyE1oOcKq3C6t16FFTUQKNSYMrAaAYzInIrwT4ajI2oQ1X6NlhF+5ZPsz85ABMXrCUXwnDmAeqtNmw9XoJ1afmoslgR5K3GbUNiERfsLXVpREROp1YAhvWv4J6B/lApBHz3RwFuePs3ZBZVSl0aUaswnLm5wooarNlzGmmnywEA/aMDcMewOAT7aKQtjIiok03u6YNPH7gU4f5aZJVU4fq3f8ea3XpunE6yxzFnbqrKUo/fswxIL7D/T9FLrcSE3uFIDPWRuDIioq6Rnp6O1FRg8ZgALNlVjj+KavHE2kNYtysTfxsSAD9ty+0T3P6JpMRw5mYEjRcyKhQ4viMHdVb7/w5TI/0wqkcovDV8u4nI/TW//ZMA/2E3IvCKu7AzD/gtIw+l372OmlMHm70GlxQiKfHT2k2UVFqw+lAlov/2AY5UqACIiPDXYXTPbogI0EldHhFRl7nQ9k9naoHdBhEmvxCE3/4CEn2t6BdohfqcRjQuKURSYzhzsrau0H++tjSli6KIvafO4Mt9ufjqQB4s9TYodb7wVYkY2TMCvSL82j19PD09vUvPIyJytua2f4oBkGK14bdMA/7Iq0C2SYmSOi3GpYQhgcM+SCYYzpyorSv0N+diTemiKOJ4kQnfHyrAVwfyoC/787WSg9X4/b/P4KaHHkNspH+7Xr/57oC2M5lMHTqfiKizqJUKjE0JQ3K4L35OL0ZFdR2+PpiPnmG+GJUcKnV5RAxnztTaFfpb0lJTekmlBXtzyrD1eAm2Hi9BQUWN4zkfjRJX943EX4bEQH0mB0Pm7UBH1lq8UHdAa6Tv3oofVr6Jmpqaix9MRCShmCBvTB0ehx1ZpUg7XY7jxSacNFShl58CUKqlLo88GMNZJ7jQCv0XIoqA0jcYaYUWHPgtGwdPl2O//gxyz1Q3Ok6nVuCypFBcPyAKV/UJdwz037//lFPqB5rvDmiNIn2W02ogIupsaqUCV/TshtRIf2w5Xoz88hocqVAh6t53cbDIwu2fSBIMZxIQRRHmWitKq2pRVlWLUpMFpVW1MBjViJn1IRZtKwNQ5jheEICeYX64rEcIxvQKw/DEYOjUSulugIjIzXTz0+KWS2JwvMiErRkFQGAE6m1cD42kwXDWySx1VhhMtTBUWVBqqkVplQVlplrU1NuaOVqAaLMiOkCDfnGh6BMVgEvigjAgNgB+OjaxExF1JkEQ0CvCD9oKPf7772UYfOsrUpdEHorhzIlOldfBp/cYHCpXYl9aHgym2gvu5xbgpUaIjwYhvhoE+2hQX5qL/z16K77avROXXMLGdCIiKagUQNXhTVKXQR6M4cyJnt1WhtDrHsVxIwD8OYvSV6tCqK8Gob5ahPhoEOyrQbC3Bipl49WpcytFwFrXtUUTERGRrLhNOFu2bBleffVVFBYWYsCAAVi6dCmGDRvWpTWkhmrwy4596J2agvjoCIT4ahHqo4G2jePDuMYYERGR53KLcPbpp59i7ty5WL58OYYPH44lS5Zg4sSJOHbsGMLCwrqsjscuC8Kav8/D1GVrERMT2ObzucYYERERuUU4e/3113Hfffdh5syZAIDly5fju+++w//+9z888cQTElfXelxjjIiIiFw+nNXW1mLfvn2YP3++4zGFQoHx48djx44dzZ5jsVhgsVgc31dUVAAAjEZjh2ppaLHKzTwCS3XbdwloWCOsrtbSrvPrau33VJhzHFk+3m0+/9wa2nsNni/t+XKogefzZ8DVzy/JzQZg/53e0c+FBn5+7d9OjzyPIIqiSy/kkp+fj+joaGzfvh0jRoxwPP74449j69at2LVrV5NznnnmGTz77LNdWSYREXmwiooK+Pu3b1s98jwu33LWHvPnz8fcuXMd39tsNpSVlSEkJMQj/mdjNBoRGxuL06dPe+QvC0++f967Z9474Nn3L4d79/Pzk+R1yTW5fDgLDQ2FUqlEUVFRo8eLiooQERHR7DlarRZarbbRY4GBgZ1Vomz5+/t73C/pc3ny/fPePfPeAc++f0++d3ItiosfIm8ajQaDBw/Gpk1/Lhhos9mwadOmRt2cRERERK7A5VvOAGDu3LmYPn06hgwZgmHDhmHJkiWoqqpyzN4kIiIichVuEc5uu+02lJSU4KmnnkJhYSEGDhyIDRs2IDw8XOrSZEmr1eLpp59u0rXrKTz5/nnvnnnvgGffvyffO7kml5+tSUREROROXH7MGREREZE7YTgjIiIikhGGMyIiIiIZYTgjIiIikhGGMzexbNkyJCQkQKfTYfjw4di9e3eLxx45cgQ333wzEhISIAgClixZ0uSYZ555BoIgNPpKSUnpxDtov7bc+/vvv4/LL78cQUFBCAoKwvjx45scL4oinnrqKURGRsLLywvjx49HZmZmZ99Guzn7/mfMmNHkvb/66qs7+zbapS33vnbtWgwZMgSBgYHw8fHBwIED8dFHHzU6xpXee2ffuyu970Db7v9ca9asgSAImDJlSqPHXem9Jw8gkstbs2aNqNFoxP/973/ikSNHxPvuu08MDAwUi4qKmj1+9+7d4qOPPip+8sknYkREhPjGG280Oebpp58W+/TpIxYUFDi+SkpKOvlO2q6t937nnXeKy5YtEw8cOCCmp6eLM2bMEAMCAsTc3FzHMS+99JIYEBAgrlu3Tjx48KB4/fXXi4mJiWJ1dXVX3Vardcb9T58+Xbz66qsbvfdlZWVddUut1tZ7/+WXX8S1a9eKR48eFU+cOCEuWbJEVCqV4oYNGxzHuMp73xn37irvuyi2/f4bZGdni9HR0eLll18u3nDDDY2ec5X3njwDw5kbGDZsmDhr1izH91arVYyKihIXL1580XPj4+NbDGcDBgxwYpWdoyP3LoqiWF9fL/r5+YkrV64URVEUbTabGBERIb766quOY8rLy0WtVit+8sknzi3eCZx9/6Jo/5A+/4NLjjp676IoioMGDRKffPJJURRd67139r2Louu876LYvvuvr68XL7vsMvE///lPk3t1pfeePAO7NV1cbW0t9u3bh/HjxzseUygUGD9+PHbs2NGha2dmZiIqKgrdu3fH1KlTodfrO1quUznj3s1mM+rq6hAcHAwAyM7ORmFhYaNrBgQEYPjw4R3++3S2zrj/Blu2bEFYWBh69eqFv/3tbygtLXVq7R3V0XsXRRGbNm3CsWPHcMUVVwBwnfe+M+69gdzfd6D9979o0SKEhYXhnnvuafKcq7z35DncYocAT2YwGGC1WpvshhAeHo6MjIx2X3f48OFYsWIFevXqhYKCAjz77LO4/PLLcfjwYfj5+XW0bKdwxr3PmzcPUVFRjl/KhYWFjmucf82G5+SiM+4fAK6++mrcdNNNSExMRFZWFv75z39i0qRJ2LFjB5RKpVPvob3ae+8VFRWIjo6GxWKBUqnEO++8gwkTJgBwnfe+M+4dcI33HWjf/f/222/473//i7S0tGafd5X3njwHwxk1a9KkSY4/9+/fH8OHD0d8fDw+++yzZv/n6YpeeuklrFmzBlu2bIFOp5O6nC7X0v3ffvvtjj/369cP/fv3R1JSErZs2YJx48ZJUarT+Pn5IS0tDSaTCZs2bcLcuXPRvXt3jBkzRurSOt3F7t1d3/fKykrcddddeP/99xEaGip1OUStwnDm4kJDQ6FUKlFUVNTo8aKiIkRERDjtdQIDA9GzZ0+cOHHCadfsqI7c+2uvvYaXXnoJP//8M/r37+94vOG8oqIiREZGNrrmwIEDnVe8E3TG/Tene/fuCA0NxYkTJ2TzId3ee1coFOjRowcAYODAgUhPT8fixYsxZswYl3nvO+PemyPH9x1o+/1nZWUhJycH1113neMxm80GAFCpVDh27JjLvPfkOTjmzMVpNBoMHjwYmzZtcjxms9mwadMmjBgxwmmvYzKZkJWV1egXl9Tae++vvPIKnnvuOWzYsAFDhgxp9FxiYiIiIiIaXdNoNGLXrl1O/ft0hs64/+bk5uaitLTULd7789lsNlgsFgCu8953xr03R47vO9D2+09JScGhQ4eQlpbm+Lr++usxduxYpKWlITY21mXee/IgUs9IoI5bs2aNqNVqxRUrVohHjx4V77//fjEwMFAsLCwURVEU77rrLvGJJ55wHG+xWMQDBw6IBw4cECMjI8VHH31UPHDggJiZmek45pFHHhG3bNkiZmdni7///rs4fvx4MTQ0VCwuLu7y+7uQtt77Sy+9JGo0GvGLL75otGRAZWVlo2MCAwPFr7/+Wvzjjz/EG264QbZT6p19/5WVleKjjz4q7tixQ8zOzhZ//vln8ZJLLhGTk5PFmpoaSe6xJW299xdffFH86aefxKysLPHo0aPia6+9JqpUKvH99993HOMq772z792V3ndRbPv9n6+5mamu8t6TZ2A4cxNLly4V4+LiRI1GIw4bNkzcuXOn47nRo0eL06dPd3yfnZ0tAmjyNXr0aMcxt912mxgZGSlqNBoxOjpavO2228QTJ0504R21XlvuPT4+vtl7f/rppx3H2Gw2ceHChWJ4eLio1WrFcePGiceOHevCO2obZ96/2WwWr7rqKrFbt26iWq0W4+Pjxfvuu8/xoSc3bbn3BQsWiD169BB1Op0YFBQkjhgxQlyzZk2j67nSe+/Me3e1910U23b/52sunLnSe0/uTxBFUZSgwY6IiIiImsExZ0REREQywnBGREREJCMMZ0REREQywnBGREREJCMMZ0REREQywnBGREREJCMMZ0REREQywnBGREREJCMMZ0REREQywnBGREREJCMMZ0REREQywnBGREREJCP/DxV+Bcq2b+z8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 617.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(citricAcid, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Citric Acid\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.6816899375976969\n",
      "Skewness pada distribusi = -0.10283436799948968\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['citric acid'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['citric acid'].mean() - data['citric acid'].median()))/data['citric acid'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Residual Sugar\n",
    "Distribusi Residual Sugar bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Residual Sugar juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EHTt2bLCkziOPPILvvvsOd9xxB+677z5kZGTgiy++aDAZpLnZxowZg9tvvx3PP/88Lly4gO7du2Pjxo34/vvvMXPmzKuO3VKPPfYYPvzwQ0yePBkHDx5EdHQ0vvvuO+zcuRNLliy57jWfLTFy5Eh07doVixcvxrRp03Dbbbdh6tSpWLhwIY4cOYLhw4dDqVQiLS0N3377Ld5++22MHz8en376Kd5//33cc889iIuLQ3l5OT7++GN4eXnhzjvvvOb7zZ8/H+vXr8egQYPwt7/9zVp6k5KScOzYsQb7PvLII/jXv/6FRx55BH369MGOHTtw9uzZq445evRofP755/D29kaXLl2we/du/PLLL9ddFuh6ZsyYgcrKStxzzz1ISEhATU0Ndu3aha+//hrR0dENJks1NeO8efOwceNGDBw4EI8//rj1Pxldu3ZtcEvG4cOHQ6VSYcyYMZg6dSr0ej0+/vhjBAUFWc9UEpENSDgj2m5wuRr6o/rlauo/VCqVGBISIg4bNkx8++23GyyLUu/K5Wo2b94s3n333WJYWJioUqnEsLAw8cEHHxTPnj3b4HXff/+92KVLF+vSHPXLe9x2221iUlJSo/mutVzNV199Jc6ePVsMCgoStVqtOGrUqAZLn9R78803xQ4dOohqtVocOHCgeODAgauOeb1sVy5XI4qiWF5eLj755JNiWFiYqFQqxfj4ePH1118XzWZzg/1wxRIw9a61jM6V8vLyxClTpogBAQGiSqUSu3Xr1uiSKM1druZa+y5fvvyqZVc++ugjsXfv3qJWqxU9PT3Fbt26iU8//bSYnZ0tiqIoHjp0SHzwwQfFyMhIUa1Wi0FBQeLo0aPFAwcONDg2rli2RRRFcfv27WLv3r1FlUolxsbGisuWLbvq35YoWpZtefjhh0Vvb2/R09NTvO+++8T8/PyrjllcXGz9enl4eIgjRowQT58+fdXXu6nL1axbt07861//KiYkJIgeHh6iSqUSO3bsKM6YMUPMy8trUUZRtHy/9OzZU1SpVGJcXJz473//W/zHP/4hajSaBvutXbtWTE5OFjUajRgdHS0uWrRI/O9//ysCEM+fP2/drzl//0TUkCCKvN1HWVkZF7gmIrIjY8eOxcmTJxu97pKI2g6vMSQiIklVVVU1eJyWloaff/650Vs/ElHb4jWGREQkqdjYWEyePBmxsbG4ePEiPvjgA6hUKjz99NNSRyNyOSyGREQkqTvuuANfffUVcnNzoVar0b9/f7z66quNLvhNRG2L1xiC1xgSERERAbzGkIiIiIjqsBgSEREREQAWQyIiIiKqw2JIRERERABYDImIiIioDoshEREREQHgOoZEksvMzIROp2v1cQICAhAZGWmDRERE5KpYDIkklJmZiYTERFRVVrb6WFo3N5xOTWU5JCKiFmMxJJKQTqdDVWUlJjzzOoIj41p8nLzMDKxY9BR0Oh2LIRERtRiLIZEdCI6MQ3h8ktQxiIjIxXHyCREREREBYDEkIiIiojoshkREREQEgMWQiIiIiOqwGBIRERERABZDIiIiIqrDYkhEREREAFgMiYiIiKgOF7gmchKamF54cf1F5P2Yj5JqE4Ld5Yj3U+HuBHdEeSubfBzec5mIyHWxGBI5uLLqWmy7bEbwfQtwsgwATACAy+UmXC6vwtYLFag4uQ1FG9+HWFt9w+PxnstERK6LxZDIgRXqDVhzJBt6kwaiqRYBRh2SIgKgVYjQ1wq4WCFDVqUcHl2HoEPPwRgQaITbdb7rec9lIiLXxmJI5KAKyg3436EsGIxmuMGAtH9Px+An56NHty7WffoAyC6pwo/HclBaC/xW5Ib7+0TAXc1vfSIiuhonnxA5oKpaE348lg2D0YwQLw264QKMJTmN7hvmo8UDfSPgo1WivNqItUezUWsyt3NiIiJyBCyGRA7GLIpYdyIHZdVGeGuVuLtHGJS4ftHzqttPq5Qjv9yAjafyIIpiOyUmIiJHwWJI5GCOXirBpaIqKGQCRieHQqOUN+l1Pm4qjE4OhUwA0vP1OJ1b3sZJiYjI0bAYEjmQ0qpa7MooBADc2ikQAR7qZr0+zEeLfrH+AIBtZwpQVlVr84xEROS4WAyJHIQoithyOh9Gs4gOPlp0DfNq0XH6RPoi1FuDGpMZW87kc0iZiIisWAyJHMR5XQUyiyohlwkYmhgEQRBadByZTMCwLsGQCcDFwkqcL6ywcVIiInJULIZEDsBsFrEz3TKE3CPCB75uqlYdz9dNhZ6RvgCAHWd1MJo5S5mIiFgMiRzCqZwyFFXWQKOQoW+Ur02OeVO0H9xVcpRW1eLopVKbHJOIiBwbiyGRnTOazdh7vggA0DfGD+omzkK+EZVChgFxAQCAAxeKYDCabHJcIiJyXCyGRHYuNacceoMRHmoFksO9bXrshFBP+LopUW0043BmiU2PTUREjofFkMiOmc0iDl4sBgD0ivSBQmbbb1mZIKB/3fI1hzNLYOBJQyIil8ZiSGTHzuaVo7SqFlqlHF072PZsYb2OQR4I9FCjxmRGerlthqmJiMgxsRgS2SlRFHEg03K2sEekD5Tytvl2FQQBfWMsE1oy9DIIKm2bvA8REdk/FkMiO5VVXIVCfQ2UcgHJbXS2sF5coAd83JSoNQvw6D6iTd+LiIjsF4shkZ06cqkEAJAY4tXk+yG3lEwQ0KduGRyvvveg1sS7oRARuSIWQyI7VFJZg3M6yx1JekT4tMt7JoR4QSsXofD0x85LVe3ynkREZF9YDIns0LEsy4LTUf5u8HVv3V1OmkouExDrYZmWvC69sl3ek4iI7AuLIZGdMZrMSM0pAwB0D/dp1/eO9jBDNNYirajWOpRNRESug8WQyM5kFFSg2miGh1qBKH+3dn1vjRyoSN0BAPhs14V2fW8iIpIeiyGRnTmRbRlGTgrzgkwQ2v39yw/9CAD48VgOdHpDu78/ERFJh8WQyI6UVNYgq9gy8aNLmJckGWpy0xDvp0SNyYyV+zIlyUBERNJgMSSyIyezLdcWRvm7wUujlCzHnfGWIewv9mTCaDJLloOIiNoXiyGRnTCZRZyqm3TSNaxtF7S+kQHhWgR4qJBbVo2Np/IkzUJERO2HxZDITpzXVaCyxgStUo6YAHdJsyjlAh68KRIAsJyTUIiIXAaLIZGdOFk36aRLmBfksvafdHKlP/eLhFwmYN/5IqTn66WOQ0RE7YDFkMgOVBqBi4WWRaWTJJp0cqVQby1u7xwIAPj2wCWJ0xARUXuQtBju2LEDY8aMQVhYGARBwJo1axo8L4oi5syZg9DQUGi1WqSkpCAtLa3BPkVFRZgwYQK8vLzg4+ODhx9+GHo9z26QY8mskEEE0MFHC1+39rnTSVPc1ycCAPC/Q1mo5SQUIiKnJ2kxrKioQPfu3bF06dJGn3/ttdfwzjvvYNmyZdi7dy/c3d0xYsQIVFdXW/eZMGECTp48iU2bNuHHH3/Ejh078Nhjj7XXp0BkExcr5ACALqH2cbaw3u0JQQjwUEOnr8GW0/lSxyEiojYmaTEcOXIkXn75Zdxzzz1XPSeKIpYsWYIXXngBd999N5KTk/HZZ58hOzvbemYxNTUV69evx7///W/069cPt9xyC959912sXLkS2dnZ7fzZELWMKrQT9EYBCpmAjkEeUsdpQCmXYVzvDgCAr/dzOJmIyNnZ7TWG58+fR25uLlJSUqzbvL290a9fP+zevRsAsHv3bvj4+KBPnz7WfVJSUiCTybB37952z0zUEu5JtwMA4gI9oFLY37fk/XXDydvO5CO3tPoGexMRkSNTSB3gWnJzcwEAwcHBDbYHBwdbn8vNzUVQUFCD5xUKBfz8/Kz7NMZgMMBg+P1WX2VlZbaKTdQstSYR7om3AgASQj0lTvO71NTUBo+7BKhwSleDd37Yh/FdbnxWMyAgAJGRkW0Vj4iI2ojdFsO2tHDhQsyfP1/qGEQ4nGuA3M0bapmISF83qeOgrKgAADBx4sQG2927DkHAqFn49NezWPjQYwDE6x5H6+aG06mpLIdERA7GbothSEgIACAvLw+hoaHW7Xl5eejRo4d1n/z8hhfEG41GFBUVWV/fmNmzZ2PWrFnWx2VlZYiIiLBheqKm2X7Rcl/kSHczZHawdmGV3nL2fNTU59E5ubd1u9EM/HRZBHxDMfHNVQjSXLsY5mVmYMWip6DT6VgMiYgcjN0Ww5iYGISEhGDz5s3WIlhWVoa9e/fi8ccfBwD0798fJSUlOHjwIHr3tvwS27JlC8xmM/r163fNY6vVaqjV6jb/HIiup7SqFgeyLdfsRbrb11Iw/mFRCI9ParAtQczDictlyJf5oVf8tf/jRUREjkvSYqjX65Genm59fP78eRw5cgR+fn6IjIzEzJkz8fLLLyM+Ph4xMTF48cUXERYWhrFjxwIAEhMTcccdd+DRRx/FsmXLUFtbi+nTp+OBBx5AWFiYRJ8VUdP8fDwHtWagpuACvCPs/99rUqg3TlwuQ0a+HjWdzXY5UYaIiFpH0p/sBw4cQM+ePdGzZ08AwKxZs9CzZ0/MmTMHAPD0009jxowZeOyxx9C3b1/o9XqsX78eGo3GeowVK1YgISEBQ4cOxZ133olbbrkFH330kSSfD1FzrDqUBQCoOLEFgvSjyDcU7KWGr5sSRrPIW+QRETkpSc8YDh48GKJ47WuVBEHAggULsGDBgmvu4+fnhy+//LIt4hG1mUtFldh/oRgCgIpT2wFMvNFLJCcIAhJCvbA7oxCpuWXoYie37iMiItvhWBCRBNYetSzA3i1IBZO+UOI0TZcQYllSJ6u4CmVVtRKnISIiW2MxJJLAD3XF8JZIrcRJmsdLo0S4ryXz6dxyidMQEZGtsRgStbPTuWU4nVsOlVyGm8M1N36BnUkMsQwhp+aWXfdSECIicjwshkTtbO0Ry9nCwZ0D4aFyvG/BjkEeUMgElFTWIq/McOMXEBGRw3C830pEDkwURev1hXf1sP8lahqjUsjQMchyW7zUHN5OkojImbAYErWjQ5klyCqugrtKjqEJwTd+gZ2qn4RyNq8cRrN9Lc5NREQtx2JI1I7WHrkMABiRFAKtSi5xmpaL8HODh1qBaqMZF3SVUschIiIbYTEkaidGkxk/Hc8BAIxx0GHkejJBQOe6s4YcTiYich4shkTtZFdGIXT6Gvi5q3BLxwCp47RaYl0xvFBYgaoak8RpiIjIFlgMidrJ93WzkUd1C4VS7vjfev4eagR5qmEWLdcaEhGR43P8305EDqC61oQNJ3MBOO5s5MYkhv6+piERETk+FkOidrD1dD70BiM6+GjRO9JX6jg20ynYAzIByCszoKiiRuo4RETUSiyGRO2gfhh5TPcwyGSCxGlsx02lQLS/OwBOQiEicgYshkRtrKy6FlvO5AMA7uruPMPI9RJCLZNQTueWw8xb5BEROTQWQ6I2tuFELmqMZsQHeSCxrkQ5k5gAd6gVMugNRmQVV0kdh4iIWoHFkKiN1d8C7+4eYRAE5xlGrqeQydApuO6sIYeTiYgcGoshURsqKDdgZ7oOgOX6QmdVfyY0vUAPI++QR0TksFgMidrQT8eyYRaBHhE+iKqbpOGMQrw08NEqUWsScbmSP1aIiBwVf4ITtaHv/zCM7MwEQbCuaXixgj9WiIgcFX+CE7WRzMJKHM4sgUwARiWHSh2nzSXU3SKvwCBA7un4t/wjInJFLIZEbeSHY5azhQPiAhDkqZE4Tdvz0ioR7qMFIMA96Xap4xARUQuwGBK1ke+PXAbgXLfAu5H6NQ09ug6ByDUNiYgcDoshURs4nVuGs3l6qOQyjEgKkTpOu+kY5AG5IELpH4H04lqp4xARUTOxGBK1gfpb4N2eEAhvrVLiNO1HrZAjTGtZr2bbBS52TUTkaFgMiWxMFEWsPVI/G7mDxGnaX5S7pRj+mlmFGi5qSETkUFgMiWzsUGYxLpdUwUOtwJCEIKnjtLsgjQhjeSH0NSK21t0jmoiIHAOLIZGN1Q8jD08KhkYplzhN+xMEoOLkVgDA/w5mSZyGiIiaQyF1ACJHlpmZCZ1OZ31sNItYc8hylizJowqHDh267utTU1PbNJ9UKk5uhffN47H1TD6KKmrg566SOhIRETUBiyFRC2VmZiIhMRFVlZXWbZqYXgi+bwFMFSV4ZPRdgNi0a+z0en1bxZREre4iYn0VOFdsxI/HsvGX/tFSRyIioiZgMSRqIZ1Oh6rKSkx45nUER8YBAPYXypFZAcQHe+K+97674TFS923Huk/fRnV1dVvHbXe3R7nhXHEZ/ncwi8WQiMhBsBgStVJwZBzC45NgNJmRc/kcABG9O0chzEd7w9fmZWa0fUCJDIzU4NNj5TiaVYr0/HJ0DPKUOhIREd0AJ58Q2ch5XQVqTSI8NQqEejv/LfBuxEcjx+DOgQCAVYcuS5yGiIiagsWQyEbS8i3XCXYK9oQgCBKnsQ/jeoUDAFYfvgyTmbfIIyKydyyGRDZQazLjvK4CABAf5CFxGvsxJDEIXhoFckqrsedcodRxiIjoBlgMiWzgvK4CRrMIb60SQZ5qqePYDbVCjjHdwwAA/zvENQ2JiOwdJ5+QS7py/cGW+OMahGfzygFYzhZyGLmhcb3DsWJvJtafyMVLdxvhruaPHSIie8Wf0ORyGlt/sDVKyvW4UGo5S9gpmDNvr9QzwgcxAe44r6vA+hO5GNc7XOpIRER0DSyG5HIaW3+wJerXILxcIcBkFuHjpkSAB+/wcSVBEHBvzw54c9NZrDqcxWJIRGTHWAzJZdWvP9hS9WsQFpgsS9N0CuJs5Gu5p5elGO7KKER2SVWT1ngkIqL2x8knRK0gqLQoqiuG8cGcjXwt4b5uuDnWD6JoWbqGiIjsE4shUSu4xd8MEQL83FTwd+cw8vXcW7em4f8OZUEUuaYhEZE9YjEkagW3hEEALGcLOYx8fXd2C4VGKcO5ggocuVQidRwiImoEiyFRCxkhgzamJwAuat0UHmoF7kgKAcA1DYmI7BWLIVELFcITglwJd6EW/h5c1LopxveOAACsPZKN6lqTxGmIiOhKLIZELaSDFwAgUFElcRLHMSDOHx18tCirNmLDyVyp4xAR0RVYDIlaoLrWhBK4AwAC5SyGTSWTCdZ1DL87yOFkIiJ7w2JI1ALpBXqIEFCTdw5uMg6JNsef6orhb+k6XC5hqSYisicshkQtkJanBwBUnP5V4iSOJ8LPDf1j/SGKwP941pCIyK6wGBI1U3WtCVnFlvssV57ZKXEax/SnPr8PJ5vNXNOQiMhesBgSNdMFXQXMIuCGahiLs6WO45BGdg2Fh1qBzKJK7LtQJHUcIiKqw3slEzVTeoFlGNkfeomT2LfU1NTrPt+/gxKbzhnx4cajUN3k0+g+AQEBiIyMbIN0RETUGBZDomYwmsy4WGgZRvZHucRp7FNZUQEAYOLEidfdTxWWgNCH3sDms8X49O+jIdZcPRFF6+aG06mpLIdERO2ExZCoGTKLKmE0i/DUKOBeXS11HLtUpS8DAIya+jw6J/e+5n6iCGzMEaGHBmNf+goxHuYGz+dlZmDFoqeg0+lYDImI2gmLIVEzZBRUAADiAjwgcELtdfmHRSE8Pum6+3RXFWFnRiFyTB4YFB/RTsmIiOhaOPmEqInMZhHndJbrCmMD3SVO4xwSQ70gCEBOaTWKK2qkjkNE5PJYDImaKLu0CtW1ZmgUMnTw0Uodxym4qxWI8nMDAJzKKZM4DRERsRgSNVH9MHJMoDtkMkHiNM6jS5jlntOpuWVc05CISGIshkRNIIoiMuqWqYkL9JA4jXOJDfCAVilHhcGEi0WVUschInJpLIZETVCgN6C82giFTEBk3dAn2YZcJqBziCcADicTEUmNxZCoCeqHkaP83aCU89vG1rqEWoaTzxXoUVVjkjgNEZHr4m84oia4oLMUw9gADiO3hUBPNYI81TCLwJk8LhxORCQVFkOiG6gwGJFfbgBgOWNIbaP+rOGpbA4nExFJhcWQ6AYuFFrOFgZ7qeGu5prwbaVziCfkgoACvQH55byrDBGRFFgMiW7gQt29kaP9uah1W9Io5daFw3nWkIhIGiyGRNdhMovIZDFsN/VrGp7JLYeJSxoSEbU7uy6GJpMJL774ImJiYqDVahEXF4eXXnoJovj7bwxRFDFnzhyEhoZCq9UiJSUFaWlpEqYmZ5JTWoUakxlapRzBXmqp4zi9SD83eKgVqDaakVPFRcSJiNqbXRfDRYsW4YMPPsB7772H1NRULFq0CK+99hreffdd6z6vvfYa3nnnHSxbtgx79+6Fu7s7RowYgepqXqNErXdBZzlbGOXvBkFgUWlrMkFAQt2ahhf0conTEBG5Hrsuhrt27cLdd9+NUaNGITo6GuPHj8fw4cOxb98+AJazhUuWLMELL7yAu+++G8nJyfjss8+QnZ2NNWvWSBuenEL9xBMOI7ef+uHkvGoBcg9/idMQEbkWuy6GAwYMwObNm3H27FkAwNGjR/Hbb79h5MiRAIDz588jNzcXKSkp1td4e3ujX79+2L17tySZyXmUVdWisKIGArhMTXvydVMhzFsDQIB70u1SxyEicil2vfbGs88+i7KyMiQkJEAul8NkMuGVV17BhAkTAAC5ubkAgODg4AavCw4Otj7XGIPBAIPBYH1cVsYZkHS1+rOFod4aaJQc1mxPXcK8kF1aDY/kYQ2uKSYiorZl12cMv/nmG6xYsQJffvklDh06hE8//RRvvPEGPv3001Ydd+HChfD29rZ+RERE2CgxORPrMjUBHEZub/FBnpALIpR+HXC6sFbqOERELsOui+FTTz2FZ599Fg888AC6deuGhx56CE8++SQWLlwIAAgJCQEA5OXlNXhdXl6e9bnGzJ49G6WlpdaPS5cutd0nQQ7JaDLjUhGXqZGKSiFDuJsZALDlfKXEaYiIXIddF8PKykrIZA0jyuVymM2WXxgxMTEICQnB5s2brc+XlZVh79696N+//zWPq1ar4eXl1eCD6I8ul1TBaBbhoVYgwEMldRyXFOVu+T7fdakalTVGidMQEbkGu77GcMyYMXjllVcQGRmJpKQkHD58GIsXL8Zf//pXAIAgCJg5cyZefvllxMfHIyYmBi+++CLCwsIwduxYacOTQ8usO1sY6cdlaqQSoBZRW5wD+IZi/Ylc3NsrXOpIREROz66L4bvvvosXX3wRf/vb35Cfn4+wsDBMnToVc+bMse7z9NNPo6KiAo899hhKSkpwyy23YP369dBoNBImJ0d3qagKABDhp5U4iesSBKDixGb4DJqI7w5msRgSEbUDuy6Gnp6eWLJkCZYsWXLNfQRBwIIFC7BgwYL2C0ZOrarGhAK9ZdZ6hC+XqZGSvq4Y7sooxKWiSkT48e+DiKgt2XUxJJJCVrFlGNnfXQV3Nb9FpGQqK0Csey3OVSix9Kf9uC/Js0XHCQgIQGRkpI3TERE5H/7WI7pC/fWFPDslrbKiAgDAvpVvI2DMP/H5znQs+sujAJq/rqHWzQ2nU1NZDomIboDFkOgKl4p5faE9qNJbFp6/ddAgnBVEwCcED725CoGa5hXDvMwMrFj0FHQ6HYshEdENsBgS/UFZVS1Kq2ohCEC4D88Y2oPAsAgIPt44mV2GArkfesZfe41SIiJqHbtex5CovWXWXV8Y4qWBSsFvD3vRJdSy1mh6vh41RrPEaYiInBd/8xH9wSVeX2iXQr018NYqUWsSkZ6vlzoOEZHTYjEkqiOKonX9wkguU2NXBEGwnjU8lVMmcRoiIufFYkhUR6evQVWtCQqZgBBvLpBubxJDLUvVXC6pQmlVrcRpiIicE4shUZ1LddcXdvDVQi7jbfDsjadGici6IX6eNSQiahsshkR16q8v5DCy/ao/a5iaUwZRbP56hkREdH0shkQATGYRl0vq1y9kMbRXHQM9oJLLUF5tRFbdepNERGQ7LIZEAArKDag1idAoZAjwUEkdh65BIZehU7AHAA4nExG1BRZDIsB6tjDMRwtB4PWF9qxL2O9rGhqMJonTEBE5FxZDIvxeDDv48jZ49i7ESwNfNyWMZhFpXNOQiMimWAzJ5YmiiOz6YujDYmjvBEFAYt2ahqnZHE4mIrIlFkNyeTp9DQxGM5RyAYEeaqnjUBMkhnhBAJBdWo2Syhqp4xAROQ0WQ3J59WcLQ721kHH9QofgoVFYZ4+fzi2XOA0RkfNgMSSXd5nDyA4pIcSypuHp3HKuaUhEZCMshuTSRFFkMXRQcYEeUMgElFbVIresWuo4REROgcWQXFpJVS0qa0yQywQEe/H6QkeiUsgQF2RZ0/B0DoeTiYhsgcWQXFr92cJgLzUUcn47OJrEuuHks/nlMJk5nExE1Fr8TUguLbuYw8iOLMLXDW4qOaprzbhYWCF1HCIih8diSC6N1xc6NplMQOfg3yehEBFR67AYksuqNAJl1UYIgmWpGnJMCaGWYnhOVwFDLW+RR0TUGiyG5LJ0Bss//0APNVQKfis4qkAPNfzcVTCZRaQV8BZ5REStwd+G5LJ0Bsti1rw/smMTBOH3NQ05O5mIqFVYDMllFdYXQ15f6PA61xXDyyVVKKuulTgNEZHjYjEklySotCirtRTDEC+NxGmotbw0SoTXFfwznIRCRNRiLIbkktQh8QAEeGoUcFcrpI5DNlA/CeV0Dm+RR0TUUiyG5JJUYZ0B8GyhM+kY5AG5TEBRZQ0Kyg1SxyEickgshuSS1KGdALAYOhO1Qo7YAHcAQCqHk4mIWoTFkFyOKIrWM4bB3iyGzqR+dnJaXjnMHE4mImo2FkNyOYVVZig8/CBARJCnWuo4ZEOR/m5QK2SoqDEhu+6uNkRE1HQshuRyzhbWAAC8lSKUcn4LOBOFTIa4QA8AwNk8LnZNRNRc/K1ILietyLLOna+aQ43OqFOwpRim5+thMvPvmIioOVgMyeWkFVqKoZ+KpcEZRfi6QauUo6rWhKziSqnjEBE5FBZDcilGkxkZxXXFUG2WOA21BZlMQHyQ5azhmTzOTiYiag4WQ3IpZ/LKYTCJMBsq4Ml1rZ1Wp2DL7OSM/AqYeGKYiKjJWAzJpRy5VAIAMOSkQRCkzUJtJ8xHAw+1AjUmM/Kq+BdNRNRULIbkUo5klgAAarLPSBuE2pQgCIivm4SSVckfc0RETcWfmORSfj9jyGLo7OqHk7OrZBCUXK+SiKgpWAzJZZRX1yK9wLK2nSH7rMRpqK0Fe6rhrVXCJArQxvWVOg4RkUNgMSSXcTyrFKIIBLrJYa4skToOtTFBEKxrGron3CpxGiIix8BiSC7jcN0wcry/Utog1G7qh5O1cX1QUcPliYiIboTFkFxG/fWFnfxYDF2Fv7sKnkozBIUK+7KrpY5DRGT3WAzJJYiiaC2G8f4qacNQuxEEARFuljOFOzNZDImIboTFkFxCdmk1CsoNUMgExPrwjKErCa8rhkfzDCiqqJE4DRGRfWMxJJdQv35hQqgn1AoueOxKPJWAITcdJhHYeDJX6jhERHaNxZBcwpFLxQCA7uE+0gYhSVSe2QkAWHeCxZCI6HpYDMkl1F9f2CPCR9IcJI3Ks7sAALsydCitqpU4DRGR/WIxJKdXazLj+OVSAEDPSB9pw5AkjEWXEeGlQK1JxObUPKnjEBHZLRZDcnpn88pRXWuGp0aB2AAPqeOQRPqHawBwOJmI6HpYDMnp1Q8jdw/3gUzGiSeu6ua6YrjjbAEqDEaJ0xAR2ScWQ3J69TOSeX2ha4vyViDa3w0Goxlbz+RLHYeIyC6xGJLT48QTAiyLXd/RNRQAh5OJiK6FxZCcWnl1LdIL9ACAHpx44vJGdg0BAGw9nY/qWpPEaYiI7E+LimFsbCwKCwuv2l5SUoLY2NhWhyKylWNZpRBFINxXiwAPtdRxSGLJ4d7o4KNFZY0JO84WSB2HiMjutKgYXrhwASbT1f/bNhgMuHz5cqtDEdkKh5HpjwRBwIgky1nD9RxOJiK6iqI5O69du9b65w0bNsDb29v62GQyYfPmzYiOjrZZOKLWOsyJJ3SFO7qG4L87z2NTah5qjGaoFLyihoioXrOK4dixYwFY/tc9adKkBs8plUpER0fjzTfftFk4otYQRZFnDOkqvaN8EeChhk5vwK4MHQZ3DpI6EhGR3WjWf5XNZjPMZjMiIyORn59vfWw2m2EwGHDmzBmMHj26rbISNcvlkiro9AYoZAK6dvC+8QvIJchlAkYkBQPgcDIR0ZVaNIZy/vx5BAQE2DoLkU0dvWS5DV5CqCc0SrnEaciejKxbtmbjqTwYTWaJ0xAR2Y9mDSX/0ebNm7F582brmcM/+u9//9vqYEStdeRSMQAOI9PV+sX6wcdNiaKKGuy7UIQBcfyPLhER0MIzhvPnz8fw4cOxefNm6HQ6FBcXN/ggsge/X1/oK20QsjtKuQzDEjmcTER0pRadMVy2bBmWL1+Ohx56yNZ5iGyi1mTG8cuWoWSeMaTGjOwWgm8PZmHDyVzMG5PE+2gTEaGFZwxramowYMAAW2dp1OXLlzFx4kT4+/tDq9WiW7duOHDggPV5URQxZ84chIaGQqvVIiUlBWlpae2SjezXmdxyVNea4alRIDbAXeo4ZIcGdgyAu0qOvDIDTmSXSh2HiMgutKgYPvLII/jyyy9tneUqxcXFGDhwIJRKJdatW4dTp07hzTffhK/v70ODr732Gt555x0sW7YMe/fuhbu7O0aMGIHq6uo2z0f264/L1PBMEDVGrZDj1k6BAIBfTuVJnIaIyD60aCi5uroaH330EX755RckJydDqVQ2eH7x4sU2Cbdo0SJERETgk08+sW6LiYmx/lkURSxZsgQvvPAC7r77bgDAZ599huDgYKxZswYPPPCATXKQ4+H6hdQUKYnBWHciFxtP5WHW8M5SxyEiklyLzhgeO3YMPXr0gEwmw4kTJ3D48GHrx5EjR2wWbu3atejTpw/+9Kc/ISgoCD179sTHH39sff78+fPIzc1FSkqKdZu3tzf69euH3bt32ywHOZ76Ytg93EfSHGTfhiQEQSYAp3PLcamoUuo4RESSa9EZw61bt9o6R6POnTuHDz74ALNmzcJzzz2H/fv344knnoBKpcKkSZOQm2uZTRgcHNzgdcHBwdbnGmMwGGAwGKyPy8rK2uYTIEmUVdcio0APAOgR6SNtGLJrvu4q9In2w77zRfglNQ9TBsbc+EVERE7Mrm8Sajab0atXL7z66qvo2bMnHnvsMTz66KNYtmxZq467cOFCeHt7Wz8iIiJslJjswfGsUogiEO6rRYCHWuo4ZOeGd7H8x/KXVF5nSETUojOGt99+OwTh2hf0b9mypcWB/ig0NBRdunRpsC0xMRH/+9//AAAhISEAgLy8PISGhlr3ycvLQ48ePa553NmzZ2PWrFnWx2VlZSyHToTXF1JzpCQG4+WfUrH3XBFKq2rhrVXe+EVERE6qRWcMe/Toge7du1s/unTpgpqaGhw6dAjdunWzWbiBAwfizJkzDbadPXsWUVFRACwTUUJCQrB582br82VlZdi7dy/69+9/zeOq1Wp4eXk1+CDncTizBACLITVNdIA74oM8YDSL2HYmX+o4RESSatEZw7feeqvR7fPmzYNer29VoD968sknMWDAALz66qu47777sG/fPnz00Uf46KOPAACCIGDmzJl4+eWXER8fj5iYGLz44osICwvD2LFjbZaDHIcoitYzhj15fSE1UUqXYKTl67HpVB7u7tFB6jhERJKx6TWGEydOtOl9kvv27YvVq1fjq6++QteuXfHSSy9hyZIlmDBhgnWfp59+GjNmzMBjjz2Gvn37Qq/XY/369dBoNDbLQY7jckkVdHoDFDIBSWHeUschBzGs7jrD7WcKUGM032BvIiLn1aIzhteye/dumxey0aNHY/To0dd8XhAELFiwAAsWLLDp+5Jjqj9bmBjqBY1SLm0Ychg9wn0Q4KGGTm/A3vOFGBQfKHUkIiJJtKgY3nvvvQ0ei6KInJwcHDhwAC+++KJNghG1xBFeX0gtIJMJSEkMwsr9l/DLqTwWQyI7NnnyZJSUlGDNmjXX3Gfw4MHo0aMHlixZYrP3nTdvHtasWWPT9ZrtUYuKobd3wyE6mUyGzp07Y8GCBRg+fLhNghG1hHVhaxZDaqaUxGBLMUzNx7y7xOuuvEBkLzIzM6HT6drlvQICAhAZGdku73U9b7/9NkRRlDqG02pRMfzjLeqI7EWtyYzjl0sB8IwhNd/AjgHQKGW4XFKFUzllvEaV7F5mZiYSEhNRVdk+d+3RurnhdGpqq8phTU0NVCpVq3JceXKKrq+5X/NWXWN48OBBpKamAgCSkpLQs2fP1hyOqFXO5JbDYDTDU6NAbIC71HHIwWhVcgyKD8SmU3n45VQ+iyHZPZ1Oh6rKSkx45nUER8a16XvlZWZgxaKnoNPpmlUMBw8ejK5du0KhUOCLL75At27dsHXrVpw4cQJPPfUUfv31V7i7u2P48OF46623EBAQAAD47rvvMH/+fKSnp8PNzQ09e/bE999/D3d396uGkisqKvD4449j1apV8PT0xD//+c+rcgiCgNWrVzdYscTHxwdLlizB5MmTAQDPPPMMVq9ejaysLISEhGDChAmYM2cOlMqmrW1aXFyM6dOnY+PGjdDr9QgPD8dzzz2HKVOmYNu2bbj99ttRXFwMHx8fAMCRI0fQs2dPnD9/HtHR0QCAjz/+GAsWLEBhYSFGjBiBQYMGYcGCBSgpKQEAZGRkYNasWdizZw8qKiqQmJiIhQsXNrg1cHR0NB5++GGkpaVhzZo1uPfee7F8+fIm/521qBjm5+fjgQcewLZt26yfYElJCW6//XasXLkSgYG8Pofa3x8XtpbJOAxIDdX/J/Z6OrlVYxOA7w+ewyC/8gbP2cswGtGVgiPjEB6fJHWMa/r000/x+OOPY+fOnQAsfWHIkCF45JFH8NZbb6GqqgrPPPMM7rvvPmzZsgU5OTl48MEH8dprr+Gee+5BeXk5fv3112sOHz/11FPYvn07vv/+ewQFBeG5557DoUOHrnuji8Z4enpi+fLlCAsLw/Hjx/Hoo4/C09MTTz/9dJNe/+KLL+LUqVNYt24dAgICkJ6ejqqqqia//86dO/F///d/WLRoEe666y788ssvV83b0Ov1uPPOO/HKK69ArVbjs88+w5gxY3DmzJkGP5/eeOMNzJkzB3Pnzm3y+9drUTGcMWMGysvLcfLkSSQmJgIATp06hUmTJuGJJ57AV1991ZLDErUK73hCjSkrKgBgWU7rRmRu3gif/jnOFRtx0+DhMJUXWp+zxTAakSuKj4/Ha6+9Zn388ssvo2fPnnj11Vet2/773/8iIiICZ8+ehV6vh9FoxL333mu9ocW1bp6h1+vxn//8B1988QWGDh0KwFJEw8PDm53zhRdesP45Ojoa//znP7Fy5comF8PMzEz07NkTffr0sR6jOd59912MHDnSesazU6dO2LVrF3788UfrPvU3Fqn30ksvYfXq1Vi7di2mT59u3T5kyBD84x//aNb712tRMVy/fj1++eUXaykEgC5dumDp0qWcfEKSYTGkxlTpywAAo6Y+j87JvW+4/7ZcoLAGGP38fxDnaVnTsKXDaEQE9O7d8Pvu6NGj2Lp1Kzw8PK7aNyMjA8OHD8fQoUPRrVs3jBgxAsOHD8f48ePh6+vb6P41NTXo16+fdZufnx86d+7c7Jxff/013nnnHWRkZFjLaXPujPb4449j3LhxOHToEIYPH46xY8diwIABTX79mTNncM899zTYdtNNNzUohnq9HvPmzcNPP/2EnJwcGI1GVFVVITMzs8Hr6stpS7SoGJrN5kbH3JVKJcxmLg5L7a+suhYZBZa77rAYUmP8w6KaNNzWWVmEXRmFKJF5ITyed0Ehai1394bXfOv1eowZMwaLFi26at/Q0FDI5XJs2rQJu3btwsaNG/Huu+/i+eefx969exETE9OiDIIgXDUUXVtba/3z7t27MWHCBMyfPx8jRoyAt7c3Vq5ciTfffLPJ7zFy5EhcvHgRP//8MzZt2oShQ4di2rRpeOONNyCTWe4n8scMf3z/pvrnP/+JTZs24Y033kDHjh2h1Woxfvx41NTUNNjvyq95c7TozidDhgzB3//+d2RnZ1u3Xb58GU8++aT1VC5Rezp2qRSiCET4aeHvoZY6DjmwmLqJS5eKq1Br4n90iWytV69eOHnyJKKjo9GxY8cGH/WFRhAEDBw4EPPnz8fhw4ehUqmwevXqq44VFxcHpVKJvXv3WrcVFxfj7NmzDfYLDAxETk6O9XFaWhoq/zCbe9euXYiKisLzzz+PPn36ID4+HhcvXmz25xYYGIhJkybhiy++wJIlS6y38K2fe/HHDFeuh9i5c2fs37+/wbYrH+/cuROTJ0/GPffcg27duiEkJAQXLlxods7raVExfO+991BWVobo6GjExcUhLi4OMTExKCsrw7vvvmvTgERNceRSMQCgR8TVQw1EzeHvroKHWgGTWURWcdMvHCeippk2bRqKiorw4IMPYv/+/cjIyMCGDRswZcoUmEwm7N27F6+++ioOHDiAzMxMrFq1CgUFBQ0uX6vn4eGBhx9+GE899RS2bNmCEydOYPLkydYzdPWGDBmC9957D4cPH8aBAwfwf//3fw1GPuPj45GZmYmVK1ciIyMD77zzTqNF9HrmzJmD77//Hunp6Th58iR+/PFHa+aOHTsiIiIC8+bNQ1paGn766aerzkbOmDEDP//8MxYvXoy0tDR8+OGHWLduXYM1VePj47Fq1SocOXIER48exZ///Gebj9S2qBhGRETg0KFD+OmnnzBz5kzMnDkTP//8Mw4dOtSiCz6JWovXF5KtCIJgPWt4XlchcRoi5xMWFoadO3fCZDJh+PDh6NatG2bOnAkfHx/IZDJ4eXlhx44duPPOO9GpUye88MILePPNNzFy5MhGj/f6669j0KBBGDNmDFJSUnDLLbdcdV3jm2++iYiICAwaNAh//vOf8c9//hNubm7W5++66y48+eSTmD59Onr06IFdu3Y1+05uKpUKs2fPRnJyMm699VbI5XKsXLkSgOVSu6+++gqnT59GcnIyFi1ahJdffrnB6wcOHIhly5Zh8eLF6N69O9avX48nn3yywa2GFy9eDF9fXwwYMABjxozBiBEj0KtXr2blvJFmXWO4ZcsWTJ8+HXv27IGXlxeGDRuGYcOGAQBKS0uRlJSEZcuWYdCgQTYNSXQ9oij+oRhy7TlqvegANxy/XIoLhRW8wwLZvbzMDLt9j23btjW6vf7MV2MSExOxfv36ax7zyjX5PDw88Pnnn+Pzzz+3bnvqqaca7BMWFoYNGzY02Fa/NmC91157rcHsaQCYOXOm9c/z5s3DvHnzrpnrhRdeaDCz+UoDBw7EsWPHGmy78ufLo48+ikcffbTB444dO1ofR0dHY8uWLQ1eM23atAaPWzu03KxiuGTJEjz66KONztLx9vbG1KlTsXjxYhZDaldZxVXQ6WugkAlclJhsIsLXDXKZgPJqIworam78AiIJBAQEQOvmhhWLnrrxzjagdXOzLkBNbeONN97AsGHD4O7ujnXr1uHTTz/F+++/364ZmlUMjx492ugsonrDhw/HG2+80epQRM1xNKsEAJAY6gWNUi5tGHIKSrkMEb5aXCisxAVdBUKkDkTUiMjISJxOTXW5eyU7s3379uG1115DeXk5YmNj8c477+CRRx5p1wzNKoZ5eXnXvTWMQqFAQUFBq0MRNceRzBIAvL6QbCs6wB0XCitxvrACIU1fyoyoXUVGRrKsOZFvvvlG6gjNm3zSoUMHnDhx4prPHzt2DKGhoa0ORdQcnHhCbSHG3zIBJae0GjUmicMQEbWTZhXDO++8Ey+++CKqq6uveq6qqgpz587F6NGjbRaO6EZqTWYcv1wKAOgR6SNtGHIqXlol/N1VEEUgr7pFCzgQETmcZg0lv/DCC1i1ahU6deqE6dOnW285c/r0aSxduhQmkwnPP/98mwQlasyZ3HIYjGZ4aRTWMzxEthId4I7CihrkVAk33pmIyAk0qxgGBwdj165dePzxxzF79mzrNGtBEDBixAgsXboUwcHBbRKUqDGH64aRu0f4QCbjL2+yrRh/dxy8WGw5YyjwrCEROb9m3ys5KioKP//8M4qLi5Geng5RFBEfH9/oza2J2lr9xJOevL6Q2kCotwZqhQwGoxnqsE5SxyEianPNLob1fH190bdvX1tmIWq2w/W3wuP1hdQGZDIBUX5uOJuvhzaWP++IyPlxbIQcVkllDc4VWG5Z1pP3SKY2El13ezxtHIshETk/FkNyWPXXF8YGuMPXXSVtGHJaUf5uAESogmOhq+S6NUTk3FgMyWEdvshhZGp7bioF/FSWiXaHcgwSpyEialsshuSw6s8Y9orkMDK1rRCtGQBwMOfqNVyJiJwJiyE5JLNZtM5IZjGkthaqtZwxPJZXg+paDicTkfNiMSSHlJavR7nBCDeVHJ2CPaSOQ07OWynCWF4Ig0nEvvNFUschImozLV6uhkgKmZmZ0Ol02HSuEgAQ6yPHsaNHmnWM1NTUNkhGzkwQgKpzB+DZfQS2nsnHrZ0CpY5ERNQmWAzJYWRmZiIhMRFVlZXwH/kEPJKHY+f3X6D3Pz5r0fH0er2NE5Izq8qwFMNtZwowd4zUaYiI2gaLITkMnU6HqspKTHjmdRxVdkZ5LXDHmHsQdv/YZh0ndd92rPv0bVRXcyIBNV31xSNQyIDzugqc11UgJoD35iYi58NiSA7HNzwO5VmWy2O7JnaCm6p5/4zzMjPaIhY5ObGmCokBKhzPr8HW0/mIuSVG6khERDbHySfkcIoMAgDAW6tsdikkao3eoWoAwNYz+RInISJqGyyG5HCKaizFMNRbI3EScjW9Qi3/5vaeK0JljVHiNEREtsdiSA6nyGD5ZxvCYkjtrIOnHBF+WtSYzNiVXih1HCIim2MxJAcjWIeSQ71YDKl9CYKA2zsHAeBwMhE5JxZDcigK/w6oFQUoZAICPNRSxyEXZC2Gp/MhiqLEaYiIbIvFkByKOiwRABDspYFMJkichlzRzbH+UCtkyC6txtk8roVJRM6FxZAciiaiCwBOPCHpaFVyDIjzB8DhZCJyPiyG5FDU4UkAgA4+WomTkCu7PeH34WQiImfCYkgOo6jKBKVvGAARoT48Y0jSGdzJUgwPXCxGWXWtxGmIiGyHxZAcxmldDQDAWylCrZBLnIZcWaS/G+IC3WEyi/j1rE7qOERENsNiSA7jVIGlGAaoOROUpMdla4jIGfF+YuQwUuvOGAZozBInIVeVmppq/XOE3AAA+OVkNg7EGiETmjZLPiAgAJGRkW2Sj4iotVgMySGUVdfiQonlFmQ8Y0jtrayoAAAwceLE3zfKFIh44kuUwA0DR92PmryMJh1L6+aG06mpLIdEZJdYDMkhHLxYDBFAbVE2NJEBUschF1OlLwMAjJr6PDon97Zu312gQHYVMPTvbyLR+8ZnsvMyM7Bi0VPQ6XQshkRkl1gMySHsP18EADBknQR63CZxGnJV/mFRCI9Psj5OdCtF9ul8FIkeCI+PkDAZEZFtcPIJOYT9FyzFsDrrpMRJiH4X7e8OAMgtq0ZljVHiNERErcdiSHavutaEo5dKAQCGrFMSpyH6nYdGgQAPFQAgs7BS4jRERK3HYkh271hWKWpMZvhoZDAWZ0sdh6iBmADLWcPzhRUSJyEiaj0WQ7J79cPIiQEqiZMQXa1+OPliYSXMImfME5FjYzEku7evbuJJFxZDskMhXhqoFTIYjGbkllZLHYeIqFVYDMmumcwiDl0sBgAkBrIYkv2RyQRE+bsBAC5wOJmIHByLIdm1U9llKDcY4alWIMqbqyuRfaofTr6g4wQUInJsLIZk13Zm6AAA/WL9IJc17ZZjRO2t/oxhgd4AfTWXrSEix8ViSHZtZ7qlGA7syLudkP1yUykQ4qUBAFwo4nAyETkuFkOyWwajyTojmcWQ7F10/XWGOhZDInJcLIZktw5dLEF1rRmBnmrEB3lIHYfouqLr1jO8VFQFk5nL1hCRY2IxJLtlHUaO84cg8PpCsm9Bnmq4qeSoMZmRXVIldRwiohZhMSS7VT/xZACHkckBCAKXrSEix8diSHaprLoWRy+VAOD1heQ4YrhsDRE5OBZDskt7zxXBLFruQ9vBRyt1HKImifRzgyAARZU1KK2qlToOEVGzsRiSXaq/vnBAnL/ESYiaTq2UI8zb8h8ZDicTkSNiMSS7xPULyVFx2RoicmQshmR38suqkZavhyAA/WN5xpAcS/2yNVnFVTCazBKnISJqHhZDsjv1s5GTwrzg666SOA1R8/i7q+ChVsBoFnGpmMvWEJFjcahi+K9//QuCIGDmzJnWbdXV1Zg2bRr8/f3h4eGBcePGIS8vT7qQ1Go70wsBAAPjOIxMjkcQBMTWnTU8p9NLnIaIqHkcphju378fH374IZKTkxtsf/LJJ/HDDz/g22+/xfbt25GdnY17771XopTUWqIo/j7xhNcXkoOKDbQUw/O6Cogi74JCRI7DIYqhXq/HhAkT8PHHH8PX19e6vbS0FP/5z3+wePFiDBkyBL1798Ynn3yCXbt2Yc+ePRImppY6k1eOnNJqqBUy9IvxkzoOUYt08NVCKRdQYTAhv9wgdRwioiZziGI4bdo0jBo1CikpKQ22Hzx4ELW1tQ22JyQkIDIyErt3777m8QwGA8rKyhp8kH3YeroAgGWZGo1SLnEaopZRyGSI8qsbTi7g7GQichx2XwxXrlyJQ4cOYeHChVc9l5ubC5VKBR8fnwbbg4ODkZube81jLly4EN7e3taPiIgIW8emFtp2Jh8AMLhzkMRJiFqnfjiZ1xkSkSOx62J46dIl/P3vf8eKFSug0WhsdtzZs2ejtLTU+nHp0iWbHZtarqy6FgcuFgMAbmcxJAcX7e8OAYBOX4My3gWFiByEXRfDgwcPIj8/H7169YJCoYBCocD27dvxzjvvQKFQIDg4GDU1NSgpKWnwury8PISEhFzzuGq1Gl5eXg0+SHo703QwmUXEBrojsm6RYCJHpVXJEepj+Q/teS52TUQOwq6L4dChQ3H8+HEcOXLE+tGnTx9MmDDB+melUonNmzdbX3PmzBlkZmaif//+EianlthaP4zciWcLyTnEBngAAM6xGBKRg1BIHeB6PD090bVr1wbb3N3d4e/vb93+8MMPY9asWfDz84OXlxdmzJiB/v374+abb5YiMrWQ2SxiS93Ek9sTAiVOQ2QbsYHu+C1dh6ziShiMJqnjEBHdkF0Xw6Z46623IJPJMG7cOBgMBowYMQLvv/++1LGomY5klUCnN8BTrUC/GN4Gj5yDr5sKvm5KFFfWIrOwElqpAxER3YDDFcNt27Y1eKzRaLB06VIsXbpUmkBkE5tOWe5WMzghCCqFXV/hQNQssQEeOJhZjHO6CiTxDo9EZOf4G5jsQn0xTEnk9YXkXGICfr8Lipk3QSEiO8diSJI7r6tAer4eCpnA9QvJ6YT6aKBVymEwmlFgEKSOQ0R0XSyGJLlf6s4W9ov1g7dWKXEaItuSCQLi6ha7zq7kj1wism/8KUWSW3/ScpeaYYnBEichahtxQZZlayzFkGcNich+sRiSpHJLq3Gw7m4nd3QNlTgNUduI8HWDSi5DtVmAukOC1HGIiK6JxZAktaHubGHvKF+EeNvutodE9kQuExBTN5zs1mmAxGmIiK6NxZAk9fPxHADAyK7XvoUhkTPoGGgZTnbr1B+iyOnJRGSfWAxJMgXlBuy7UAQAGNmNw8jk3KL83SAXRCh8QnC+xCh1HCKiRrEYkmTWn8yFKALdI3zQwYf3hCDnppTLEKyxnCnck1UtcRoiosaxGJJkfjiSDQAY1Y3DyOQaOriZAQB7LrMYEpF9YjEkSVwuqcK+C0UQBGBM9zCp4xC1i1CtGaKpFlllRqTnl0sdh4joKiyGJIkfjlrOFvaL8UOoN4eRyTUoZUD1haMAgPUnciVOQ0R0NRZDksT3dcPId/foIHESovZVeXYXAOCn4yyGRGR/WAyp3aXllSM1pwxKucBlasjlVJ7dBbkApOaUcTiZiOwOiyG1u/8dugwAuK1TEHzcVBKnIWpf5mo9eoSoAQBrj+ZInIaIqCEWQ2pXJrOI1YezAADje4dLnIZIGrdEWq6r/eFoNhe7JiK7wmJI7WpHWgHyygzwdVNiSEKQ1HGIJHFTmBpqhQzndRU4mV0mdRwiIisWQ2pX3x20nC28u0cHqBT850euSauUISUxGMDvM/SJiOwBfzNTuymprMGmk3kAgD/14TAyubYx3S23gfzhaDbMZg4nE5F9YDGkdrPq0GXUmMxIDPVCUpi31HGIJDW4cxA81Apkl1bjUGax1HGIiACwGFI7EUURX+7LBAD8uV+kxGmIpKdRyjE8yTKcvJbDyURkJ1gMqV3sv1CM9Hw93FRyjO3BW+ARAb/fDvLn4zkwmswSpyEiYjGkdvLl3osAgLu6h8FTo5Q4DZF9uKVjAHzdlNDpa7Aro1DqOEREUEgdgJxfod6An+vuC9vLuxKHDh1q0XFSU1NtGYtIckq5DHd2C8WKvZlYdSgLt3YKlDoSEbk4FkNqc1/syUSN0YzavHTcP2x0q4+n1+ttkIrIPozvHY4VezOx/mQuyqpr4cUz6kQkIRZDalMGowmf77EMI5fsXYUJz7yO4Mi4Fh0rdd92rPv0bVRXV9syIpGkekT4oGOQB9Lz9fjpWA4evImTs4hIOiyG1KbWHsmGTm+Av1aGi2d2IjhyJsLjk1p0rLzMDBunI5KeIAgY3zsc/1p3Gt8dzGIxJCJJcfIJtRlRFPGf384DAEZ2dAfMJokTEdmne3t2gEwADl4sxrkCXipBRNJhMaQ2szujEKdzy6FVyjE8zk3qOER2K8hLg9vqJp7U3zaSiEgKLIbUZurPFo7vHQ4PFf+pEV3Pn/pEALDcIcjEW+QRkUR4jSHdUGZmJnQ6XbNek11uxObTBQCAfr6VSE1Na4toRE5jaGIQfNyUyC2rxm/pOusZRCKi9sRiSNeVmZmJhMREVFVWNut1fsP+D569RqMybS/GLHrJup1LzRA1Tq2Q4+7uYfh090V8dzCLxZCIJMFiSNel0+lQVVnZrGVmqkzA+stKmAHcMbAXAoeu4lIzRE0wvncEPt19ERtO5qKksgY+biqpIxGRi2ExpCYJjoxr8jIzO84WwIwShHpr0KNrRwiCwKVmiJqgawcvJIZ6ITWnDN8dzMIjg2KljkRELoYzAsimKgxGHL9cCgDoF+MHQRAkTkTkOARBwEM3RwEAvthzEWZOQiGidsZiSDZ1KLMYRrOIEC8NIv24RA1Rc93dIwyeagUuFFbit/TmTfoiImotFkOymcoaI45lWc4W3sSzhUQt4q5WYFzvcACw3k6SiKi9sBiSzRzOLIHRLCLIU41of54tJGqpiXXDyZtT83C5pEriNETkSlgMySaqak04mlUCgNcWErVWxyAPDIjzh1kEvtzLs4ZE1H5YDMkmDl0sRq1JRKCHGjEB7lLHIXJ4f+lvOWv49f5LMBh5n3Eiah8shtRqeoMRRy6VAABujuXZQiJbSEkMRoiXBjp9DdafyJU6DhG5CBZDarW95wphNIsI9dbwbCGRjSjkMvy5XyQA4NNdF6QNQ0QugwtcU6sUVdTgZE4ZAOCWjgE8W0jUBKmpqU3aL0ljgkIGHMoswYqNe5AY8PudUAICAhAZGdlWEYnIRbEYUqvsytBBFIHYAHeE+WiljkNk18qKCgAAEydObPJr/O6YAc/uI/Dkhz+hYNXL1u1aNzecTk1lOSQim2IxpBbLKa1CRkEFBAAD4vyljkNk96r0lrPro6Y+j87JvZv0mvJaYGOOCLf4m/HIklXwUgJ5mRlYsegp6HQ6FkMisikWQ2oRURSxM70QAJAY6gV/D7XEiYgch39YVJPvPQ4AGcZsZBRU4DIC0CU+uA2TEZGr4+QTapHzugpcLqmCXCbg5lg/qeMQObXeUb4AgNO5ZdAbjBKnISJnxmJIzWYyi/g1zXIP1x4RPvDUKCVOROTcQr21CPPRwCzCujQUEVFbYDGkZjuaVYKSqlq4qeToG+0rdRwil1B/1vB4VilqzRKHISKnxWJIzVJZY8Te80UAgP5x/lAr5BInInINMf7u8HNXocZkxrly/ugmorbBny7ULHvOFaHGaEaghxpdQr2kjkPkMgRBQJ+6s4Zny+UQVFweiohsj8WQmkynN+DE5VIAwK2dAiDjYtZE7apzsCd83JSoMQvw6nO31HGIyAmxGFKTiCKwI60AIoCOgR4I93WTOhKRy5HJBNwcY1kz1KvvWOhreLEhEdkWiyE1yeUqAZeKLMvT3BIfIHUcIpfVKdgDXkozZBoPrD1TIXUcInIyLIZ0Q4JSg2PFlrXQe0f5wlvL5WmIpCIIArp4mwAAP6ZVoFBvkDgRETkTFkO6Ie8BD6DKJMBLo0DfKC5PQyS1MK0IQ04aqo0iPtxxTuo4ROREWAzpui6V1sKr71gAwG2dA6GQ858MkdQEASj59XMAwKe7LiCvrFriRETkLPhbnq5JFEV8fLgMglyBUK0ZsQEeUkciojrV5w8hIUAJg9GM1zeckToOETkJFkO6prVHs3EivwbmWgO6+/L+rET2Zkp3y1qi3x3MwrGsEmnDEJFTYDGkRpVX1+KVn1IBAGW7v4G7QuJARHSVeH8V7u3ZAQCw4IdTEEVR4kRE5OhYDKlRS35JQ365AaEecpTuWyV1HCK6hqfvSIBWKceBi8X44ViO1HGIyMGxGNJVTueWYfmuCwCAR3p5A6ZaaQMR0TWFeGvw+OA4AMC/fk5FVY1J4kRE5MhYDKkBs1nE7FXHYTKLuCMpBD1D1FJHIqIbeOzWWHTw0SK7tBofcfkaImoFFkNqYMXeizicWQJ3lRxz7+oidRwiagKNUo5nRyYAAD7Yno7MwkqJExGRo2IxJKvc0mosWm9Z9uLpOxIQ6q2VOBERNdXo5FD0j/VHda0Zz685zokoRNQidl0MFy5ciL59+8LT0xNBQUEYO3YszpxpuF5XdXU1pk2bBn9/f3h4eGDcuHHIy8uTKLFjm/P9CegNRvSM9MHEm6OkjkNEzSAIAl69txvUChl+TdNh1aHLUkciIgdk18Vw+/btmDZtGvbs2YNNmzahtrYWw4cPR0XF7zeOf/LJJ/HDDz/g22+/xfbt25GdnY17771XwtSOaf2JXGw8lQeFTMDCe7tBLhOkjkREzRQT4I6ZKZ0AAC/9dAo63keZiJrJrlenW79+fYPHy5cvR1BQEA4ePIhbb70VpaWl+M9//oMvv/wSQ4YMAQB88sknSExMxJ49e3DzzTdLEdvhlFXXYu7aEwCAqbfFIiHES+JERNRSjwyKwQ9Hs3EqpwzzfziFdx/sKXUkInIgdn3G8EqlpaUAAD8/PwDAwYMHUVtbi5SUFOs+CQkJiIyMxO7du695HIPBgLKysgYfruz19WeQV2ZAtL8bZgyJlzoOEbWCUi7DonHJkAnAD0ezsTmVl9YQUdM5TDE0m82YOXMmBg4ciK5duwIAcnNzoVKp4OPj02Df4OBg5ObmXvNYCxcuhLe3t/UjIiKiLaPbtYMXi/DF3osAgFfv6QaNUi5xIiJqrW7h3nhkUCwAYPaq4yiqqJE4ERE5CocphtOmTcOJEyewcuXKVh9r9uzZKC0ttX5cunTJBgkdT43RjGf/dxyiCIzvHY4BHQOkjkRENvJkSifEBbojv9yAp787xlnKRNQkDlEMp0+fjh9//BFbt25FeHi4dXtISAhqampQUlLSYP+8vDyEhIRc83hqtRpeXl4NPlzRe1vSkJavh7+7Cs/fmSh1HCKyIa1Kjrcf6AmVXIZfUvOwYm+m1JGIyAHY9eQTURQxY8YMrF69Gtu2bUNMTEyD53v37g2lUonNmzdj3LhxAIAzZ84gMzMT/fv3lyKywzhxuRRLt2UAABbc3RW+7iqJExFRc6Wmpt5wnz93dcfyo+VY8MMJeFXnItxLaX0uICAAkZGRbRmRiByMXRfDadOm4csvv8T3338PT09P63WD3t7e0Gq18Pb2xsMPP4xZs2bBz88PXl5emDFjBvr3788ZyddRYzTjn98ehcks4s5uIRiVHCp1JCJqhrKiAgDAxIkTm7C3gKD75gMxvfB/n+5DzuezAJMRAKB1c8Pp1FSWQyKysuti+MEHHwAABg8e3GD7J598gsmTJwMA3nrrLchkMowbNw4GgwEjRozA+++/385JHcvSrek4nVsOXzclFtzdVeo4RNRMVXrLSgqjpj6Pzsm9b7y/CfglRwSCY3H7vP+hp58JeZkZWLHoKeh0OhZDIrKy62LYlIulNRoNli5diqVLl7ZDIsd3KrsMS7emA7AMIQd4qCVOREQt5R8WhfD4pCbtqwiowNqj2TinlyM2IhTB7IJE1AiHmHxCtlFrsgwhG80i7kgKwWgOIRO5jJgAd9wcY1kDduvpAhQZeHcjIroai6EL+WBbBk7llMHHTYmXxnaFIPAXA5EruSnGD3GB7jCJIvboFJC5+0gdiYjsDIuhi0jNKcO7W9IAAPPvSkKgJ4eQiVyNIAgY1iUYvm5KVJkEBN49G7Umrm9IRL9jMXQBNUYz/vHNUdSaRAzrEoy7uodJHYmIJKJWyDEmOQwKQYQmIglL95fAbGY5JCILu558Qq2XmZmJJVvO4VROBTxVAu6PE3H48OEmv74p66QRkWPxdVfh5gAjfs0VsCOzGos2nMbskVzknohYDJ1aZmYmug29Bz73zoMgyJDx9csY9tLuFh1Lr9fbNhwRSSpYK6Jw/bsIGDULH24/hxAvDaYMjLnxC4nIqbEYOrHMnHx4pkyDIMgQ5W7CuL8/1exjpO7bjnWfvo3q6uo2SEhEUqo4sQVPPDMXX54ox4IfTyHIU8MF74lcHIuhE/vP4TIovIPhJhcxsk881Ap5s4+Rl5nRBsmIyF6MS3SHzMMXX+zJxMyvD0OjlGFoYrDUsYhIIpx84qTWn8jB1gtVEEUz+vobW1QKicj5CYKA+Xd1xajkUNSaRDz+xSFsPZ0vdSwikgiLoRPKL6vG7FXHAQBle/6HAA1nHBLRtcllApbc3wMju4agxmTG1C8OYvvZAqljEZEEWAydjCiKeOq7YyiurEWMjwIlv62QOhIROQClXIZ3HuyJ4V2CUWM047HPDmAHyyGRy2ExdDKf7LyA7WcLoFLIMLOfD2A2Sh2JiByEUi7De3/uhZTEYBiMZjz86X78fDxH6lhE1I5YDJ3I8axSLFxnWXfw+TsTEeGtlDgRETkalUKGpRN64s5uIag1iZj+5SGs3JcpdSwiaicshk6ivLoW0786hFqTiBFJwfhL/yipIxGRg1Ir5Hj3wV54oG8EzCLw7KrjWLadKxQQuQIWQycgiiKeW30CFwsr0cFHi9fGdYcgCFLHIiIHJpcJWHhvN/zfbXEAgH+tO415a0/CxNvnETk1FkMn8PX+S/jhaDbkMgHvPNgD3m4cQiai1hMEAc+OTMDskQkAgOW7LmDq5wdQYeC1y0TOisXQwZ3NK8e8H04CAP45vDN6R/lJnIiInM3U2+Lw3p97QqWQ4ZfUfNz/0W7klfFuSETOiMXQgVXVmDBtxSFU15pxa6dATL01VupIROSkRieH4atHb4afuwonLpdh7NKdSM0pkzoWEdkYi6EDm7f2JNLy9Qj0VGPxfd0hk/G6QiJqO72jfLHmbwMRF+iOnNJqjP9gF7ad4V1SiJwJ75VspzIzM6HT6a75/KaMSnx9sBQCgGm93JF59iSuXFAiNTW1TTMSkeuJ9HfDqscH4v++OIjd5wrx1+X7MeOWUNwW2vpjBwQEIDIysvUHIqIWYzG0Q5mZmUhITERVZWWjz6tCOyHkz4sgKJQo2v4ppiz69rrH0+v1bRGTiFyUt5sSn/71Jjy3+ji+O5iFt3/NwUsHf0Tx5o8A0dzi42rd3HA6NZXlkEhCLIZ2SKfToaqyEhOeeR3BkXENnqs2AVtylagyCQjTmnHvxAchPPRgo8dJ3bcd6z59G9XVvEiciGxLpZDh9fHJ0NaW4rOjpfDqPRodB96JmwOMULbgIqW8zAysWPQUdDodiyGRhFgM7VhwZBzC45Osj81mEasPX0aVqQq+bkrc1TcCaoX8mq/Py+SCtETUdgRBwD0JHnhz3rMIHfcc8qtl+K3IA3f1CIO3lstmETkiFkMHsjNDh6ySKijlAkYnh123FBIRNUVrr0VOTU1FVdpuDA42Ym+xFkWVNVi5PxOju4Whg6/WRimJqL2wGDqIs3nlOJRZAgAY3iUEfu4qaQMRkUMrKyoAAEycONEmx1PUlOP+vp3ww9Fs5JcbsOpwFlISg5EY6mWT4xNR+2AxdAA6vQGbTuUBAPpE+aJjkIfEiYjI0VXpLWsQjpr6PDon927xcf54LbOHWoHxvcOx8WQe0gv02HgqD0UVNRgQ58/bdBI5CBZDO1dhMGLt0WwYzSIi/LToH+cvdSQiciL+YVENrmVuriuvZVbKZbizWwh2nyvE/gvFOHCxGMWVNRiRFAKlnEvnEtk7fpfaMZMZ+PFYDsqrjfBxU+LOrqGQ8X/dRGTnBEHAgLgADO8SDLkgIKOgAt8dzIK+mvdYJrJ3LIZ2S8D+Ijlyy6qhUchwV/cwaJScbEJEjiMx1Av39OoArVKO/HIDVh7IhE5vkDoWEV0Hi6Gd8hk0AZcr5ZAJwKjkUPi6cbIJETmeDj5a3N83An5uKlQYTPj2QBayihtfvJ+IpMdiaIe2XqiE94AHAABDE4MR7usmcSIiopbz1irxpz7hCPPRoMZkxprD2UjLK5c6FhE1gsXQzuw9V4gPDpQCADp7mdCFSz0QkRPQKOW4p0cHxAW6wySK+PlELo5cKpE6FhFdgcXQjqTnl2PqFwdhNAMVp39DkrdJ6khERDajkMtwZ7dQJHfwBgBsP1uAnek6iKIocTIiqsdiaCeyS6rwl//sQ0llLeL9lCj86S1wAjIRORuZIGBw50D0j7UsvXXgYjE2pebBzG5IZBe4jqGNZWZmQqfTNes15QYznt9aiOwyIzp4yvGnYB1+MXLmHhE5J0EQcFOMH9zVcmw+nY/UnHKUaBUQFJxkRyQ1FkMbyszMREJiIqoqmz7jTlCqEfzAK1CHJcBYrsPe95/CrnLLrar0en1bRSUiklxSmDe0Sjl+PpGLnCoZgu5bgIoas9SxiFwai6EN6XQ6VFVWYsIzryM4Mu6G+5tFYFeBAnnVMihlIoZ18oLXvz5scIspIiJnFhvogXt6dMCaw5egieiKF7cV4pukagR5aqSORuSSWAzbQHBk3A1vMSWKIjaczENedTkUMgH39ApHqLcWwNW3mCIicmYdfLW4LdiIjel6XIAv/rRsNz7/az9E+nOpLqL2xsknEhBFEdvPFuBMXrllAetuodZSSETkinxUInJXPIVgdzkuFlZi3LJdSM0pkzoWkcthMWxnoiji1zQdjmZZ1ioc1iUY0QHuEqciIpKesSQXrwzxR0KIJwrKDbjvw93Yf6FI6lhELoXFsB2JooidGYU4XLeo69CEICSEcAFrIqJ6flo5vp7aH32jfVFebcTEf+/FltN5Uscichkshu1oz7kiHLxYDAC4vXMgutYt8kpERL/z1irx2V/7YUhCEAxGMx797CBWHcqSOhaRS+Dkk3ay91wh9tUNidzWKRDJ4T7SBiIiskOpqanWP/9fVxlMVVpsv1iFWd8cxfGz5zGm0/UvvQkICEBkZGRbxyRyWiyG7WD/hSLsOW8phYM6BqBHhI+0gYiI7ExZkWX91okTJ17xjADfIQ/Dq+9YfHKkDEve/xglv35+zeNo3dxwOjWV5ZCohVgM25Aoith3oQh7zllK4YA4f/SK8pU4FRGR/anSW2Ygj5r6PDon927wnCgCZ8qMOFmqgPeA+9Fj+Hj09DVdddvQvMwMrFj0FHQ6HYshUQuxGLYRURSxM70QBzMt1xT2j/VH32g/iVMREdk3/7CoRteBjQAQfLkUW0/n47xeDrmbN0YkBUMh46XyRLbEYtgGRBHYeqYAxy9blqQZFB+AXpE8U0hE1BrdOnhDo5Rhw4k8pOfrYag1YXRyGFQKlkMiW+F3k60JMhwokltL4dCEIJZCIiIbiQ/yxF09wqCUC7hUXIX/HcpCZY1R6lhEToPF0IZqTSIC7n4GmRVyCAIwIimYS9IQEdlYpJ8bxvUKh1YpR365Ad8dzEJZVa3UsYicAouhjVTXmrBoZzHcOw+EDCJGdQvl4tVERG0k2EuD8b3D4aFWoLiyFiv3X4LOINz4hUR0XSyGNmIyi9DXmmGurcaAQCPiAj2kjkRE5NT83FW4r084Aj3UqKo1YUeeAu5dh0odi8ihsRjaiLtagRcG+SHvq+cQrBWljkNE5BI8NUr8qU844gLdIUJAwKgn8enRMhhNZqmjETkkFkMb8lDJUJNzVuoYREQuRSmXWS7f8TIBAL4/U4GH/rMP+eXVEicjcjwshkRE5PAEQUCSjwkF3y+CRiFg97lCjH7nN+w9Vyh1NCKHwmJIREROo/L0r3gtxR/xQR7ILzfgz//ei/e3pcNk5iU+RE3BYkhERE4l3EuJ76cPxD09O8BkFvHa+jN44KPduFhYIXU0IrvHYkhERE7HTaXA4vu647VxyXBXybH/QjFGvv0rVuy9CFHk2UOia2ExJCIipyQIAu7rG4H1M29Fvxg/VNaY8PzqE/jzx3uRllcudTwiu8RiSERETi3Czw1fPXozXhiVCLVCht3nCjHy7V+x8OdUVBh4Oz2iP2IxJCIipyeTCXhkUCx+mXUbUhKDYTSL+HDHOQx9czu+3p+JWq57SASAxZCIiFxIhJ8b/j2pD/4zqQ8i/dyQW1aNZ/53HMMWb8eaw5c5e5lcnkLqAERERLaUmpp6w318Abx2uxc2pMvxv9MVuFBYiZlfH8Eb607grk7uuKdXOOJjo9s8K5G9YTEkIiKnUFZUAACYOHFis14nKDXw7D0GXv3GIQseeP9AKd7dfhFfPVKLW7rHt0VUIrvFYkhERE6hSl8GABg19Xl0Tu7d7NfXmoHzeiPOlIioqK2GurbM1hGJ7B6LIRERORX/sCiExye16LUxADqePYn3PpoD+WM/2DYYkQNwmsknS5cuRXR0NDQaDfr164d9+/ZJHYmIiByQTACMxdlSxyCShFMUw6+//hqzZs3C3LlzcejQIXTv3h0jRoxAfn6+1NGIiIiIHIZTFMPFixfj0UcfxZQpU9ClSxcsW7YMbm5u+O9//yt1NCIiIiKH4fDXGNbU1ODgwYOYPXu2dZtMJkNKSgp2797d6GsMBgMMBoP1cWlpKQCgrKx1Fxrr9XoAQFbaSRiqKlt8nLzMDABA7oWzyHB3k/Q4zNK2x7GnLLY6jj1lsdVx7CmLrY5jT1lsdRxbZSnIOg/A8jO9tb8X6nl6ekIQBJsci6gtCaKD3008OzsbHTp0wK5du9C/f3/r9qeffhrbt2/H3r17r3rNvHnzMH/+/PaMSURELqy0tBReXl5SxyC6IYc/Y9gSs2fPxqxZs6yPzWYzioqK4O/v36L/0ZWVlSEiIgKXLl1y+W98fi1+x69FQ/x6/I5fi9+5ytfC09NT6ghETeLwxTAgIAByuRx5eXkNtufl5SEkJKTR16jVaqjV6gbbfHx8Wp3Fy8vLqX+wNQe/Fr/j16Ihfj1+x6/F7/i1ILIPDj/5RKVSoXfv3ti8ebN1m9lsxubNmxsMLRMRERHR9Tn8GUMAmDVrFiZNmoQ+ffrgpptuwpIlS1BRUYEpU6ZIHY2IiIjIYThFMbz//vtRUFCAOXPmIDc3Fz169MD69esRHBzcLu+vVqsxd+7cq4anXRG/Fr/j16Ihfj1+x6/F7/i1ILIvDj8rmYiIiIhsw+GvMSQiIiIi22AxJCIiIiIALIZEREREVIfFkIiIiIgAsBjaxNKlSxEdHQ2NRoN+/fph3759Ukdqdzt27MCYMWMQFhYGQRCwZs0aqSNJZuHChejbty88PT0RFBSEsWPH4syZM1LHksQHH3yA5ORk6+LF/fv3x7p166SOZRf+9a9/QRAEzJw5U+ookpg3bx4EQWjwkZCQIHUsIpfHYthKX3/9NWbNmoW5c+fi0KFD6N69O0aMGIH8/Hypo7WriooKdO/eHUuXLpU6iuS2b9+OadOmYc+ePdi0aRNqa2sxfPhwVFRUSB2t3YWHh+Nf//oXDh48iAMHDmDIkCG4++67cfLkSamjSWr//v348MMPkZycLHUUSSUlJSEnJ8f68dtvv0kdicjlcbmaVurXrx/69u2L9957D4DlrisRERGYMWMGnn32WYnTSUMQBKxevRpjx46VOopdKCgoQFBQELZv345bb71V6jiS8/Pzw+uvv46HH35Y6iiS0Ov16NWrF95//328/PLL6NGjB5YsWSJ1rHY3b948rFmzBkeOHJE6ChH9Ac8YtkJNTQ0OHjyIlJQU6zaZTIaUlBTs3r1bwmRkT0pLSwFYCpErM5lMWLlyJSoqKlz6dpXTpk3DqFGjGvzccFVpaWkICwtDbGwsJkyYgMzMTKkjEbk8p7jziVR0Oh1MJtNVd1gJDg7G6dOnJUpF9sRsNmPmzJkYOHAgunbtKnUcSRw/fhz9+/dHdXU1PDw8sHr1anTp0kXqWJJYuXIlDh06hP3790sdRXL9+vXD8uXL0blzZ+Tk5GD+/PkYNGgQTpw4AU9PT6njEbksFkOiNjRt2jScOHHCpa+d6ty5M44cOYLS0lJ89913mDRpErZv3+5y5fDSpUv4+9//jk2bNkGj0UgdR3IjR460/jk5ORn9+vVDVFQUvvnmG5e9zIDIHrAYtkJAQADkcjny8vIabM/Ly0NISIhEqcheTJ8+HT/++CN27NiB8PBwqeNIRqVSoWPHjgCA3r17Y//+/Xj77bfx4YcfSpysfR08eBD5+fno1auXdZvJZMKOHTvw3nvvwWAwQC6XS5hQWj4+PujUqRPS09OljkLk0niNYSuoVCr07t0bmzdvtm4zm83YvHmzS19D5epEUcT06dOxevVqbNmyBTExMVJHsitmsxkGg0HqGO1u6NChOH78OI4cOWL96NOnDyZMmIAjR464dCkELJNyMjIyEBoaKnUUIpfGM4atNGvWLEyaNAl9+vTBTTfdhCVLlqCiogJTpkyROlq70uv1Df6nf/78eRw5cgR+fn6IjIyUMFn7mzZtGr788kt8//338PT0RG5uLgDA29sbWq1W4nTta/bs2Rg5ciQiIyNRXl6OL7/8Etu2bcOGDRukjtbuPD09r7rO1N3dHf7+/i55/ek///lPjBkzBlFRUcjOzsbcuXMhl8vx4IMPSh2NyKWxGLbS/fffj4KCAsyZMwe5ubno0aMH1q9ff9WEFGd34MAB3H777dbHs2bNAgBMmjQJy5cvlyiVND744AMAwODBgxts/+STTzB58uT2DySh/Px8/OUvf0FOTg68vb2RnJyMDRs2YNiwYVJHI4llZWXhwQcfRGFhIQIDA3HLLbdgz549CAwMlDoakUvjOoZEREREBIDXGBIRERFRHRZDIiIiIgLAYkhEREREdVgMiYiIiAgAiyERERER1WExJCIiIiIALIZEREREVIfFkIiIiIgAsBgSERERUR0WQyIiIiICwGJIRERERHVYDImIiIgIAPD/VlOgN8f4aRcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 648.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(residualSugar, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Residual Sugar\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.2246670332131056\n",
      "Skewness pada distribusi = 0.1447697076471561\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['residual sugar'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['residual sugar'].mean() - data['residual sugar'].median()))/data['residual sugar'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chlorides\n",
    "Distribusi Chlorides bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Chlorides juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 612.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(chlorides, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Chlorides\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.17048274704296862\n",
      "Skewness pada distribusi = -0.14496047465064746\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['chlorides'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['chlorides'].mean() - data['chlorides'].median()))/data['chlorides'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Free Sulfur Dioxide\n",
    "Distribusi Free Sulfur Dioxide bersifat normal secara graphical. Distribusi pada histogram membentuk kurva yang cukup simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Namun, distribusi Fixed Acidity tidak bersifat normal secara analytical dengan p-value < 0.05. Distribusi ini menggambarkan skewness positif."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 675.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(freeSulfurDioxide, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Free Sulfur Dioxide\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bukan Distribusi Normal dengan p-value = 0.01743043451827735\n",
      "Skewness pada distribusi = 0.02904994822740363\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['free sulfur dioxide'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['free sulfur dioxide'].mean() - data['free sulfur dioxide'].median()))/data['free sulfur dioxide'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Sulfur Dioxide\n",
    "Distribusi Total Sulfur Dioxide bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Total Sulfur Dioxide juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 680.375x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(totalSulfurDioxide, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Total Sulfur Dioxide\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.8488846101395726\n",
      "Skewness pada distribusi = 0.030148205216689577\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['total sulfur dioxide'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['total sulfur dioxide'].mean() - data['total sulfur dioxide'].median()))/data['total sulfur dioxide'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Density\n",
    "Distribusi Density bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Density juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pz4der2/TMVFRUUhOTu6kioiI3IfBiohcJj8/Hz179UJ9XV2bjtMFBOBYVhbDFRF5PQYrInIZvV6P+ro6TH72TcQkp7fqmOL8XKxeMBt6vZ7Bioi8HoMVEblcTHI6EjP6SF0GEZHbcfE6ERERkYswWBERERG5CIMVERERkYswWBERERG5CIMVERERkYswWBERERG5CNstEFGnqzFacLyoBla7QKBaibSoQARp+fFDRL6Hn2xE1GnMVju2Hi/B8eIaCHF+uypHhhFdIzEwMUyy2oiIOgODFRF1ijqzFesOFKKkxgQASAjTITxAhZIaE0pqTPgxW4+SahP6qCUulIjIhRisiMjlLHbgy8wzqKizQKuSY1L/eCSE6QAAQggcLqzGtoaRLHmIQuJqiYhch8GKiFxuX7kCFXUWBGmUuGNwAsIDzg9LyWQy9EsIhQzA5mMlyKpWQJcxQrpiiYhciFcFEpFLBQ26EWfqFJDLgBv7xTYJVRfqmxCKQUlhAICIa6fDZBXN7kdE5E0YrIjIZcrqbAgfOw0AMLpbFOJCdZfcf2R6JHQKAWVoNNYeN7ijRCKiTiVpsPrhhx9w8803Iz4+HjKZDGvXrm3yvBACL730EuLi4qDT6TB+/HhkZ2c32ae8vByTJ09GSEgIwsLC8NBDD8Fg4Ac0kRQ+PFgNuVqHSI3dORp1KSqFHP3CbACAr44ZUFhZ38kVEhF1LkmDVW1tLQYMGIClS5c2+/wbb7yBd999F8uWLcPu3bsRGBiIiRMnwmg0OveZPHkyjhw5gk2bNuF///sffvjhB0yfPt1dp0BEDXafLMOP+UYIYcfAcBtkMlmrjksMsMNYcARmG/Dvn/I6uUoios4labC64YYb8Nprr+H222+/6DkhBBYtWoQXXngBt956K/r3748PP/wQhYWFzpGtrKwsbNy4Ef/v//0/DB8+HGPGjMGSJUvw6aeforCw0M1nQ+S/hBD46zdZAADDgY0IU7d+vZRMBlTt+gIA8NkvBagxWjqlRiIid/DYNVZ5eXkoKirC+PHjndtCQ0MxfPhw7Ny5EwCwc+dOhIWFYejQoc59xo8fD7lcjt27d7f42iaTCdXV1U0eRNR+20+U4tczVdAoZKj8aXWbjzeezERCsAI1Jis+33umEyokInIPjw1WRUVFAICYmJgm22NiYpzPFRUVITo6usnzSqUSERERzn2aM3/+fISGhjofSUlJLq6eyH8IIbB4s2Pt48T0ANjrqtrzKpjUPRAAsHxHHmx2XiFIRN7JY4NVZ5ozZw6qqqqcj4KCAqlLIvJaP2brsT+/EhqlHLf2CGz364xNCUB4gApnKurxQ3apCyskInIfjw1WsbGxAIDi4uIm24uLi53PxcbGoqSkpMnzVqsV5eXlzn2ao9FoEBIS0uRBRO3z/g8nAQC/H56McF37u6hrlDLcMiAeALBu/1mX1EZE5G4eG6zS0tIQGxuLzZs3O7dVV1dj9+7dGDlyJABg5MiRqKysRGZmpnOfLVu2wG63Y/jw4W6vmcjfHC+qwU85eshlwENj0jr8ercPTgQAfHukGLUma4dfj4jI3SS9pY3BYEBOTo7z67y8PBw4cAARERFITk7GrFmz8NprryEjIwNpaWl48cUXER8fj9tuuw0A0KtXL1x//fV45JFHsGzZMlgsFsycORP33nsv4uPjJTorIv/R2B7h+r6xSAwPQEkHuiVkZWWhpxCIC1LgnMGGf369C2NTAy55TFRUFJKTk9v/pkRELiZpsNq7dy/GjRvn/PrJJ58EADz44INYsWIFnnnmGdTW1mL69OmorKzEmDFjsHHjRmi1Wucxq1evxsyZM3HttddCLpfjzjvvxLvvvuv2cyHyN2UGE7464Jiy+8Po9o9WVZc71lNNmTIFABA66l6EXTkFCz7biqc+f+mSx+oCAnAsK4vhiog8hqTBauzYsRCi5at/ZDIZ5s2bh3nz5rW4T0REBD7++OPOKI+ILuHTXwpgttrRPzEUQ1LC2/069QZHu5ObHn0ePfoPgcEKfFsI6NIG4U/vroG2hWVbxfm5WL1gNvR6PYMVEXkMSYMVEXknu13g01/yAQAPjExtdZf1S4mMT0FiRh8AwP6afJTUmGAKSUS3+NAOvzYRkbt47OJ1IvJcP+boUVBej2CtEjf1i3P563eNcrRtOFla6/LXJiLqTAxWRNRmH+8+DQC4c3AidOr2t1hoSdcuQQCA/PI6WG12l78+EVFnYbAiojYpqTbi+yxH/7jfD++ctU1RQWoEa5Ww2gXyy+s65T2IiDoDgxURtcl/9p2FzS4wJCUc3WOCO+U9ZDLZ+elAPacDich7MFgRUasJIbBmn+Mmyb8bktip79U4HZinr73k1cNERJ6EwYqIWu3Q2SpklxigUcpxY3/XL1q/UHyYFiqFDHVmG/QGc6e+FxGRqzBYEVGr/SfTMVo1oU8sQrSqTn0vpVyOhDAdAHCdFRF5DQYrImoVs9WO9b8WAgDuHJzglvdMjnDc0obBioi8BYMVEbXKlmMlqKizIDpYgyszurjlPRuD1dnKerZdICKvwGBFRK3SuGj99kEJUMg73mm9NSIC1QjUKGCzCxRWGd3ynkREHcFgRUSXVV5rxtbjjt5Vdwzu3KsBLySTyTgdSERehcGKiC5r/YGzsNgE+iaEoEds5/SuakljsCpgsCIiL8BgRUSX9Z99ZwE4bmHjbolhjmBVWmOCyWpz+/sTEbUFgxURXVJOiQGHzlZBKZfhlgHxbn//IK0SoToVBIDCSq6zIiLPxmBFRJfU2GLhyowoRAZpJKmhsZ/V2cp6Sd6fiKi1GKyIqEVCCPy3IVjdOtA9vauakxjeEKwqGKyIyLMxWBFRiw6drUKevhZalRzX9Y6RrI7GEaviGiPMVvazIiLPxWBFRC1af8AxWjW+VwwCNUrJ6gjRqRCsVUII4FwVR62IyHMxWBFRs2x2gf8edAQrKRat/xbXWRGRN2CwIqJm7ckrR3G1CSFaJa7u4Z5b2FxKY7A6xysDiciDMVgRUbPW/+roXXVD3zholAqJqwFiQ7UAHOus7HYhcTVERM1jsCKii5itdnxzqAgAcMtA6acBASAyUA21Qg6LTaCs1ix1OUREzWKwIqKL/Jhdiqp6C7oEazCia6TU5QBw3DcwJtTRR4sL2InIUzFYEdFF1jVcDTipfxwUcpnE1ZwXF+JYZ1VUxXVWROSZGKyIqIk6sxWbjhYDkLYpaHMa11mdq2awIiLPxGBFRE1sOlqMeosNKZEBGJAYKnU5TTQGq8o6C0y8HzMReSAGKyJqovEWNrcMiIdM5jnTgACgUykQFqACAJSbPas2IiKAwYqILlBZZ8b2E6UAPKMpaHPiQhyjVuUmfnwRkefhJxMROW04XASLTaBXXAgyYoKlLqdZjdOBHLEiIk/EYEVETusOOJqCeupoFQDEhTquDCw3yQAZP8KIyLPwU4mIADhaGOzOKwcA3DwgTuJqWhYZqIZKIYNVyKCKTJS6HCKiJhisiAgA8L+DhRACGJoSjsTwAKnLaZFcLkNMsGM6UBPfU+JqiIiaYrAiIgDA+sarAT3kFjaX0rjOSh3fQ+JKiIiaYrAiIuTpa3HwTBUUchlu7Oe504CN4hqClSaBI1ZE5FkYrIgI6xtuYTO6WxSigjQSV3N5zhGrqBTUmu0SV0NEdB6DFZGfE0Jg3a+OqwFv9eCrAS8UoFYiUCkAANnlFomrISI6Tyl1AUQkrSOF1ThZWguNUo4JfWKc2/Pz86HX69v0WllZWa4ur0URajtqrQrklJvd9p5ERJfDYEXk5xpvYXNtr2gEax23i8nPz0fPXr1QX1fXrtc0GAwuq68lYWqBgjogp4IjVkTkORisiPyY3S7OXw14wTSgXq9HfV0dJj/7JmKS01v9ell7tmPDysUwGo0ur/W3wtWOqcCTDFZE5EEYrIj82N7TFThXZUSwRomxPaIvej4mOR2JGX1a/XrF+bmuLO+SwtQCQtihrwNKa0zoEuz5i+6JyPdx8TqRH2ucBpzQJxZalULiatpGJQes5Y5F94fPVklcDRGRA4MVkZ+y2uz45tA5AN7RFLQ5pnPZAICDZxisiMgzMFgR+amdJ8tQVmtGeIAKo9IjpS6nXcxFOQCAQxyxIiIPwWBF5Kf+96tjtOqGfnFQKbzzo+B8sKqUthAiogbe+WlKRB1ittqx4bAjWN3c3zunAQHAXHISchlQXG1CcXXnX4lIRHQ5DFZEfujH7FJUG62IDtbgirQIqctpN2ExIiHYcXHzIa6zIiIPwGBF5Icarwa8sV8cFHKZxNV0TLcIR1NTrrMiIk/AYEXkZ4wWGzYdLQYA3Owl9wa8lK7hDFZE5DkYrIj8zNZjJag125AQpsPg5DCpy+mwbg3B6uCZKgghJK6GiPwdgxWRn/nvQcc04KQBcZDJvHsaEABSw1RQyGXQG0wo4gJ2IpIYgxWRHzGYrNicVQLAu68GvJBGKUNGdBAALmAnIukxWBH5kc1ZxTBZ7UiLCkSf+BCpy3GZfgmhALjOioikx5swE/m4/Px86PV6AMCqn8oBAEOjZdi/f3+Lx2RlZbmlNlfIyspCmCoZAPDT0QKMi6q97DFRUVFITk7u7NKIyA8xWBH5sPz8fPTs1Qv1dXWQawKR+OePIFOosPjJ+/FWWcFljzcYDG6osn2qy0sBAFOmTIE6rjviHliIvSdLMOSJ6y57rC4gAMeyshiuiMjlGKyIfJher0d9XR0mP/sm6iMykFmuRIjKjjvnLb7kcVl7tmPDysUwGj13MXi9oRoAcNOjz6NbvyFYVyCgCAjFHxevQcAlPtmK83OxesFs6PV6BisicjkGKyI/EJOcjl8MIQDq0CepCxIv0229OD/XPYW5QGR8ClK690FURT5KDSbII1OQ2LCYnYjI3bh4ncgPGG1AQUUdAKB7jG+GjugQDQDwnoFEJCkGKyI/cLZODiGA6GANwgLUUpfTKWKCtQCAkhqTxJUQkT9jsCLyA2fqHD/q3WOCJa6k8zSOWJVUG9mBnYgkw2BF5OMUQZHQmxwd1jN8dBoQACKD1JDLAKPVjmqjVepyiMhPMVgR+biAnqMByBAXqkWIViV1OZ1GKZcjKuj8qBURkRQYrIh8XGDPqwAAPXx4GrCRcwE711kRkUQYrIh8mL7OBk1CTwAC3fygBYFzATtHrIhIIgxWRD5s9xlHwIjUCARqfL9t3YUjVlzATkRS8P1PWiIfceE9/1pr84lyAEok6OydU5SHiQzUQCGXwWy1o6re4rOtJYjIczFYEXmBC+/511rywDAkzvgQMhkQJjz3nn+upJDLEBWkRnG1CcXVJgYrInI7BisiL3DhPf9iktNbdUxujRwHKuQwFR6HLMD3F643ignRorjahJIaI3rE+s95E5FnYLAi8iIxyelIzOjTqn337DsDoB51x38Guk3s3MI8iGMBexWKq3llIBG5HxevE/mgerMNZyrrAQB1J3ZIXI17OTuw17ADOxG5n0cHK5vNhhdffBFpaWnQ6XRIT0/Hq6++2uTDUgiBl156CXFxcdDpdBg/fjyys7MlrJpIerl6A4QAAmGEtbJI6nLcKiJADaVcBotNoKLOInU5RORnPDpYLViwAP/4xz/w3nvvISsrCwsWLMAbb7yBJUuWOPd544038O6772LZsmXYvXs3AgMDMXHiRBiN7GND/iunxLFYPRLVElfifnK5DF2CG9ousJ8VEbmZRwern3/+GbfeeituuukmpKam4q677sKECROwZ88eAI7RqkWLFuGFF17Arbfeiv79++PDDz9EYWEh1q5dK23xRBIxWWwoKHdcPRiFGomrkUZMSGOjUK6zIiL38uhgNWrUKGzevBknTpwAAPz666/46aefcMMNNwAA8vLyUFRUhPHjxzuPCQ0NxfDhw7Fz584WX9dkMqG6urrJg8hXnNTXwi6AiEA1AmCWuhxJxDSOWNVwxIqI3Mujrwp87rnnUF1djZ49e0KhUMBms+H111/H5MmTAQBFRY61IzExMU2Oi4mJcT7XnPnz5+OVV17pvMKJJNQ4DditSxBQK3ExEoluGLEqrTHBbheQy2USV0RE/sKjR6w+//xzrF69Gh9//DH27duHlStX4q233sLKlSs79Lpz5sxBVVWV81FQUOCiiomkZbbacbphGtAf7g3YkvAAFdQKOax2gfI6/xy1IyJpePSI1ezZs/Hcc8/h3nvvBQD069cPp0+fxvz58/Hggw8iNjYWAFBcXIy4uDjnccXFxRg4cGCLr6vRaKDRaDq1diIpnCqrhc0uEKpTISpIjXypC5KITCZDdLAGZyrrUVxtRFQQf96JyD08esSqrq4OcnnTEhUKBex2x33P0tLSEBsbi82bNzufr66uxu7duzFy5Ei31krkCbIbpgEzooMgk/n39JeznxUXsBORG3n0iNXNN9+M119/HcnJyejTpw/279+PhQsX4g9/+AMAx2+ls2bNwmuvvYaMjAykpaXhxRdfRHx8PG677TZpiydyM4vNjlN6x6Iqf54GbNR4ZSAXsBORO3l0sFqyZAlefPFF/OlPf0JJSQni4+Px6KOP4qWXXnLu88wzz6C2thbTp09HZWUlxowZg40bN0Kr1UpYOZH7nS6rg9UuEKxVIjqYU1+N3wN9jRk2u4CCC9iJyA08OlgFBwdj0aJFWLRoUYv7yGQyzJs