2201 fixed errors

_site/materials/glm-practical-logistic-binary.html

@@ -603,7 +603,7 @@ <h1 class="title">
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-<figure class="figure"><p><img src="images/dgplots/2024_01_22-11-16-01_AM_dgplots.png" class="img-fluid figure-img" width="804"></p>
+<figure class="figure"><p><img src="images/dgplots/2024_01_22-03-59-55_PM_dgplots.png" class="img-fluid figure-img" width="805"></p>
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@@ -749,7 +749,7 @@ <h1 class="title">
 Link Function:                  Logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -4.5939
 Date:                Mon, 22 Jan 2024   Deviance:                       9.1879
-Time:                        11:16:02   Pearson chi2:                     15.1
+Time:                        15:59:56   Pearson chi2:                     15.1
 No. Iterations:                     8   Pseudo R-squ. (CS):             0.7093
 Covariance Type:            nonrobust                                         
 ==============================================================================
@@ -1212,7 +1212,7 @@ <h1 class="title">
 Link Function:                  Logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -376.10
 Date:                Mon, 22 Jan 2024   Deviance:                       752.20
-Time:                        11:16:06   Pearson chi2:                     713.
+Time:                        16:00:00   Pearson chi2:                     713.
 No. Iterations:                     4   Pseudo R-squ. (CS):             0.2238
 Covariance Type:            nonrobust                                         
 ==============================================================================
@@ -1239,23 +1239,23 @@ <h1 class="title">
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-<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Log.html">exp</a></span><span class="op">(</span><span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="fl">150</span><span class="op">)</span> <span class="op">/</span> <span class="op">(</span><span class="fl">1</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">exp</a></span><span class="op">(</span><span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="fl">145</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Log.html">exp</a></span><span class="op">(</span><span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="fl">150</span><span class="op">)</span> <span class="op">/</span> <span class="op">(</span><span class="fl">1</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">exp</a></span><span class="op">(</span><span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="fl">150</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-<pre><code>[1] 0.6685441</code></pre>
+<pre><code>[1] 0.5962827</code></pre>
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-<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>math.exp(<span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="dv">150</span>) <span class="op">/</span> (<span class="dv">1</span> <span class="op">+</span> math.exp(<span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="dv">145</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>math.exp(<span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="dv">150</span>) <span class="op">/</span> (<span class="dv">1</span> <span class="op">+</span> math.exp(<span class="op">-</span><span class="fl">5.61</span> <span class="op">+</span> <span class="fl">0.04</span> <span class="op">*</span> <span class="dv">150</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-<pre><code>0.6685441044999503</code></pre>
+<pre><code>0.5962826992967878</code></pre>
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-<p>This tells us that the probability of having a positive diabetes test result, given a glucose tolerance level of 150 is around 67%.</p>
+<p>This tells us that the probability of having a positive diabetes test result, given a glucose tolerance level of 150 is around 60 %.</p>
 </section>
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materials/glm-practical-logistic-binary.qmd

@@ -747,17 +747,17 @@ Using the formula above, we can now calculate the probability of having a positi
 ## R
 
 ```{r}
-exp(-5.61 + 0.04 * 150) / (1 + exp(-5.61 + 0.04 * 145))
+exp(-5.61 + 0.04 * 150) / (1 + exp(-5.61 + 0.04 * 150))
 ```
 
 ## Python
 
 ```{python}
-math.exp(-5.61 + 0.04 * 150) / (1 + math.exp(-5.61 + 0.04 * 145))
+math.exp(-5.61 + 0.04 * 150) / (1 + math.exp(-5.61 + 0.04 * 150))
 ```
 :::
 
-This tells us that the probability of having a positive diabetes test result, given a glucose tolerance level of 150 is around 67%.
+This tells us that the probability of having a positive diabetes test result, given a glucose tolerance level of 150 is around `r round(exp(-5.61 + 0.04 * 150) / (1 + exp(-5.61 + 0.04 * 150)) * 100)` %.
 
 :::
 :::

materials/glm-practical-logistic-proportion.qmd

@@ -254,7 +254,7 @@ Before we can make any firm conclusions, though, we need to check our model:
 1- pchisq(12.633,20)
 ```
 
-We get quite a high score (around 0.9) for this, which tells us that our goodness of fit is pretty good – our points are quite close to our curve, overall.
+We get quite a high score (around 0.9) for this, which tells us that our goodness of fit is pretty rubbish – our points are not very close to our curve, overall.
 
 Is the model any better than the null though?
 

materials/glm-practical-poisson.qmd

@@ -181,7 +181,7 @@ To assess if the model is any good we’ll again use the residual deviance and t
 
 :::
 
-This gives a probability of `0.60`. This suggests that this model is actually a good one and that the data are pretty well supported by the model. For Poisson models this has an extra interpretation. This can be used to assess whether we have significant over-dispersion in our data.
+This gives a probability of `0.60`. This suggests that this model is actually a reasonably decent one and that the data are pretty well supported by the model. For Poisson models this has an extra interpretation. This can be used to assess whether we have significant over-dispersion in our data.
 
 For a Poisson model to be appropriate we need that the variance of the data to be exactly the same as the mean of the data. Visually, this would correspond to the data spreading out more for higher predicted values of `species.` However, we don’t want the data to spread out too much. If that happens then a Poisson model wouldn’t be appropriate.
 
@@ -317,6 +317,7 @@ Is the model well-specified?
 1 - pchisq(850.41, 190)
 ```
 
+This value indicates that the model is actually pretty good. Remember, it is between $[0, 1]$ and the closer to zero, the better the model.
 :::
 
 How about the overall fit?
@@ -328,6 +329,8 @@ How about the overall fit?
 1 - pchisq(984.50 - 850.41, 191 - 190)
 ```
 
+Again, this indicates that the model is markedly better than the null model.
+
 :::
 
 #### Plotting the regression