```
require(mosaic)
require(car)
```

Researchers observed the following data on 20 individuals with high blood pressure:

- blood pressure (
`BP`

, in mm Hg) - age (
`Age`

, in years) - weight (
`Weight`

, in kg) - body surface area (
`BSA`

, in`m^2`

) - duration of hypertension (
`Dur`

, in years) - basal pulse (
`Pulse`

, in beats per minute) - stress index (
`Stress`

)

Our goal is to build a model for blood pressure as a function of (some subset) of the other variables. In this case all of our variables are quantitative, so we can get a quick look at their relationships using the a pairs plot.

```
BP <- read.csv("http://www.math.smith.edu/~bbaumer/mth247/labs/bloodpress.csv")
pairs(BP)
```

```
# Better than the standard pairs plot is the generalized pairs plot.
#install.packages("gpairs")
#gpairs(BP)
```

- What do you see in these scatterplots? Which of the variables are most highly correlated with
`BP`

?

Hint: use

`cor`

to calculate the correlation coefficient matrix.

`Weight`

seems to be highly correlated with `BP`

, so as a first step, we should understand how well a simple linear model for blood pressure as a function of weight works. Keep in mind that it accords with our intuition that there would be a strong link between a person’s weight and their blood pressure.

`xyplot(BP ~ Weight, data=BP, pch=19, alpha=0.5, cex=1.5, type=c("p", "r"))`