3D/PmzXNfYUQe6KTeMQ2Y3oXTgAAQqlNBo5TDZLWjzGByXilIRNSZPHqNFRG1jt0ukNcwDZjeJVDiajyDTCZzrrMqruE6KyJyDwYrIh9wrsoIo8UOjVKO+FCd1OV4jJjgxg7sXGdFRO7BYEXkAxqnAdOiAtkM8wLnF7BzxIqI3IPBisjLCSGQW+qYBuwaxWnACzVOBZYZTLDa7BJXQ0T+gMGKyMtV1FlQVW+BQiZDSiSD1YWCNUroVArYBaA3sAM7EXU+BisiL5db6pgGTIzQQa3kj/SFmi5g5zorIup8/BQm8nKNVwNyGrB5jQvYi7mAnYjcgMGKyIvVmqw4V+UIDF2j2G29OTG8tQ0RuRGDFZEXyytzjFZFB2sQpPXofr+SaWwMWl5rhoUL2ImokzFYEXmxk41XA7IpaIuCNEoEqhUQAErZdoGIOhmDFZGXstjsyC+vA8BpwMtx9rPiOisi6mQMVkReKr+8DraGmy5HBamlLsejNV4ZWMRgRUSdjMGKyEs1TgOmR/Gmy5cT6xyx4lQgEXUuBisiL2QX52+6nMb1VZcVG+oIVlX1FhhtEhdDRD6NwYrICxVVGVFvsUGjlCMhjDddvhyNUoGIQMd0abmJo3tE1HkYrIi80MmG0arUyEAoeNPlVmmcDiw382OPiDoPP2GIvNDJhtvYsM1C68U1TAdyxIqIOhODFZGXqag1o6LOArkMSIkMkLocr9G4zqrCLANk/Ogjos7BTxciL9M4DZgYHgCNUiFxNd4jIlANlUIGq5BBFZkkdTlE5KMYrIi8jHMakDddbhO5TOZsFKpJ6ClxNUTkqxisiLyIyQbnTZfZZqHtGhewa+J7SFwJEfkqBisiL3KuXg4BoEuwBiFaldTleJ3GBezqOAYrIuocDFZEXuRcveNHltOA7dM4FaiKSkKt2S5xNUTkixisiLyETKlGsdHRKoBtFtonUKNEgEJAJpMjp8IidTlE5IMYrIi8hDZlAGxChiCNEl2CNFKX47UiNY6RqhNlZokrISJfxGBF5CV03YYDcIxW8abL7RehEQCAE2UcsSIi12OwIvICdiGg63YFAK6v6qgIdWOwMkMIIXE1RORrGKyIvEBOuQXKoAgoZQKJ4ey23hFhagFhNaPGLHC6rE7qcojIxzBYEXmBPWcdvatidXbedLmD5DLAVJwLANh7ukLiaojI1zBYEXmBXwpNAIA4HaeuXMF05igAYO+pcokrISJfw2BF5OFO6WtRUG2FsFkRq2PvJVcwnTkCAPiFwYqIXIzBisjDfZ9VDAAwFhyBmj+xLmE6kwUAyC2tRZnBJHE1RORL+DFN5OE2HXUEq/qcXRJX4jvsxhokhSgBcJ0VEbkWgxWRB6uoNTunq+qyd0tcjW/pFaUGwHVWRORaDFZEHmzr8RLYBZASqoStukTqcnxKry6Om1jvOcURKyJyHQYrIg/WOA04LF4rcSW+p3HE6sjZKtSZrRJXQ0S+gsGKyEMZLTZsP1EKABieyGDlal0CFIgL1cJqFzhQUCl1OUTkIxisiDzUT9l61JltiA/VomuYUupyfI5MJsPQ1AgAwC95nA4kItdgsCLyUN8dLQIATOgTy5sud5JhqeEAgL2nuYCdiFyDwYrIA9nsAt9nORarT+gdI3E1vmtYw4jVvtMVsNrYfJWIOo7BisgDZZ6uQHmtGaE6FYalRUhdjs/qHhOMYK0StWYbss7VSF0OEfkABisiD/TdEcc04LU9o6FS8Me0syjkMgxJcUwH8vY2ROQK/MQm8jBCCHzX0GZhQp9YiavxfY3TgQxWROQKDFZEHuZYUQ3yy+ugUcpxVfcoqcvxeeeDVQWEEBJXQ0TejsGKyMN8d8QxWnVlRhcEqNlmobP1TwyFWimH3mDCSX2t1OUQkZdjsCLyMOfbLPBqQHfQqhQYlBQGANh1skzaYojI6zFYEXmQMxV1OFJYDbnMsXCd3GNkeiQAYGcugxURdQyDFZEHabw34NDUCEQGaSSuxn+M6OoIVrtOlnOdFRF1CIMVkQdpXF81kVcDutXApDBoGtZZ5ZYapC6HiLwYV8YSSSA/Px96vb7JthqTHbvzHFNR8fZS7Nt3/v51WVlZbq3P32hVCgxODsfOk2XYebIc3aKDpS6JiLwUgxWRm+Xn56Nnr16or6trsj2w7zWIuulJmItP4sarJzV7rMHA0ZTOMjI9EjtPlmFXbhnuH5EidTlE5KUYrIjcTK/Xo76uDpOffRMxyenO7TtLlSisBwZkpOC+pWuaHJO1Zzs2rFwMo9Ho7nL9xvl1VmUQQvDG10TULgxWRBKJSU5HYkYfAIDFZkfJmZMABAb1TEOX4KYL14vzcyWo0L8MSAqFRilHWa0ZOSUGZMRwOpCI2o6L14k8wOmyOljtAsFaJaKC1FKX45c0SgWGpjruG7iT/ayIqJ0YrIg8QE6JY+1URnQQp6AkNCLt/HQgEVF7MFgRScxqsyOv4VYq3aKDJK7GvzU2Ct11shx2O/tZEVHbMVgRSSy/vA5mmx1BGiViQ7RSl+PX+ieGQadSoLzWjOwSXoFJRG3HxetEEmucBuzGaUDJqZVyDE0Nx4/Zeuw6WYYesU0XsDfXf+xyoqKikJyc7MoyiciDMVgRSchmF8jlNKBHGdE1Ej9m67EztwwPjkp1bm+p/9jl6AICcCwri+GKyE8wWBFJqKC8DmarHYFqBeJDOQ3oCZz9rPLKYLcLyOWOUcSW+o9dSnF+LlYvmA29Xs9gReQnGKyIJNS4jied04Aeo39iKALVClTWWXD0XDX6JoQ2ef7C/mNERL/FxetEErEL4GTp+TYL5BlUCjmGN4xa7cxl2wUiahsGKyKJlBplMFrt0KkUiA/TSV0OXWBUQ9uFn3PbtlCdiIjBikgiZ+sdP37p0YGQcxrQozT2s9qTVw6LzS5xNUTkTRisiKQgk6OwzvHjlxHNe9J5ml6xIQgLUKHWbMPBM1VSl0NEXoSL14k6oD19jbKysqBJ6gOTXQatSo5ETgN6HLlchpFdI7HhcBF25uoxJCVc6pKIyEswWBG1U3v7GgFAxHV/BACkdwlyXs5PnmVUuiNY7cgpw8xrMqQuh4i8BIMVUTu1p68RABzdsx2HgoYA4NWAnmxUtygAQGZ+BYwWm8TVEJG3YLAi6qC29jU6kV8IJSKghB2J4QGdWBl1RNeoQMSEaFBcbcK+0xVg+1Yiag0GKyI30yMEABCpMELBaUDJZGVlXXafnuFyFFcDa3YcwSDlGTdURUTezuOD1dmzZ/Hss89iw4YNqKurQ7du3bB8+XIMHToUACCEwMsvv4x//etfqKysxOjRo/GPf/wDGRlcE0GeRwiBMjiuAuyirJe4Gv9UXV4KAJgyZcpl9w3sNx5RN87CR9//grc/mg0AMBgMnVofEXk3jw5WFRUVGD16NMaNG4cNGzagS5cuyM7ORnj4+St03njjDbz77rtYuXIl0tLS8OKLL2LixIk4evQotFoO3pNnOVdlhBkq2E21CNeZpC7HL9UbqgEANz36PHr0H3LJfWutwMZCQJfQExOmPonvViyE0Wh0R5lE5KU8OlgtWLAASUlJWL58uXNbWlqa8/+FEFi0aBFeeOEF3HrrrQCADz/8EDExMVi7di3uvfdet9dMdCk5DfcGrMveDfkVPSWuxr9Fxqe0am3czopTqKq3QBHDUXAiujyPbhC6fv16DB06FL/73e8QHR2NQYMG4V//+pfz+by8PBQVFWH8+PHObaGhoRg+fDh27tzZ4uuaTCZUV1c3eRB1NiGE86bLdcd3SFwNtVZiuKPPWCV4oQERXZ5HB6uTJ08610t9++23eOyxx/D4449j5cqVAICioiIAQExMTJPjYmJinM81Z/78+QgNDXU+kpKSOu8kiBoUV5tgMFmhgA31efukLodaKanhys0qBEpcCRF5A48OVna7HYMHD8Zf//pXDBo0CNOnT8cjjzyCZcuWdeh158yZg6qqKuejoKDARRUTtSy7pAYAEAEDYLNIXA21VuOIVS20kGt5+yEiurR2BauuXbuirKzsou2VlZXo2rVrh4tqFBcXh969ezfZ1qtXL+Tn5wMAYmNjAQDFxcVN9ikuLnY+1xyNRoOQkJAmD6LOdOE0YCRqJK6G2iJQo0RkoBoAoE3uJ3E1ROTp2hWsTp06BZvt4k7EJpMJZ8+e7XBRjUaPHo3jx4832XbixAmkpKQAcCxkj42NxebNm53PV1dXY/fu3Rg5cqTL6iDqqJIaE2qMVqgUMoSDl+t7m8ZRK21Kf4krISJP16arAtevX+/8/2+//RahoaHOr202GzZv3ozU1FSXFffEE09g1KhR+Otf/4q7774be/bswfvvv4/3338fACCTyTBr1iy89tpryMjIcLZbiI+Px2233eayOog6qnG0KjUyEIoSIXE11FZJEQH49UwVtCkDpC6FiDxcm4JVY1iRyWR48MEHmzynUqmQmpqKt99+22XFDRs2DF999RXmzJmDefPmIS0tDYsWLcLkyZOd+zzzzDOora3F9OnTUVlZiTFjxmDjxo3sYUUeQwjhbLOQER2E6hKJC6I2SwjTARBQRSbBZG/5whgiojYFK7vdDsAxBffLL78gKiqqU4q60KRJkzBp0qQWn5fJZJg3bx7mzZvX6bUQtUepwYSqeguUchlSowJxUOqCqM20KgWCYIQBOlTaNVKXQ0QerF0NQvPy8lxdB5HPyrlgGlCl8OgLcekSQlHrCFY2Bisialm7O69v3rwZmzdvRklJiXMkq9G///3vDhdG5AuEEMgudgSrbtFBEldDHRGGOpwFUGFXQwgBmYw30Caii7UrWL3yyiuYN28ehg4diri4OH7AELVAbzCjst4ChVyGtCg2mPRmIaiDsFlhUihRbbQiVKeSuiQi8kDtClbLli3DihUrcP/997u6HiKfcn4aMABqJacBvZkCAqbC49Am9UFBeR1CE0IvfxAR+Z12fdKbzWaMGjXK1bUQ+RRHU1BHM1BOA/oGY/6vAICCijqJKyEiT9WuYPXwww/j448/dnUtRD6lvNaMijoLFDJOA/oK46mGYFVeDyHYj4yILtauqUCj0Yj3338f33//Pfr37w+Vqulag4ULF7qkOCJvllPqmAZMjgyARqmQuBpyBdO545DDjnqLIzhHBvEKQSJqql3B6uDBgxg4cCAA4PDhw02e40J2IoeTpbUAgK5dOFrlM2xWhMrNqLBrUVBRz2BFRBdpV7DaunWrq+sg8ik1RgtKakwAgLRIBitfEqZwBKszFXUYmBQmdTlE5GF4mRJRJzipd4xWxYVqEahpd7s48kBhckdgPlNRDzvXWRHRb7TrE3/cuHGXnPLbsmVLuwsi8gWcBvRdwXIL1Ao5TFY7SmtMiAnhfUmJ6Lx2BavG9VWNLBYLDhw4gMOHD190c2Yif2Oy2nCm4XL89Ci2WfA1MhmQEK5Dnr4WBRV1DFZE1ES7gtU777zT7Pa5c+fCYDB0qCAib3e6rA52AYQHqBAeqJa6HOoESQ3B6kx5PYamSF0NEXkSl66xmjJlCu8TSH4vt6HNQtcuHK3yVUkRAQCAs5X1sNm5zoqIznNpsNq5cye0Wg6Lk/+y2QVOlTmmAbuyKajPigxUQ6dSwGoXKKo2Sl0OEXmQdk0F3nHHHU2+FkLg3Llz2Lt3L1588UWXFEbkjc5W1sNstUOnUiA2lL9k+CqZTIbEcB2ySww4U16HhDCd1CUReY2xY8di4MCBWLRoUae/19y5c7F27VocOHCg09+rUbuCVWho05uPyuVy9OjRA/PmzcOECRNcUhiRNzrpnAYMhJzNcn1aUngAsksMKKiox3CpiyG/lZ+fD71e75b3ioqKQnJyslvey1Wefvpp/PnPf3Z+PXXqVFRWVmLt2rWd9p7tClbLly93dR1EXk8I4exfxWlA35cY4RilKqoywmKzQ6VgW0Byr/z8fPTs1Qv1de65KbguIADHsrK8KlwFBQUhKMi961071LkwMzMTWVlZAIA+ffpg0KBBLimKyBuV1ZpRY7RCKZchuWFxM/muMJ0KQRolDCYrCivrkcIO++Rmer0e9XV1mPzsm4hJTu/U9yrOz8XqBbOh1+vbFKxqa2vx2GOPYc2aNQgODsbTTz/d5HmTyYTnn38en3zyCSorK9G3b18sWLAAY8eOBQCsWLECs2bNwmeffYZZs2ahoKAAY8aMwfLlyxEXFwcA2LZtG5555hkcOXIEKpUKffr0wccff4yUlJQmU4Fz587FypUrAZy//d7WrVsxb9489O7dG++9956zrtLSUiQkJGDDhg249tpr2/S9alewKikpwb333ott27YhLCwMAFBZWYlx48bh008/RZcuXdrzskRe7XTDovXEcB2UHL3weTKZDEkROmSdq8GZCgYrkk5McjoSM/pIXUazZs+eje3bt2PdunWIjo7GX/7yF+zbt8/ZD3PmzJk4evQoPv30U8THx+Orr77C9ddfj0OHDiEjIwMAUFdXh7feegurVq2CXC7HlClT8PTTT2P16tWwWq247bbb8Mgjj+CTTz6B2WzGnj17mm1i/vTTTyMrKwvV1dXOmbeIiAg8/PDDmDlzJt5++21oNI77f3700UdISEjANddc0+Zzbten/5///GfU1NTgyJEjKC8vR3l5OQ4fPozq6mo8/vjj7XlJIq93qswxDZjKf2D9RlK4Y2SyoMI9UzFE3sRgMOCDDz7AW2+9hWuvvRb9+vXDypUrYbVaATimMpcvX44vvvgCV155JdLT0/H00087R6QaWSwWLFu2DEOHDsXgwYMxc+ZMbN68GQBQXV2NqqoqTJo0Cenp6ejVqxcefPDBZkfVgoKCoNPpoNFoEBsbi9jYWKjVaucFeevWrXPuu2LFCkydOvWSd5lpSbtGrDZu3Ijvv/8evXr1cm7r3bs3li5dysXr5JfMVjsKK+sBACmRnAb0F4nhjnVWJdUmmKw2aJQKiSsi8hy5ubkwm80YPvz85R0RERHo0aMHAODQoUOw2Wzo3r17k+NMJhMiIyOdXwcEBCA9/fxUZ1xcHEpKSpyvN3XqVEycOBHXXXcdxo8fj7vvvts5TdgaWq0W999/P/7973/j7rvvxr59+3D48GGsX7++XefdrmBlt9uhUqku2q5SqWC329tVCJE3O1Ph6LYeqlMhLIDd1v1FsFaFMJ0KlfUWnK2oZ1NYojYwGAxQKBTIzMyEQtH0l5ILF5z/Nm/IZDKIC26Avnz5cjz++OPYuHEjPvvsM7zwwgvYtGkTRowY0epaHn74YQwcOBBnzpzB8uXLcc011yAlpX23VWjXVOA111yD//u//0NhYaFz29mzZ/HEE0+0eZEXkS9oXF+VwkXrfqfx6sCCinqJKyHyLOnp6VCpVNi9e7dzW0VFBU6cOAEAGDRoEGw2G0pKStCtW7cmj9jY2Da916BBgzBnzhz8/PPP6Nu3Lz7++ONm91Or1bDZbBdt79evH4YOHYp//etf+Pjjj/GHP/yhTe9/oXYFq/feew/V1dVITU1Feno60tPTkZaWhurqaixZsqTdxRB5IyHOr6/iNKD/SeY6K6JmBQUF4aGHHsLs2bOxZcsWHD58GFOnToVc7oge3bt3x+TJk/HAAw9gzZo1yMvLw549ezB//nx8/fXXrXqPvLw8zJkzBzt37sTp06fx3XffITs7u8lSpQulpqbi4MGDOH78OPR6PSwWi/O5hx9+GH/7298ghMDtt9/e7vNu11RgUlIS9u3bh++//x7Hjh0DAPTq1Qvjx49vdyFE3spgBaqNVshlQGI4g5W/SWhYZ1VmMKPObEWAukNdbIh8yptvvgmDwYCbb74ZwcHBeOqpp1BVVeV8fvny5Xjttdfw1FNP4ezZs4iKisKIESMwadKkVr1+QEAAjh07hpUrV6KsrAxxcXGYMWMGHn300Wb3f+SRR7Bt2zYMHToUBoMBW7dudbZ2uO+++zBr1izcd999Hbo9X5s+AbZs2YKZM2di165dCAkJwXXXXYfrrrsOAFBVVYU+ffpg2bJluPLKK9tdEJG3KTY6fvuKD9NBrWSbBX8ToFYiKkgNvcGMMxX16B4TLHVJ5GeK83M99j2CgoKwatUqrFq1yrlt9uzZzv9XqVR45ZVX8MorrzR7/NSpUzF16tQm22677TbnGquYmBh89dVXLb7/3LlzMXfuXOfXXbp0wXfffdfsvnq9HkajEQ899NDlTuuS2hSsFi1ahEceeQQhISEXPRcaGopHH30UCxcuZLAiv1Jc7whTbLPgvxLDA6A3mFFQUcdgRW4TFRUFXUAAVi+YffmdXUAXEICoqCi3vJc7WSwWlJWV4YUXXsCIESMwePDgDr1em4LVr7/+igULFrT4/IQJE/DWW291qCAir6JQodTk6HPC9VX+KylchwMFlThTzgXs5D7Jyck4lpXFewV20I4dOzBu3Dh0794dX375ZYdfr03Bqri4uNk2C84XUypRWlra4aKIvIU2qQ9sQoZAjQKRgWyz4K8SwnSQAaist8BgsiJIw3VW5B7Jyck+GXbcaezYsU3aN3RUmxaEJCQk4PDhwy0+f/DgwTY15SLydrq0IQCAlIjAdnXoJd+gUSnQJdhxK4yzbLtA5NfaFKxuvPFGvPjiizAajRc9V19fj5dffrnVK/mJfIE2zTEXz2lAarw68AzbLhD5tTaNV7/wwgtYs2YNunfvjpkzZzrb0h87dgxLly6FzWbD888/3ymFEnmaSqMN6i4pAASS2BjU7yWG6bA/vxJnKjliReTP2hSsYmJi8PPPP+Oxxx7DnDlznHOSMpkMEydOxNKlSxETE9MphRJ5miOlZgBAqEpAp+I94vxdQphjxKqyjuusiPxZm3/yU1JS8M0336CiogI5OTkQQiAjIwPh4eGdUR+Rxzpc4ghWXbSuW/RI3kujUiA6WIOSGhPOVtSjRyzbLhD5o3b/ShUeHo5hw4a5shYir3K4xAQA6KLhjcfJISFch5IaE85U1DFYEfkptokmaoeSaiPO1tgghB1RHLGiBokN04FcZ0XkvxisiNph58kyAIC5+CTU/CmiBr9dZ0VE/of/JBC1w66GYGXKPyRxJeRJGtdZAexnReSvGKyI2mFnriNYGfMPSlwJeRr2syLybwxWRG10rqoep8rqIJcBxoIjUpdDHobrrIj8G4MVURs1TgN2DVdBmDkqQU1duM6q3iZxMUTkdgxWRG3UOA3YtwtvukwXu/C+gXojP2KJ/A1/6onaqPGKwL7RDFbUvMbpwFITb8xN5G8YrIja4ExFHQrK66GQy9ArisGKmpfYsIC9lCNWRH6HP/VEbbDrZDkAoH9iKHQq/vhQ8+IbRqwMVhkUgbzdF5E/4b8MRG2wu2EacETXSIkrIU+mvWCdlSa5n8TVEJE7MVgRtcHe0xUAgCtSIySuhDxd4zorbRKDFZE/YbAiaiW9wYQ8fS0AYHAyp3fo0hrXWWmT+0pcCRG5E4MVUStlNoxWdY8JQmiASuJqyNM51lkJqCKTUM6GVkR+g8GKqJUag9WQFE4D0uVpVQqEqQQA4GipWeJqiMhdGKyIWmnvKccVgUNTOA1IrROldQSrwyUMVkT+gsGKqBWMFhsOn60GAAxNZbCi1umisQMAjpSaJK6EiNyFwYqoFQ6drYLZZkdUkAbJEQFSl0NeIkojIIQdZ2tsKKkxSl0OEbkBgxVRK+w95VhfNSw1HDIZb1NCraNWAJaSPADA7obmskTk2xisiFqhcX3VEK6vojYy5h8CAOxqaC5LRL6NwYroMux2gcx8x4jVUDYGpTZisCLyL0qpCyDyBPn5+dDr9c0+d6bagso6C9QKwFyci32ljqnArKwsd5ZIXsp05ghkAHJLa1FSY0R0sFbqkoioEzFYkd/Lz89Hz169UF9X1+zzQf0nIPKGx1GddwjDh0266HmDwdDZJZIXsxsNSA1TIq/Sit0ny3HzgHipSyKiTsRgRX5Pr9ejvq4Ok599EzHJ6Rc9v7dMgdO1wIC+vdF36Rrn9qw927Fh5WIYjbzaiy6tTxcN8iqt2HWyjMGKyMcxWBE1iElOR2JGn4u2by49BcCCHmlJSIwKdG4vzs91X3Hk1fpGq/G/7FqusyLyA1y8TnQJdWYrKustAIC4UK6NofbpFaWGTHZ+nRUR+S4GK6JLOFfl+EcwMlANrUohcTXkrYI1cvSMDQHAflZEvo7BiugSihqCFUerqKNGdHW06uB0IJFvY7AiuoTGYBXLYEUdNKJrJAAGKyJfx2BF1AK7XaC4YT1MbAiDFXXM8LQI5zqr0hrelJnIVzFYEbWgrNYMi01ArZAjIlAtdTnk5cIC1OfXWeVx1IrIVzFYEbWgcRowJlTDGy+TS3CdFZHvY7AiasG56noAQFyITuJKyFecX2fFKwOJfBWDFVELuHCdXK1xnVVOiYHrrIh8lFcFq7/97W+QyWSYNWuWc5vRaMSMGTMQGRmJoKAg3HnnnSguLpauSPIJRosNFXWOxqBcuE6uwnVWRL7Pa4LVL7/8gn/+85/o379/k+1PPPEE/vvf/+KLL77A9u3bUVhYiDvuuEOiKslXFFc7RqtCdSro1GwMSq4zPI3rrIh8mVcEK4PBgMmTJ+Nf//oXwsPDndurqqrwwQcfYOHChbjmmmswZMgQLF++HD///DN27dolYcXk7dgYlDoL11kR+TavCFYzZszATTfdhPHjxzfZnpmZCYvF0mR7z549kZycjJ07d7b4eiaTCdXV1U0eRBc6V83+VdQ5LlxnVVLN+wYS+RqPD1affvop9u3bh/nz51/0XFFREdRqNcLCwppsj4mJQVFRUYuvOX/+fISGhjofSUlJri6bvJgQggvXqdOEB6rRJ96xzmpHrl7iaojI1Tw6WBUUFOD//u//sHr1ami1rvsHbs6cOaiqqnI+CgoKXPba5P0q6y0wWe1QyGWICtJIXQ75oNHdogAAP2VznRWRr/HoYJWZmYmSkhIMHjwYSqUSSqUS27dvx7vvvgulUomYmBiYzWZUVlY2Oa64uBixsbEtvq5Go0FISEiTB1GjxtGq6GANFHI2BiXXu7JbFwDAjhw9hBASV0NEruTRweraa6/FoUOHcODAAedj6NChmDx5svP/VSoVNm/e7Dzm+PHjyM/Px8iRIyWsnLzZOS5cp042NDUcaqUcRdVG5JbWSl0OEbmQUuoCLiU4OBh9+/Ztsi0wMBCRkZHO7Q899BCefPJJREREICQkBH/+858xcuRIjBgxQoqSyQcUceE6dTKtSoFhqeHYkVOGHTl6dIsOkrokInIRjx6xao133nkHkyZNwp133omrrroKsbGxWLNmjdRlkZey2OzQGxwdsblwnTpT4zqrH7O5gJ3Il3j0iFVztm3b1uRrrVaLpUuXYunSpdIURD6lpNoEIYBAjQLBWpXU5ZAPG9MtCm/gOHadLIPVZodS4fW/5xIRfGDEisiVOA1I7tInPhRhASoYTFb8eqZK6nKIyEUYrIgucK6qHgAQF6qTuBLydQq5DKPSHV3Yd+RwOpDIVzBYEV2AI1bkTs5+VgxWRD6DwYqoQZ0VqDXZIJMB0SFsDEqdb0xDsNqfX4Fak1XiaojIFRisiBqUmx3NQKOCNFBxITG5QUpkIJIidLDYBPbk8abMRL6A/3oQNSg3OX4cOA1I7jSG04FEPoXBiqhB44gVO66TOzWus/rhRKnElRCRKzBYEQGAXIGKhmDFxqDkTmO6RUEuA7JLDCisrJe6HCLqIAYrIgDqLqmwCxk0SjnCdGwMSu4TFqDGoORwAMB2jloReT0GKyIA6vieAByjVTKZTOJqyN9c3b0LAGDb8RKJKyGijmKwIgKgie8BgAvXSRpjeziC1Y6cMlhsdomrIaKOYLAiAqBpGLHiwnWSQt/4UEQGqmEwWZF5ukLqcoioAxisyO/VmOxQRcQDAGI4YkUSkMtluMo5Hch1VkTejMGK/N6JcjMAIEgpoFUpJK6G/FXjOisuYCfybgxW5PdOlFkAAJEarm0h6VyZEQWZDMg6V43ihntWEpH3YbAiv3eizDFiFaEWEldC/iwySIP+CaEAOGpF5M0YrMiv2e0C2eWOEasIDYMVSevqHtEAgO1cZ0XktRisyK+d1BtQZxGwW4wIUTFYkbQa11n9mF0KK9suEHklBivya/vyKwEA5nPZkLMvKElsYFIYwgJUqDZacaCgUupyiKgdGKzIr+1vCFamwuPSFkIEQCGX4coMx6jVVnZhJ/JKDFbk1xpHBRisyFNc09MRrDZnMVgReSOl1AUQSaXWZMXxomoAgPkcgxV1nqysrFbvG2GyQy4DjhXVoKC8DkkRAZ1YGRG5GoMV+a2DZ6pgF0BUgBynDeVSl0M+qLrccXXflClT2nRczH3zoU3uhy9/PoYnJg3ujNKIqJMwWJHf2l/guCdb9wg1MiWuhXxTvcExInrTo8+jR/8hrT4uM78CpwBszS7DE51TGhF1EgYr8lv7TlcCADIiVdIWQj4vMj4FiRl9Wr1/jeUITp0DjpaaUW20IETLv6NE3oKL18kv2e0Cmacd03+9otQSV0PUVLAKsJSdgdUO/MAu7ERehcGK/NJJvQEVdRZoVXKkhXE0gDxPXc5uALw6kMjbMFiRX/rllGN91aCkcKgU7AxKnqc+Zw8ARz8rdmEn8h4MVuSXfslzTAMOSw2XuBKi5pnOZiFILUNlnQWZpyukLoeIWonBivzSLw3rq4amRkhcCVELhB1D4rQAgM3HOB1I5C0YrMjvFFUZUVBeD7kMGJQcJnU5RC0aGq8BAHx/tFjiSoiotRisyO/sbbwaMC4EwbyMnTzYwBgNVAoZTuprkVtqkLocImoFBivyO3sbFq4P4zQgebhAtRwjukYCAL47wlErIm/ABqHkd3451bi+igvXybNlZWWhd0gyfgSwZk8uhodUXfaYqKgoJCcnd35xRNQsBivyKzVGC7LOOW4zwhEr8lQX3mNQHhiGxBkfIrvcgivGToStRn/JY3UBATiWlcVwRSQRBivyK/vyK2EXQHJEAGJCtFKXQ9Ss395jcFsxUGYCbnr+/6FbcMs9rYrzc7F6wWzo9XoGKyKJMFiRX9nLaUDyIo33GOytqcCP2XqUIRhjMxKlLouILoGL18mvNK6v4jQgeZP0LkEAgLMV9ag32ySuhoguhcGK/IbZaseBgkoA7LhO3iVUp0KXYA0EHPe5JCLPxWBFfuNIYRWMFjvCA1TOEQAib9Gt4e9sbmmtxJUQ0aUwWJHf2N1wf8AhKRGQyXjjZfIu6V0CAQD5ZXUwW3lTZiJPxWBFfmNnbhkAYGR6pMSVELVdRKAaYQEq2ITAqTKOWhF5KgYr8gsWm925cH0UgxV5IZlMdn46sITrrIg8FYMV+YWDZypRZ7YhPECFHjHBUpdD1C7p0Y5glVdWC6uN04FEnojBivzChdOAcjnXV5F3ignWIFirhMUmcKqsTupyiKgZDFbkF35uDFZdOQ1I3ksmkyGjYdQqu6RG4mqIqDkMVuTzTFYbMk9XAABGpkdJXA1Rx2Q0TGXn6Wth4XQgkcdhsCKft+90JUxWO7oEa5yXrBN5q5hgDUKc04G8OpDI0zBYkc/7MbsUAHBltyj2ryKvJ5PJnKNW2cW8OpDI0zBYkc/7MVsPALiyO6cByTc0rrPidCCR52GwIp9WZjDhcGEVAGB0NwYr8g3RDdOBVrvAKT2nA4k8CYMV+bSfcvQQAugVF4LoYK3U5RC5xIXTgSfYLJTIozBYkU9rnAa8KoOjVeRbujdMB57S1/LegUQehMGKfJYQ4vzC9YwuEldD5FpdgjUI1akc04G8OpDIYzBYkc86VlSD4moTNEo5hqaGS10OkUs1aRbKqwOJPAaDFfmsLcdKAABjukVBq1JIXA2R63VvbBZaxulAIk/BYEU+a3NWMQDgml7REldC1DmigtQI1algswvk8epAIo/AYEU+qcxgwv6CSgDANT0ZrMg3yWQydI/hvQOJPAmDFfmkbcdLIQTQOy4EcaE6qcsh6jQZ0Y7pwFNldbBwNpBIcgxW5JMa11eN5zQg+bioIDXCAhzTgefq+ZFOJDX+FJLPMVlt+OGEo83COE4Dko+TyWTo3jBqVVDLj3QiqfGnkHzOjhw9akxWxIRoMCAxTOpyiDpdj1hHsCo2yiDXBktcDZF/Y7Ain7PhUBEAYGKfWMjlMomrIep8EYFqdAnSQECGgB6jpS6HyK8xWJFPsdjs2NTQZuH6vrESV0PkPo2jVoF9xkpbCJGfY7Ain7L7ZDkq6yyICFTjitQIqcshchtH2wUBbVJflNbapC6HyG8xWJFP2XD4HABgQu8YKBX8603+I1irQpRGAAB+KqiXuBoi/8V/echnWG12fHukYX0VpwHJDyUFOhpZ/ZjPYEUkFQYr8hk7csugN5gREajGmG5RUpdD5HaJOjuEzYJTlVacKGYndiIpMFiRz1i3/ywA4KZ+cVBxGpD8kFoB1J/MBACsO3BW4mqI/JNS6gKIXKHebHNOA/YKMGDfvn2tPjYrK6uzyiJyu9qj2xGQMQLrDhTi6Qk9IJOx5QiRO3l0sJo/fz7WrFmDY8eOQafTYdSoUViwYAF69Ojh3MdoNOKpp57Cp59+CpPJhIkTJ+Lvf/87YmJiJKyc3G1TVjFqzTbYqooxeeKkdr2GwWBwcVVE7lefswdapQxnKuqxL78CQ1J4dSyRO3l0sNq+fTtmzJiBYcOGwWq14i9/+QsmTJiAo0ePIjAwEADwxBNP4Ouvv8YXX3yB0NBQzJw5E3fccQd27NghcfXkTmsbpgENR7Zi8rNvIiY5vdXHZu3Zjg0rF8NoNHZWeURuI6wmDE/QYvvpeqw7UMhgReRmHh2sNm7c2OTrFStWIDo6GpmZmbjqqqtQVVWFDz74AB9//DGuueYaAMDy5cvRq1cv7Nq1CyNGjJCibHKzoiojth133HTZcHgLYu69A4kZfVp9fHF+bmeVRiSJK5Mdwerrg+fw4qTeXHNI5EZe9dNWVVUFAIiIcPwGlpmZCYvFgvHjxzv36dmzJ5KTk7Fz584WX8dkMqG6urrJg7zXl5kFsAugdxc1rBWFUpdDJLkBMRpEBqpRVmvGjhy91OUQ+RWvCVZ2ux2zZs3C6NGj0bdvXwBAUVER1Go1wsLCmuwbExODoqKiFl9r/vz5CA0NdT6SkpI6s3TqRHa7wGd7CwAA49N0EldD5BkUchkm9Y8DAKw7wF82iNzJa4LVjBkzcPjwYXz66acdfq05c+agqqrK+SgoKHBBhSSFnSfLUFBej2CNEiMTGayIGt0yMAEA8O2RItSZrRJXQ+Q/vCJYzZw5E//73/+wdetWJCYmOrfHxsbCbDajsrKyyf7FxcWIjW2587ZGo0FISEiTB3mn1btPAwBuGRgPjZKXlRM1GpwchuSIANRd0IqEiDqfRwcrIQRmzpyJr776Clu2bEFaWlqT54cMGQKVSoXNmzc7tx0/fhz5+fkYOXKku8slNyusrMe3R4oBAPePTJG4GiLPIpPJcOdgxy+i/8lks1Aid/HoYDVjxgx89NFH+PjjjxEcHIyioiIUFRWhvt5xH6zQ0FA89NBDePLJJ7F161ZkZmZi2rRpGDlyJK8I9AOrd5+GzS4womsEesZy1JHot+4Y7JgO3JGrx9lK3j+QyB08Olj94x//QFVVFcaOHYu4uDjn47PPPnPu884772DSpEm48847cdVVVyE2NhZr1qyRsGpyB6PFhk/2ONbGTR2VKm0xRB4qKSIAI7tGQgjgq31npC6HyC94dB8rIcRl99FqtVi6dCmWLl3qhorIU/z310KU15oRH6rF+F7ssk/UkruGJGLnyTJ8mXkGM8Z14y1uiDqZR49YETXHbhd4/4eTAIAHRqVCyeaHRC26oV8sAtUKnCqrQ+bpCqnLIfJ5/BeJvM6WYyXILjEgWKPE74cnS10OkUcLUCtxYz9HT6svMzkdSNTZGKzI6yzb7rgFzeQRKQjRqiSuhsjz3TXEcXXg/w6eQ73ZJnE1RL6NwYq8yi+nyrH3dAXUCjn+MDpV6nKIvMKw1AgkRehgMFnZ04qokzFYkVdZ9P0JAMCdQxIRHaKVuBoi7yCXn+9p9UUm7zRB1JkYrMhr7Mkrx46cMqgUMswYly51OURe5c7BiZDJgB05ZThdVit1OUQ+i8GKvEbjaNXvhiYhMTxA4mqIvEtSRACuyugCAPh4T77E1RD5LgYr8go7cvT4OdcxWvWnsRytImqPyQ1X0X659wxMVi5iJ+oMDFbk8ex2gb9+kwUAmDw8haNVRO10Tc9oxIZoUVZrdt5nk4hcy6M7rxMBwNoDZ3GksBrBGiUevzZD6nKIPF5WVlaLz12VqMTnR4H3vz+CRNv5KwSjoqKQnMy+cEQdxWBFHs1oseGtb48DAB4bl46IQLXEFRF5ruryUgDAlClTWtxHERyJhD/+G4dLzRh+3a2wljuahuoCAnAsK4vhiqiDGKzIoy3fcQqFVUbEh2rxh9FpUpdD5NHqDdUAgJsefR49+g9pcb+fS2U4Vw9c/eRSDAi3oTg/F6sXzIZer2ewIuogBivyWOW1Zvx9aw4A4KkJPaBVKSSuiMg7RManIDGjT4vPDwuvxfpfC1FQr8LEId3dWBmR72OwIo/1+tp9qDFZkRamRAqKsW9fySX3v9S6EiI6LyUyAMFaJWqMVsd9N6UuiMiHMFiRR9q2/wS+/LUEMrkCu5Y9i2Fzfm31sQaDoRMrI/J+cpkMfRNCsTO3DAcKKjEmTOqKiHwHgxV5HCEE3th8GjK5ApGoxp3PvNyq47L2bMeGlYthNBo7uUIi79cvPhS/5JWjpMYEvU4mdTlEPoPBijzOmn1ncVRvht1sxBWp2kuuFblQcX5uJ1dG5Dt0agV6xYXg0NkqnKjm+kUiV2GDUPIoVfUWzN/gWCtV9fOnCGD0J+o0g5LDAABFRjmUEYnSFkPkIxisyKO8/d1x6A1mJAQrUP3LWqnLIfJp4QFqdI0KBACEDLtV4mqIfAODFXmMw2er8NGu0wCA6YNDAbtV4oqIfN/g5HAAQGCfa1Bl5P0DiTqKwYo8gtVmx3NrDsIugFsGxKNfjEbqkoj8QnyYFuFqO+QqDTbm1kldDpHXY7Aij/D+jydx+Gw1QnUqvDCpl9TlEPkNmUyGjGA7AGBDTh2MFo5aEXUEgxVJLrfUgEXfZwMAXpzUG9HBWokrIvIvCQF2WKtKUG2y44u9BVKXQ+TVGKxIUna7wLNfHoTZasfV3bvgzsEJUpdE5HfkMqB6zxoAwD+25cJk5agVUXsxWJGkPtx5CntPVyBQrcBf7+gHmYyNComkUPPrtwjXylFYZcR/Ms9KXQ6R12KwIskUlNfhjW+PAwCeu6EnEsJ0EldE5MdsFtzeMwgAsHRrDsxWu8QFEXknBiuShM0u8MRnB1BntuGKtAhMHp4idUlEfu+6rgHoEqzB2cp6fMa1VkTtwmBFkli2PRd7T1cgSKPE278bALmcU4BEUtMoZZg5rhsA4N3N2ag3c60VUVsxWJHbHTpThXc2nQAAzL2lD5IiAiSuiIga3XdFMhLDdSitMWH5z3lSl0PkdXgnNup0+fn50Ov1AACTVeDpTaWw2gVGJmqRhmLs21dy0TFZWVnuLpOIAKiVcjx5XXc8+fmvWLYtF/cNS0Z4oFrqsoi8BoMVdar8/Hz07NUL9XWOjs7h4/+IkCGTYK0pw+ezZ+JTY80ljzcYDO4ok4gucOvABLz/w0kcK6rB4s3ZmHtLH6lLIvIaDFbUqfR6Perr6jD52TdhjeqGXXoVAGBs1xDEvL2yxeOy9mzHhpWLYTQa3VUqETVQyGV46ebe+P2/dmPVrtOYPDwZGTHBUpdF5BUYrMgtAuLSsa1EA8COwclhGJLR5ZL7F+fnuqcwImrWqPQoTOgdg++OFuPVr7Owctow9pkjagUuXqdOJ1NqsFuvhNlmR3yYFqPTo6QuiYha4S839oJaIccPJ0qx8XCR1OUQeQUGK+pUQghETPgjqixyBKgVuLFvHFsrEHmJ1KhA/PHqrgCAV/57FAaTVeKKiDwfgxV1qu/z6hHU7zoAAjf0jUWghrPPRN7kT+O6ITkiAEXVRmebFCJqGYMVdZqduWX4174qAEDfUBsSw9mvisjbaFUKzLvVcVXgv3fkYU9eucQVEXk2BivqFLmlBvzxo0xY7UBt1g/oHsL7jhF5q7E9onH30EQIATz1xQFOCRJdAoMVuVyZwYRpy39BVb0F3SNVKPtmEXgxEZF3e3FSbySE6VBQXo/Xvz4qdTlEHovBilzKaLFh+qpM5JfXISlCh+dGh0NYzVKXRUQdFKxV4a3fDQAAfLKnAFuPXXzHBCJisCIXstjsePyT/cg8XYFgrRLLpw5DmFYhdVlE5CIj0yPx0Jg0AMAz/zmIilr+0kT0W7xEi1zCarNj1mcH8N3RYqiVcvzz/iHoFh2MfWekroyIWqs19+gcHy3wbYgSZ6pNeGzlLnz8xyvZQoXoAgxW1GF2u8AzXx7E1wfPQaWQ4Z9ThmAUm4ASeY3q8lIAwJQpU1q1vyo6DbFT3sKufGDemr2Ye9ewziyPyKswWFGH2O0Cz689jDX7z0Ihl2HJfYMxrme01GURURvUG6oBADc9+jx69B/SqmMO5ZfiBBKwYm8JxvQuxvjeMZ1ZIpHXYLCidrPa7Hj2P4fwn31nIJcBi+4ZiOv7xkpdFhG1U2R8ChIz+rRy7yPY+9V/ETLkZjzx2QGs//MYpEUFdmp9RN6Ai9epXYwWG/740T78Z98ZKOQyvH33ANw8IF7qsojIjSq2fICeUSrUmKx4dNVe1BgtUpdEJDkGK2qzGqMFD/57D77PalioPmUIbh+UKHVZRORudiueHhmO6GANThQ7mgKbrWwGTP6NwYrapKjKiHvf34XdeeUI0ijx4R+u4NoKIj8WoVPggweHIUCtwI6cMjz9xa+w24XUZRFJhsGKWu1IYRVuW7oDRwqrERmoxqfTR2BE10ipyyIiifVLDMWyKUOglMuw/tdCvPZ1FoRguCL/xMXrfig/Px96vb5Nx+wtNOLtnRUw2YCEYAWevzIU5uJc7Cu+9HGt6YtDRN7vqu5d8NbvBmDWZwfw7x156BKswWNj06Uui8jtGKz8TH5+Pnr26oX6urpWHxM89BaEj3sIMrkC9acOYOfa+bjJVNum9zUYDG0tlYi8zG2DElBaY8Lr32RhwcZjUMpleOSqrlKXReRWDFZ+Rq/Xo76uDpOffRMxyZf+bdImgP3lCpyuddyWpubXbzEiJRQ9F65q9ftl7dmODSsXw2g0dqhuIvIOj1zVFdVGC5ZsycHr32TBahccuSK/wmDlp2KS0y/Zr8ZgtOJ/hwpRXGuCTAakimJs27gEUa+834Y+N0Bxfq4ryiUiL/LUhB5QyGVY9H02Fmw8BpvdjpnXZEhdFpFbMFjRRQor6/H1oXOoM9ugVcpxQ784lO7nWikiulhL6yivigBK+gbh48MGvPXdCeQVnMXv+wZDJpMhKioKycnJbq6UyD0YrKiJw2ersPV4CewCiAxS4+b+8QjVqVAqdWFE5FFae3/BkOF3InzsNPwnqxYrv/gvyja8C51WjWNZWQxX5JMYrAgAYLML/HCiFAfPVgEAunUJwnW9Y6BWsiMHEV2sLfcXzDNYsb9cgaC+1yCh73AcWvQH6PV6BivySQxWhFqTFRsOF+FsZT0AYGTXSAxLDYdMJpO4MiLydK25v2AigOSyWnx96ByqbIGInfIGigxW9xRI5GYcjvBzZyvq8fGefJytrIdaIcfN/eNwRVoEQxURuVRKZCB+NyQJOoWAOioFT2/S47sjRVKXReRyDFZ+Sghg7+ly/Gf/GdSZbYgMVOOeYUno2iVI6tKIyEd1CdZgXIwFxrNZqLMITF+VifkbsmC18f6C5DsYrPyQTBOInXolduSUQQigZ2ww7hmWhIhAtdSlEZGP0ymB4o/nYFJGIADgn9tP4vf/2o2C8tY3LSbyZAxWfia33IK4qYtxrl4OhUyGa3pGY0LvGKgU/KtARG5it+IPg0Kw9PeDEahWYM+pcly/6Ad8tOs07zFIXo+L1/2E1WbHP7blYtFmPVRhsQhQCNwyOAkxIVqpSyMiP5SVlYVevYA3ro3Ae79UIktvwQtrD+PznSfwp6GhiA68+J8n9r8ib8Bg5Qfy9LV48vMD2J9fCQCoPb4DN187jKGKiNyu+f5XMgQPvRlhVz2Ag8XA9LUFqN69BtW7/wNhNTn30gUEsP8VeTwGKx9mttqx4uc8vLMpG/UWG4K1SvxhQCCeXDAf6uvWSF0eEfmhS/W/qrEA+8rt0EOLsDG/R9zV96FfmA2JAXaUFORi9YLZ7H9FHo/BykftyNHjpXWHkVtaCwAYlR6Jt343AEUneWsaIpJeS/2vegqBnBIDfszRo8ZoxZ4yJfLMGqRHdQPANjDk+RisfMzxohos3nwC3xxy9IeJDFTj2Rt64q7BiZDLZWDXGCLyZDKZDBkxwUiLCkRmfgUyT1egtMaE0hoV4v6wBD/l16PfADsvuCGPxWDlI/bnV+Dv23Kx6WgxAEAuAx4YmYonruuOUJ1K4uqIiNpGqZBjeFok+ieEYX9BBfafLge6pGLhrkp8dGQL7hmWhHuGJSExPEDqUomaYLDyYpV1ZnxzqAhf7T+DX05VAABkMuCGvrF4/NoM9IwNkbhCIqKO0akVGJUehVhrMVZ98jlSxj+AkhoTlmzJwXtbc3BlRhfc2DcW1/WOQWSQRupyiRisvIkQAqfL6rDrZBk2HyvBtuMlsNgcPV+UchluG5SAP16djm7R7J5ORL5FLQeqfv4U7y96GnpNPFbvPo2fc8vww4lS/HCiFH/56hCuSIvAuB7RGN41En3jQ6DkdCFJgMHKxfLz86HX69t8nMlkgkZz/rcti02guNaGM9VWnKm2Ir/KgqN6M8rrm976IT1Si99dkYpbB8YjLlTX4fqJiDxZzolj6NVLhqeHqFHYvQt+LqjHrrNGnKywYtfJcuw6WQ4A0Cpl6BmlRtcwJRICgW5ROsQHK6GQt24BPHtmUXv5TLBaunQp3nzzTRQVFWHAgAFYsmQJrrjiCrfWkJ+fj569eqG+rg5QKCHXBEGuDYRcEwi5NghyTYDj/5tsD4RMc/7/5Zogx/+rm+8xJawWmM6dgDH/IOqO/YSSulIsz8piqCIin9Z8/6vzlKEx0GUMhza5PzRJfWHUBuFAkQkHihr7YNVC2Kyw1ehhrS6FtboUtuoSx38NFbDVV8FeVwVbXTWEuY49s6jdfCJYffbZZ3jyySexbNkyDB8+HIsWLcLEiRNx/PhxREdHu62OmV8eR+S0f0IVFAq7C+4WpJAJBKsEQpSO/0ZoBCLVAoquGcDoDBTnD2RfFyLyC5fqf/VbQgBVFgvKTDKcPluEcxU1CIjvDrtCCWVYLJRhsZc8XgY7LDUVWPHzabzEz1ZqI58IVgsXLsQjjzyCadOmAQCWLVuGr7/+Gv/+97/x3HPPua2OarMdiqBwXDhZp1HKoVHKoVbKoVEqHF+rLvh/pRznsg9ix5rlGH/fo+gzYFCT52Qy9m0hImrUUv+r30pq+G9mRTYOLJmN++e+j26DR6HGaG14WFBjtKLaaEGd2YZ6iw1Giw0Wm4CAHMrgSNh530JqB68PVmazGZmZmZgzZ45zm1wux/jx47Fz585mjzGZTDCZzt8moaqqCgBQXV3doVoe6qPCH6Y9ihvv/yPi4pOgkDmu0ruIteHR4Fz+PtRn74SpYDRqQpSoaeX7lZ7JAwBkZmbCYDC06pjjx48DAM5kH4GpvvV3ky/OzwUAFJ06gdzA1l/e7M7jvKHG9h7nDTW29zhvqLG9x3lDje09zhtqvPC44tMnEBLkOE4JILzhATkAbcMDgM0OFBYWYMPq9zHoow86/O9Co+DgYP6i7CdkwstvJV5YWIiEhAT8/PPPGDlypHP7M888g+3bt2P37t0XHTN37ly88sor7iyTiIj8WFVVFUJC2ALHH3j9iFV7zJkzB08++aTza7vdjvLyckRGRnr1bxTV1dVISkpCQUGBT/0A87y8C8/Le/jiOQGeeV7BwcFSl0Bu4vXBKioqCgqFAsXFxU22FxcXIza2+QWKGo2mSWsDAAgLC+usEt0uJCTEYz5MXInn5V14Xt7DF88J8N3zIs/m9d3T1Go1hgwZgs2bNzu32e12bN68ucnUIBEREVFn8/oRKwB48skn8eCDD2Lo0KG44oorsGjRItTW1jqvEiQiIiJyB58IVvfccw9KS0vx0ksvoaioCAMHDsTGjRsRExMjdWlupdFo8PLLL180zenteF7eheflPXzxnADfPS/yDl5/VSARERGRp/D6NVZEREREnoLBioiIiMhFGKyIiIiIXITBioiIiMhFGKw8zNKlS5GamgqtVovhw4djz549Le5rsVgwb948pKenQ6vVYsCAAdi4cWOTfWpqajBr1iykpKRAp9Nh1KhR+OWXX5rsI4TASy+9hLi4OOh0OowfPx7Z2dlee04WiwXPPvss+vXrh8DAQMTHx+OBBx5AYWGhy85JivP6rT/+8Y+QyWRYtGiRq04JgHTnlZWVhVtuuQWhoaEIDAzEsGHDkJ+f79XnZTAYMHPmTCQmJkKn06F3795YtmyZS87nhx9+wM0334z4+HjIZDKsXbv2ssds27YNgwcPhkajQbdu3bBixYqL9rnc98loNGLGjBmIjIxEUFAQ7rzzzosaNHeEFOdVXl6OP//5z+jRowd0Oh2Sk5Px+OOPO+8jS9QmgjzGp59+KtRqtfj3v/8tjhw5Ih555BERFhYmiouLm93/mWeeEfHx8eLrr78Wubm54u9//7vQarVi3759zn3uvvtu0bt3b7F9+3aRnZ0tXn75ZRESEiLOnDnj3Odvf/ubCA0NFWvXrhW//vqruOWWW0RaWpqor6/3ynOqrKwU48ePF5999pk4duyY2Llzp7jiiivEkCFDOnw+Up7XhdasWSMGDBgg4uPjxTvvvOP155WTkyMiIiLE7Nmzxb59+0ROTo5Yt25di+/rLef1yCOPiPT0dLF161aRl5cn/vnPfwqFQiHWrVvX4XP65ptvxPPPPy/WrFkjAIivvvrqkvufPHlSBAQEiCeffFIcPXpULFmyRCgUCrFx40bnPq35Pv3xj38USUlJYvPmzWLv3r1ixIgRYtSoUR0+HynP69ChQ+KOO+4Q69evFzk5OWLz5s0iIyND3HnnnS47L/IfDFYe5IorrhAzZsxwfm2z2UR8fLyYP39+s/vHxcWJ9957r8m2O+64Q0yePFkIIURdXZ1QKBTif//7X5N9Bg8eLJ5//nkhhBB2u13ExsaKN9980/l8ZWWl0Gg04pNPPvHKc2rOnj17BABx+vTp9p5KE1Ke15kzZ0RCQoI4fPiwSElJcWmwkuq87rnnHjFlyhRXncZFpDqvPn36iHnz5l1yH1doTQB55plnRJ8+fZpsu+eee8TEiROdX1/u+1RZWSlUKpX44osvnPtkZWUJAGLnzp0uOJOm3HVezfn888+FWq0WFoulfcWT3+JUoIcwm83IzMzE+PHjndvkcjnGjx+PnTt3NnuMyWSCVqttsk2n0+Gnn34CAFitVthstkvuk5eXh6KioibvGxoaiuHDh7f4vp5+Ts2pqqqCTCZzyT0hpTwvu92O+++/H7Nnz0afPn06fC4Xkuq87HY7vv76a3Tv3h0TJ05EdHQ0hg8f3qopIE8+LwAYNWoU1q9fj7Nnz0IIga1bt+LEiROYMGGCS86tLXbu3NnkewAAEydOdH4PWvN9yszMhMViabJPz549kZyc3OHPi/ZyxXk1p6qqCiEhIVAqfaKPNrkRg5WH0Ov1sNlsF3WLj4mJQVFRUbPHTJw4EQsXLkR2djbsdjs2bdqENWvW4Ny5cwAcd1MfOXIkXn31VRQWFsJms+Gjjz7Czp07nfs0vnZb3tfTz+m3jEYjnn32Wdx3330uuSGrlOe1YMECKJVKPP744x0+D085r5KSEhgMBvztb3/D9ddfj++++w6333477rjjDmzfvt1rzwsAlixZgt69eyMxMRFqtRrXX389li5diquuuqrD59VWRUVFzX4PqqurUV9f36rvU1FREdRq9UW/oLji86K9XHFev6XX6/Hqq69i+vTpnVY3+S4GKy+2ePFiZGRkoGfPnlCr1Zg5cyamTZsGufz8H+uqVasghEBCQgI0Gg3effdd3HfffU328SSuPieLxYK7774bQgj84x//cOepNOGK88rMzMTixYuxYsUKyGQyqU6lCVecl91uBwDceuuteOKJJzBw4EA899xzmDRpkssWektxXoAjWO3atQvr169HZmYm3n77bcyYMQPff/+9FKdFrVBdXY2bbroJvXv3xty5c6Uuh7yQZ/7r6oeioqKgUCguurqmuLgYsbGxzR7TpUsXrF27FrW1tTh9+jSOHTuGoKAgdO3a1blPeno6tm/fDoPBgIKCAuzZswcWi8W5T+Nrt+V9Pf2cGjWGqtOnT2PTpk0uGa2S8rx+/PFHlJSUIDk5GUqlEkqlEqdPn8ZTTz2F1NRUrz2vqKgoKJVK9O7du8lr9+rVyyVXBUp1XvX19fjLX/6ChQsX4uabb0b//v0xc+ZM3HPPPXjrrbc6fF5tFRsb2+z3ICQkBDqdrlXfp9jYWJjNZlRWVra4j7u54rwa1dTU4Prrr0dwcDC++uorqFSqTq+ffA+DlYdQq9UYMmQINm/e7Nxmt9uxefNmjBw58pLHarVaJCQkwGq14j//+Q9uvfXWi/YJDAxEXFwcKioq8O233zr3SUtLQ2xsbJP3ra6uxu7duy/7vp56TsD5UJWdnY3vv/8ekZGRHToXTziv+++/HwcPHsSBAwecj/j4eMyePRvffvut156XWq3GsGHDcPz48Sb7nzhxAikpKV57XhaLBRaL5aKRVIVC4Rylc6eRI0c2+R4AwKZNm5zfg9Z8n4YMGQKVStVkn+PHjyM/P7/Dnxft5YrzAhyfexMmTIBarcb69esvWj9H1GoSLpyn3/j000+FRqMRK1asEEePHhXTp08XYWFhoqioSAghxP333y+ee+455/67du0S//nPf0Rubq744YcfxDXXXCPS0tJERUWFc5+NGzeKDRs2iJMnT4rvvvtODBgwQAwfPlyYzWbnPn/7299EWFiYWLdunTh48KC49dZbXdpuwd3nZDabxS233CISExPFgQMHxLlz55wPk8nU4XOS6rya4+qrAqU6rzVr1giVSiXef/99kZ2d7bxk/scff/Tq87r66qtFnz59xNatW8XJkyfF8uXLhVarFX//+987fE41NTVi//79Yv/+/QKAWLhwodi/f7/zytfnnntO3H///c79G9sSzJ49W2RlZYmlS5c225bgUt8nIRztFpKTk8WWLVvE3r17xciRI8XIkSM7fD5SnldVVZUYPny46Nevn8jJyWnymWG1Wl12buQfGKw8zJIlS0RycrJQq9XiiiuuELt27XI+d/XVV4sHH3zQ+fW2bdtEr169hEajEZGRkeL+++8XZ8+ebfJ6n332mejatatQq9UiNjZWzJgxQ1RWVjbZx263ixdffFHExMQIjUYjrr32WnH8+HGvPae8vDwBoNnH1q1bvfa8muPqYCWEdOf1wQcfiG7dugmtVisGDBgg1q5d6/Xnde7cOTF16lQRHx8vtFqt6NGjh3j77beF3W7v8Pls3bq12b/jjefx4IMPiquvvvqiYwYOHCjUarXo2rWrWL58+UWve6nvkxBC1NfXiz/96U8iPDxcBAQEiNtvv12cO3euw+cj5Xm19J4ARF5ensvOjfyDTAgh3Dc+RkREROS7uMaKiIiIyEUYrIiIiIhchMGKiIiIyEUYrIiIiIhchMGKiIiIyEUYrIiIiIhchMGKiIiIyEUYrIiIiIhchMGKiIiIyEUYrIiIiIhchMGKiIiIyEUYrIiIiIhc5P8DR1vkNZWdbrkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(density, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Density\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.5985227325531981\n",
