## Ch 4: Scatterplots and Correlation

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

College women's heights and weights | |

Heights, x |
Weights, y |
---|---|

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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