Use the matrix plot to examine the relationships between two continuous variables. Also, look for outliers in the relationships. Outliers can heavily influence the results for the Pearson correlation coefficient.
Determine whether the relationships are linear, monotonic, or neither. The following are examples of the types of forms that the correlation coefficients describe. The Pearson correlation coefficient is appropriate for linear forms. Spearman's correlation coefficient is appropriate for monotonic forms.
Use the Pearson correlation coefficient to examine the strength and direction of the linear relationship between two continuous variables.
The correlation coefficient can range in value from −1 to +1. The larger the absolute value of the coefficient, the stronger the relationship between the variables.
For the Pearson correlation, an absolute value of 1 indicates a perfect linear relationship. A correlation close to 0 indicates no linear relationship between the variables.The sign of the coefficient indicates the direction of the relationship. If both variables tend to increase or decrease together, the coefficient is positive, and the line that represents the correlation slopes upward. If one variable tends to increase as the other decreases, the coefficient is negative, and the line that represents the correlation slopes downward.
Correlation type | Pearson |
---|---|
Number of rows used | 30 |
Age | Residence | Employ | Savings | Debt | |
---|---|---|---|---|---|
Residence | 0.838 | ||||
Employ | 0.848 | 0.952 | |||
Savings | 0.552 | 0.570 | 0.539 | ||
Debt | 0.032 | 0.186 | 0.247 | -0.393 | |
Credit cards | -0.130 | 0.053 | 0.023 | -0.410 | 0.474 |