      "Skewness pada distribusi = -0.11093065738249801\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['density'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['density'].mean() - data['density'].median()))/data['density'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pH\n",
    "Distribusi pH bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi pH juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(pH, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of pH\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.13678740824860436\n",
      "Skewness pada distribusi = 0.10326531781094268\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['pH'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['pH'].mean() - data['pH'].median()))/data['pH'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sulphates\n",
    "Distribusi Sulphates bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Sulphates juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 617.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(sulphates, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Sulphates\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.13678740824860436\n",
      "Skewness pada distribusi = 0.10326531781094268\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['pH'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['pH'].mean() - data['pH'].median()))/data['pH'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alcohol\n",
    "Distribusi Alcohol bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Alcohol juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 599.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(alcohol, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Alcohol\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.6790884901361043\n",
      "Skewness pada distribusi = -0.03518884535840057\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['alcohol'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['alcohol'].mean() - data['alcohol'].median()))/data['alcohol'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quality\n",
    "Distribusi Quality bersifat normal secara graphical. Distribusi pada histogram membentuk kurva simetris dengan puncak berada di tengah distribusi. <br>\n",
    "Distribusi Quality juga bersifat normal secara analytical dengan p-value > 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ditra\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\distributions.py:2142: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try histplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 597.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(quality, kde=True, ax=axs[0])\n",
    "plt.suptitle(\"Distribution of Quality\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi Normal dengan p-value = 0.3888139394184818\n",
      "Skewness pada distribusi = -0.13956552038002426\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.normaltest(data['quality'])\n",
    "if(p > 0.05):\n",
    "    print(\"Distribusi Normal\", end='')\n",
    "else:\n",
    "    \n",
    "    print(\"Bukan Distribusi Normal\", end='')\n",
    "print(f\" dengan p-value = {p}\")\n",
    "print(f\"Skewness pada distribusi = {(3 * (data['quality'].mean() - data['quality'].median()))/data['quality'].std()}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Soal 4**\n",
    "Tes hipotesis 1 sampel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 4.a\n",
    "Nilai rata-rata pH di atas 3.29?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keputusan berdasarkan z-value:\n",
      "z-value = 4.1037807933651145\n",
      "z-crit = 1.645\n",
      "H0 ditolak\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 2.032263004325685e-05\n",
      "alpha = 0.05\n",
      "H0 ditolak\n"
     ]
    }
   ],
   "source": [
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: pH = 3.29\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis Alternatif (H1)\n",
    "# H1: pH > 3.29\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan daerah kritis one-tailed\n",
    "z_crit = 1.645\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Hitung nilai t-value dan  p-value\n",
    "z_value = (data['pH'].mean() - 3.29) / (data['pH'].std() / np.sqrt(data['pH'].count()))\n",
    "p_value = 1 - stats.norm.cdf(z_value)\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit = {z_crit}')\n",
    "if z_value > z_crit:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 4.b\n",
    "Nilai rata-rata residual sugar tidak sama dengan 2.5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keputusan berdasarkan z-value:\n",
      "z-value = 2.1479619435539523\n",
      "z-crit-r = 1.96\n",
      "z-crit-l = -1.96\n",
      "H0 ditolak\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.031716778818727365\n",
      "alpha = 0.05\n",
      "H0 ditolak\n"
     ]
    }
   ],
   "source": [
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: Residual Sugar = 2.5\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis Alternatif (H1)\n",
    "# H1: Residual Sugar /= 2.5\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan daerah kritis two-tailed\n",
    "z_crit_r = 1.96\n",
    "z_crit_l = -1.96\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Hitung nilai z-value dan  p-value\n",
    "z_value = (data['residual sugar'].mean() - 2.5) / (data['residual sugar'].std() / np.sqrt(data['residual sugar'].count()))\n",
    "p_value = 2 * (1 - stats.norm.cdf(np.abs(z_value)))\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z_crit_r dan z_crit_l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit-r = {z_crit_r}')\n",
    "print(f'z-crit-l = {z_crit_l}')\n",
    "if z_value < z_crit_l or z_value > z_crit_r:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 4.c\n",
    "Nilai rata-rata 150 baris pertama kolom sulphates bukan 0.65?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keputusan berdasarkan z-value:\n",
      "z-value = -301.9554753801894\n",
      "z_crit_r = -1.96\n",
      "z_crit_l = 1.96\n",
      "H0 ditolak\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.0\n",
      "alpha = 0.05\n",
      "H0 ditolak\n"
     ]
    }
   ],
   "source": [
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: sulphates = 0.65\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis Alternatif (H1)\n",
    "# H1: sulphates /= 2.5\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan daerah kritis two-tailed\n",
    "t_crit_r = 1.96\n",
    "t_crit_l = -1.96\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Hitung nilai z-value dan  p-value\n",
    "z_value = (data['sulphates'].iloc[:150].mean() - 3.29) / (data['sulphates'].iloc[:150].std() / np.sqrt(data['sulphates'].iloc[:150].count()))\n",
    "p_value = 2 * (1 - stats.norm.cdf(np.abs(z_value)))\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit-r dan z-crit-l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z_crit_r = {z_crit_l}')\n",
    "print(f'z_crit_l = {z_crit_r}')\n",
    "if z_value < z_crit_l or z_value > z_crit_r:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 4.d\n",
    "Nilai rata-rata total sulfur dioxide di bawah 35?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keputusan berdasarkan z-value:\n",
      "z-value = 16.786387372296744\n",
      "z-crit = -1.645\n",
      "H0 diterima\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 1.0\n",
      "alpha = 0.05\n",
      "H0 diterima\n"
     ]
    }
   ],
   "source": [
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: pH = 35\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis Alternatif (H1)\n",
    "# H1: pH < 35\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan daerah kritis one-tailed\n",
    "z_crit = -1.645\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Hitung nilai z-value dan  p-value\n",
    "z_value = (data['total sulfur dioxide'].mean() - 35) / (data['total sulfur dioxide'].std() / np.sqrt(data['total sulfur dioxide'].count()))\n",
    "p_value = stats.norm.cdf(z_value)\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit = {z_crit}')\n",
    "if z_value < z_crit:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 4.e\n",
    "Proporsi nilai total Sulfat Dioxide yang lebih dari 40, adalah tidak sama dengan 50% ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keputusan berdasarkan z-value:\n",
      "z-value = 0.7589466384404118\n",
      "z-crit-r = 1.96\n",
      "z-crit-l = -1.96\n",
      "H0 diterima\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.4478844782641116\n",
      "alpha = 0.05\n",
      "H0 diterima\n"
     ]
    }
   ],
   "source": [
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: pH = 0.5\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis Alternatif (H1)\n",
    "# H1: pH /= 0.5\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan daerah kritis two-tailed\n",
    "z_crit_r = 1.96\n",
    "z_crit_l = -1.96\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Hitung nilai z-value dan  p-value\n",
    "z_value = ((data[data['total sulfur dioxide'] > 40]['total sulfur dioxide'].count() / data['total sulfur dioxide'].count()) - 0.5) / np.sqrt(0.5 * (1 - 0.5) / data['total sulfur dioxide'].count())\n",
    "p_value = 2 * (1 - stats.norm.cdf(np.abs(z_value)))\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit-r dan z-crit-l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit-r = {z_crit_r}')\n",
    "print(f'z-crit-l = {z_crit_l}')\n",
    "if z_value < z_crit_l or z_value > z_crit_r:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Soal 5**\n",
    "Tes hipotesis 2 sampel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 5.a\n",
    "Data kolom fixed acidity dibagi 2 sama rata: bagian awal dan bagian akhir kolom.\n",
    "Benarkah rata-rata kedua bagian tersebut sama?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varians equal\n",
      "\n",
      "Keputusan berdasarkan z-value:\n",
      "z-value = 0.02604106999906379\n",
      "z-crit-r = 1.96\n",
      "z-crit-l = -1.96\n",
      "H0 diterima\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.9792245804254096\n",
      "alpha = 0.05\n",
      "H0 diterima\n"
     ]
    }
   ],
   "source": [
    "# === Definisi ==============================================================================\n",
    "n = len(data[\"fixed acidity\"])\n",
    "fa_r = data.loc[:n//2-1, \"fixed acidity\"]\n",
    "fa_l = data.loc[n//2:, \"fixed acidity\"]\n",
    "mean_r = fa_r.mean()\n",
    "mean_l = fa_l.mean()\n",
    "std_r = fa_r.std()\n",
    "std_l = fa_l.std()\n",
    "n_r = fa_r.count()\n",
    "n_l = fa_l.count()\n",
    "alpha = 0.05\n",
