Question

You have been asked to evaluate a regression model based on R², adjusted R² and tolerance. What will be your criteria?

Answer 1

When evaluating a regression model, you can use R², adjusted R², and tolerance as criteria. R² represents the proportion of variation in the dependent variable explained by the independent variable(s), adjusted R² accounts for the number of predictors, and tolerance measures collinearity between predictors.

Step-by-step explanation:

Evaluating a Regression Model

When evaluating a regression model, there are several criteria you can use:

1. R²: Also known as the coefficient of determination, this measure represents the proportion of the variation in the dependent variable that can be explained by the independent variable(s) in the regression model. A higher R² indicates a better fit of the model.
2. Adjusted R²: This measure adjusts the R² value for the number of predictors in the model. It accounts for the possibility of overfitting by penalizing models with too many predictors.
3. Tolerance: Tolerance measures the collinearity (correlation) between independent variables in the model. A tolerance below 0.10 indicates high collinearity and can affect the reliability of the regression estimates.