1. Multiple Regression Lab Chapter 10 1

2. Topics Multiple Linear Regression Effects Levels of Measurement Dummy Variables 2

3. Multiple Linear Regression 3

4. Output from Regressing INCOME86 on EDUC, AGE, and SEX

5. Model Summary Multiple correlation coefficient R – Correlation of all IV’s with DV – Report strength Coefficient of determination Adj. R 2 – % of explained variability in DV by all IV’s

6. ANOVA Table - Size of F and p-value (sig.) indicate significance of overall model - Large F, small p-value (<.05 or.01) is a significant model

7. Coefficients Table Similar to bivariate regression – unstandardized coefficients – regression equation

8. Multiple Linear Regression (cont.) standardized coefficients (betas) – net effects – indicate direction and strength 8

9. Net Effects Interpretation – “What effect does this variable have separate from the effects of the other independent variables?” – a way to statistically control for other variables in the model A potential problem: multicollinearity (more on this in lecture) 9

10. Levels of Measurement Can it be used in linear regression as Dependent variableIndependent variable Interval/ratio variable yes Ordinal variable Yes (but with caution) yes Dichotomynoyes Nominal variable (with three or more attributes) no Maybe (as series of dummy variables) 10

11. Dummy Variables a way of getting nominal variables with 3 or more attributes into regression as independent variables conversion into a series of dichotomies enter all but one of the dichotomies