# 12.2.2.2 - Example: Body Correlation Matrix

12.2.2.2 - Example: Body Correlation MatrixThis correlation matrix was constructed using the body dataset. These data are from the *Journal of Statistics Education* data archive.

Six variables were used: age, weight (kg), height (cm), hip girth, abdominal girth, and wrist girth.

Cell contents grouped by Age, Weight, Height, Hip Girth, and Abdominal Girth; First row: Pearson correlation, Following row: P-Value

Cell contents grouped by Age, Weight, Height, Hip Girth, and Abdominal Girth; First row: Pearson correlation, Following row: P-Value

Age | Weight (kg) | Height (cm) | Hip Girth | Abdominal Girth | |
---|---|---|---|---|---|

Weight (kg) | 0.207265 | ||||

<0.0001 | |||||

Height (cm) | 0.067883 | 0.717301 | |||

0.1269 | <0.0001 | ||||

Hip Girth | 0.227080 | 0.762969 | 0.338584 | ||

<0.0001 | <0.0001 | <0.0001 | |||

Abdominal Girth | 0.422188 | 0.711816 | 0.313197 | 0.825892 | |

<0.0001 | <0.0001 | <0.0001 | <0.0001 | ||

Wrist Girth | 0.192024 | 0.816488 | 0.690834 | 0.458857 | 0.435420 |

<0.0001 | <0.0001 | <0.0001 | <0.0001 | <0.0001 |

This correlation matrix presents 15 different correlations. For each of the 15 pairs of variables, the top box contains the Pearson's r correlation coefficient and the bottom box contains the p value.

The correlation between age and weight is \(r=0.207265\). This correlation is statistically significant (\(p<0.0001\)). That is, there is evidence of a relationship between age and weight in the population.

The correlation between age and height is \(r=0.0678863\). This correlation is not statistically significant (\(p=0.1269\)). There is not evidence of a relationship between age and height in the population.

The correlation between weight and height is \(r=0.717301\). This correlation is statistically significant (\(p<0.0001\)). That is, there is evidence of a relationship between weight and height in the population.

And so on.