Lecture 21: Multiple Regression

Question 1

How many predictor variables does the multiple regression have

Answer 1

2

Question 2

Between which values does the regression coefficient range

Answer 2

• infinity and + infinity

Question 3

What does the R^2 stand for

Answer 3

The proportion of explained variance (= variance in outcome variable that is explained by the predictor variable)

Question 4

What are the 4 most important assumptions

Answer 4

1. Sensitivity; there is a certain sensitivity of the results to outliers - influence of outliers
2. Homoscedasticity; variance of residuals should be equal across all expected values (look at scatterplot of standardized: predicted values x residuals)
3. Linearity; continuous dependent and predictor variables are linearly related
4. Multicollinearity; predictor variables should not be too highly correlated (not too high linear correlation)

Question 5

what are the VIF and Tolerance boundaries for the multicollinearity assumption

Answer 5

VIF; max. < 10, mean < 1
Tolerance; > 0.2 = good