Simple and Multiple Linear Regression

Question 1

Regression

Answer 1

Examine how a variable(s) predicts another variable(s)

Question 2

Difference between simple and multiple linear regression

Answer 2

-Simple- 1 DV is predicted by 1 IV (predictor)
-Multiple- 1 DV is predicted by multiple IV (predictors)

Question 3

Parameters

Answer 3

-Slope coefficient- for every 1 unit increase in X there is a — unit increase in Y
-Intercept- predicted value of Y when X=0

Question 4

Assumptions

Answer 4

-Linearity
-For each IV the correlation with the error term is 0
-Variables are measured without error
-No multivariate outliers
-No multicollinearity
-Independence
-Errors are normally distributed

Question 5

Effect size

Answer 5

How much variance in the DV is explained by predictors