Model Summary

Table showing R, R-square, adjusted R-square, and standard error of the estimate — Figure 1. Model summary for standard linear regression

The standard approach for describing the relationships in this problem is linear regression. The most common measure of how well a regression model fits the data is R ². This statistic represents how much of the variance in the response is explained by the weighted combination of predictors. The closer R ² is to 1, the better the model fits. Regressing Preference on the five predictors results in an R ² of 0.707, indicating that approximately 71% of the variance in the preference rankings is explained by the predictor variables in the linear regression.