class2.Rmd

```{r global_options, include=FALSE}
library(knitr)
opts_chunk$set(fig.align="center", fig.height=4, fig.width=4)
```
##In-class worksheet 2
**Jan 21, 2016**

## 1. t test

We will try the t test on the built-in data set `PlantGrowth`. However, first we need to reformat the data set, which we do with the function `unstack()`. We store the reformatted data set in a variable `plants`:

```{r}
head(PlantGrowth)
plants <- unstack(PlantGrowth)
head(plants)
```

The data set contains plant growth yield (dry weight) under one control and two treatment conditions:
```{r}
boxplot(plants)
```

**Question:** Is the mean control weight significantly different from the mean weight under treatment 1? Is the mean weight under treatment 1 significantly different from the mean weight under treatment 2? Use the function `t.test()` to find out.

```{r}
# R code goes here.
```

## 2. Correlation

We will try the correlation test on the built-in data set `cars`. The data set contains the speed of cars and the distances taken to stop, measured in the 1920s:

```{r}
head(cars)
```

Is there a relationship between speed and stopping distance? Use the function `cor.test()` to find out. Then make a scatterplot of speed vs. stopping distance, using the function `plot()`.

```{r}
# R code goes here.
```

## 3. Regression

We will do a regression analysis on the data set `cabbages` from the R package MASS. The data set contains the weight (`HeadWt`), vitamin C content (`VitC`), the cultivar (`Cult`), and the planting date (`Date`) for 60 cabbage heads:

```{r}
library(MASS) # load the MASS library to make the data set available
head(cabbages)
```

Use a multivariate regression to find out whether weight and cultivar have an effect on the vitamin C content. You will need to use the functions `lm()` and `summary()`.

```{r}
# R code goes here.
```

## 4. If this was easy

Look into the function `predict()`. Can you use it to estimate the vitamin C content of a c52 cultivar with a weight of 4? Can you use it to calculate the residuals of the regression model?

```{r}
# R code goes here.
```