    "\n",
    "# Perform F-Test two-tailed\n",
    "if std_r ** 2 > std_l ** 2:\n",
    "    f_value = (std_r ** 2 / std_l ** 2)\n",
    "else:\n",
    "    f_value = (std_l ** 2 / std_r ** 2)\n",
    "f_crit_r = stats.f.ppf(1 - alpha, n_r - 1, n_l - 1)\n",
    "f_crit_l = stats.f.ppf(alpha, n_r - 1, n_l - 1)\n",
    "\n",
    "# Ambil keputusan berdasarkan varians kedua bagian\n",
    "if f_value < f_crit_l or f_value > f_crit_r:\n",
    "    print(\"Varians tidak equal\")\n",
    "else:\n",
    "    print(\"Varians equal\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: mean_r = mean-l\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis 1 (H1)\n",
    "# H0: mean_r /= mean-l\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan batas daerah kritis two-tailed\n",
    "z_crit_r = 1.96\n",
    "z_crit_l = -1.96\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Tentukan nilai statistik dan hitung p-value\n",
    "z_value = (mean_r - mean_l) / np.sqrt(std_r ** 2 / n_r + std_l ** 2 / n_l)\n",
    "p_value = 2 * (1 - stats.norm.cdf(np.abs(z_value)))\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit-r dan z-crit-l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit-r = {z_crit_r}')\n",
    "print(f'z-crit-l = {z_crit_l}')\n",
    "if z_value < z_crit_l or z_value > z_crit_r:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 5.b\n",
    "Data kolom chlorides dibagi 2 sama rata: bagian awal dan bagian akhir kolom. Benarkah\n",
    "rata-rata bagian awal lebih besar daripada bagian akhir sebesar 0.001?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varians equal\n",
      "\n",
      "Keputusan berdasarkan z-value:\n",
      "z-value = -0.4673171228521319\n",
      "z-crit = 1.645\n",
      "H0 diterima\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.6798634962094465\n",
      "alpha = 0.05\n",
      "H0 diterima\n"
     ]
    }
   ],
   "source": [
    "# === Definisi ==============================================================================\n",
    "n = len(data[\"chlorides\"])\n",
    "fa_r = data.loc[:n//2-1, \"chlorides\"]\n",
    "fa_l = data.loc[n//2:, \"chlorides\"]\n",
    "mean_r = fa_r.mean()\n",
    "mean_l = fa_l.mean()\n",
    "std_r = fa_r.std()\n",
    "std_l = fa_l.std()\n",
    "n_r = fa_r.count()\n",
    "n_l = fa_l.count()\n",
    "d0 = 0.001\n",
    "alpha = 0.05\n",
    "\n",
    "# Perform F-Test one-tailed\n",
    "if std_r ** 2 > std_l ** 2:\n",
    "    f_value = (std_r ** 2 / std_l ** 2)\n",
    "else:\n",
    "    f_value = (std_l ** 2 / std_r ** 2)\n",
    "f_crit = stats.f.ppf(1 - alpha, n_r - 1, n_l - 1)\n",
    "\n",
    "# Ambil keputusan berdasarkan varians kedua bagian\n",
    "if f_value > f_crit:\n",
    "    print(\"Varians tidak equal\")\n",
    "else:\n",
    "    print(\"Varians equal\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: mean_r - mean_l = 0.001\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis 1 (H1)\n",
    "# H0: mean_r - mean_l > 0.001\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan batas daerah kritis one-tailed\n",
    "z_crit = 1.645\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Tentukan nilai statistik dan hitung p-value\n",
    "z_value = (mean_r - mean_l - d0) / np.sqrt(std_r ** 2 / n_r + std_l ** 2 / n_l)\n",
    "p_value = 1 - stats.norm.cdf(z_value)\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit-r dan z-crit-l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit = {z_crit}')\n",
    "if z_value > z_crit:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 5.c\n",
    "Benarkah rata-rata sampel 25 baris pertama kolom Volatile Acidity sama dengan\n",
    "rata-rata 25 baris pertama kolom Sulphates ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varians equal\n",
      "\n",
      "Keputusan berdasarkan z-value:\n",
      "z-value = -2.63748216767487\n",
      "z-crit-r = 1.96\n",
      "z-crit-l = -1.96\n",
      "H0 ditolak\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.008352401685453703\n",
      "alpha = 0.05\n",
      "H0 ditolak\n"
     ]
    }
   ],
   "source": [
    "# === Definisi ==============================================================================\n",
    "va = data.loc[:24, \"volatile acidity\"]\n",
    "s = data.loc[:24, \"sulphates\"]\n",
    "va_mean = va.mean()\n",
    "s_mean = s.mean()\n",
    "va_std = va.std()\n",
    "s_std = s.std()\n",
    "va_n = va.count()\n",
    "s_n = s.count()\n",
    "alpha = 0.05\n",
    "\n",
    "# Perform F-Test two-tailed\n",
    "if va_std ** 2 > s_std ** 2:\n",
    "    f_value = (va_std ** 2 / s_std ** 2)\n",
    "else:\n",
    "    f_value = (s_std ** 2 / va_std ** 2)\n",
    "f_crit_r = stats.f.ppf(1 - alpha, va_n - 1, s_n - 1)\n",
    "f_crit_l = stats.f.ppf(alpha, va_n - 1, s_n - 1)\n",
    "\n",
    "# Ambil keputusan berdasarkan varians kedua bagian\n",
    "if f_value < f_crit_l or f_value > f_crit_r:\n",
    "    print(\"Varians tidak equal\")\n",
    "else:\n",
    "    print(\"Varians equal\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: v_mean = s_mean\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis 1 (H1)\n",
    "# H0: v_mean /= s_mean\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan batas daerah kritis two-tailed\n",
    "z_crit_r = 1.96\n",
    "z_crit_l = -1.96\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Tentukan nilai statistik dan hitung p-value\n",
    "z_value = (va_mean - s_mean) / np.sqrt(va_std ** 2 / va_n + s_std ** 2 / s_n)\n",
    "p_value = 2 * (1 - stats.norm.cdf(np.abs(z_value)))\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit-r dan z-crit-l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit-r = {z_crit_r}')\n",
    "print(f'z-crit-l = {z_crit_l}')\n",
    "if z_value < z_crit_l or z_value > z_crit_r:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 5.d\n",
    "Bagian awal kolom residual sugar memiliki variansi yang sama dengan bagian akhirnya?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f-value =  1.0615664973153538\n",
      "f-crit-r =  1.1588265949626437\n",
      "f-crit-l =  0.8629418796107595\n",
      "Varians equal\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# === Definisi ==============================================================================\n",
    "n = len(data[\"residual sugar\"])\n",
    "fa_r = data.loc[:n//2-1, \"residual sugar\"]\n",
    "fa_l = data.loc[n//2:, \"residual sugar\"]\n",
    "mean_r = fa_r.mean()\n",
    "mean_l = fa_l.mean()\n",
    "std_r = fa_r.std()\n",
    "std_l = fa_l.std()\n",
    "n_r = fa_r.count()\n",
    "n_l = fa_l.count()\n",
    "alpha = 0.05\n",
    "\n",
    "# Perform F-Test two-tailed\n",
    "if std_r ** 2 > std_l ** 2:\n",
    "    f_value = (std_r ** 2 / std_l ** 2)\n",
    "else:\n",
    "    f_value = (std_l ** 2 / std_r ** 2)\n",
    "f_crit_r = stats.f.ppf(1 - alpha, n_r - 1, n_l - 1)\n",
    "f_crit_l = stats.f.ppf(alpha, n_r - 1, n_l - 1)\n",
    "\n",
    "# Ambil keputusan berdasarkan varians kedua bagian\n",
    "print(f\"f-value =  {f_value}\")\n",
    "print(f\"f-crit-r =  {f_crit_r}\")\n",
    "print(f\"f-crit-l =  {f_crit_l}\")\n",
    "if f_value < f_crit_l or f_value > f_crit_r:\n",
    "    print(\"Varians tidak equal\")\n",
    "else:\n",
    "    print(\"Varians equal\")\n",
    "print(\"\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Soal 5.e\n",
    "Proporsi nilai setengah bagian awal alcohol yang lebih dari 7, adalah lebih besar\n",
    "daripada, proporsi nilai yang sama di setengah bagian akhir alcohol?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varians equal\n",
      "\n",
      "Keputusan berdasarkan z-value:\n",
      "z-value = 0.0\n",
      "z-crit = 1.645\n",
      "H0 diterima\n",
      "\n",
      "Keputusan berdasarkan p-value:\n",
      "p-value = 0.5\n",
      "alpha = 0.05\n",
      "H0 diterima\n"
     ]
    }
   ],
   "source": [
    "# === Definisi ==============================================================================\n",
    "n = len(data[\"alcohol\"])\n",
    "al_r = data.loc[:n//2-1, \"alcohol\"]\n",
    "al_l = data.loc[n//2:, \"alcohol\"]\n",
    "n_r = al_r.count()\n",
    "n_l = al_l.count()\n",
    "std_r = al_r.std()\n",
    "std_l = al_l.std()\n",
    "p_r = (al_r > 7).sum() / n_r\n",
    "p_l = (al_l > 7).sum() / n_l\n",
    "p = (al_r > 7).sum() / n\n",
    "alpha = 0.005\n",
    "\n",
    "# Perform F-Test one-tailed\n",
    "if std_r ** 2 > std_l ** 2:\n",
    "    f_value = (std_r ** 2 / std_l ** 2)\n",
    "else:\n",
    "    f_value = (std_l ** 2 / std_r ** 2)\n",
    "f_crit = stats.f.ppf(1 - alpha, n_r - 1, n_l - 1)\n",
    "\n",
    "# Ambil keputusan berdasarkan varians kedua bagian\n",
    "if f_value > f_crit:\n",
    "    print(\"Varians tidak equal\")\n",
    "else:\n",
    "    print(\"Varians equal\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# === Langkah 1 =============================================================================\n",
    "# Tentukan Hipotesis 0 (H0)\n",
    "# H0: p_r = p_l\n",
    "\n",
    "\n",
    "# === Langkah 2 =============================================================================\n",
    "# Tentukan Hipotesis 1 (H1)\n",
    "# H0: p_r > p_2\n",
    "\n",
    "\n",
    "# === Langkah 3 =============================================================================\n",
    "# Tentukan tingkat signifikan alpha yaitu 5%\n",
    "alpha = 0.05\n",
    "\n",
    "\n",
    "# === Langkah 4 =============================================================================\n",
    "# Tentukan batas daerah kritis one-tailed\n",
    "z_crit = 1.645\n",
    "\n",
    "\n",
    "# === Langkah 5 =============================================================================\n",
    "# Tentukan nilai statistik dan hitung p-value\n",
    "z_value = (p_r - p_l) / np.sqrt(p * (1 - p) * (1/n_r + 1/n_l))\n",
    "p_value = 1 - stats.norm.cdf(z_value)\n",
    "\n",
    "\n",
    "# === Langkah 6 =============================================================================\n",
    "# Ambil keputusan berdasarkan z-value dengan z-crit-r dan z-crit-l dan p-value dengan alpha\n",
    "print('Keputusan berdasarkan z-value:')\n",
    "print(f'z-value = {z_value}')\n",
    "print(f'z-crit = {z_crit}')\n",
    "if z_value > z_crit:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Keputusan berdasarkan p-value:')\n",
    "print(f'p-value = {p_value}')\n",
    "print(f'alpha = {alpha}')\n",
    "if p_value < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 diterima')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referensi\n",
    "\n",
    "- https://www.liputan6.com/hot/read/4470780/mean-adalah-nilai-rata-rata-ketahui-rumus-contoh-dan-jenisnya#:~:text=Dalam%20bahasa%20Indonesia%2C%20mean%20juga,mudah%20dihitung%20dengan%20rumus%20sederhana\n",
    "\n",
    "- https://dailysocial.id/post/median-pengertian-contoh-rumus-dan-cara-menghitungnya\n",
    "\n",
    "- https://www.merdeka.com/jateng/modus-adalah-nilai-data-dalam-statistik-ketahui-jenis-dan-cara-menghitungnya-kln.html\n",
    "\n",
    "- https://money.kompas.com/read/2021/06/23/150642926/standar-deviasi-adalah-perhitungan-statistik-simak-rumus-dan-kegunaannya\n",
    "\n",
    "- https://www.akseleran.co.id/blog/rumus-varians/\n",
    "\n",
    "- https://akupintar.id/info-pintar/-/blogs/simpangan-kuartil-pengertian-rumus-jangkauan-antar-kuartil-dan-contoh-soal#:~:text=Kuartil%20adalah%20nilai%20yang%20membagi,dan%20kuartil%20atas%20(Q3)\n",
    "\n",
    "- https://dt-sense.com/2021/09/26/belajar-mengenai-ukuran-letak-data/\n",
    "\n",
    "- https://www.editage.com/blog/normality-test-methods-of-assessing-normality/\n",
    "\n",
    "- https://support.minitab.com/en-us/minitab/21/help-and-how-to/statistics/basic-statistics/how-to/normality-test/interpret-the-results/key-results/#:~:text=If%20the%20p%2Dvalue%20is,not%20follow%20a%20normal%20distribution.&text=If%20the%20p%2Dvalue%20is%20larger%20than%20the%20significance%20level,to%20reject%20the%20null%20hypothesis.\n",
    "\n",
    "- R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability & Statistics for Engineers & Scientists, 9th ed. London: Pearson Education, 2016, p. 791."
   ]
  }
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