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
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.