# 12.2.2.2 - Example: Body Correlation Matrix

12.2.2.2 - Example: Body Correlation Matrix

This 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

Correlations
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.

  Link ↥ Has Tooltip/Popover Toggleable Visibility