## Ch 4: Scatterplots and Correlation

In a random sample of 8 college women, each was asked her height (inch) and her weight (pound).

Heights, x Weights, y College women's heights and weights 65 105 65 125 62 110 67 120 69 140 65 135 61 95 67 130

### Scatterplots

• Draw a scatterplot.

```> x <- c(65,65,62,67,69,65,61,67)
> y <- c(105,125,110,120,140,135,95,130)
> plot(x,y, col="red", pch=15)
> plot(y~x, col="red", pch=15)
```

Two plot commands above will produce identical results. In the plot commands, col="red" defines the color of dots and pch=15 defines symbol. Consult this for a list of symbols. For a chart of color, look at this.

### Pearson Correlation Coefficient, r

• Linear correlation coefficient between x (height) and y (weight).

```> cor(x,y)
[1] 0.7979212
```
```> cor.test(x,y)

Pearson's product-moment correlation

data:  x and y
t = 3.2426, df = 6, p-value = 0.01763
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.2130285 0.9617997
sample estimates:
cor
0.7979212
```

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