Multiple Regression: 2014 Edition

by G. David Garson

MULTIPLE REGRESSION An illustrated tutorial and introduction to multiple linear regression analysis using SPSS, SAS, or Stata. Suitable for introductory graduate-level study. The 2014 edition is a major update to the 2012 edition. Among the new features are * Now includes worked examples for SPSS, SAS, and Stata. * Was 180 pages with 70 illustrations, now 410 pages with over 300 illustrations. * Thoroughly revised and updated throughout. * Now covers quantile regression, needed for heterosccedastic models * Now covers difference in differences regression. * Now covers robust regression (not just regression w/ robust standard errors) * Greatly expanded coverage of residual analysis. * Greatly expanded coverage of model selection regression * New section on plotting interactions through simple slope analysis * Links to all datasets used in the text. Partial table of Overview 13 Data examples in this volume 16 Key Terms and Concepts 17 OLS estimation 17 The regression equation 18 Dependent variable 20 Independent variables 21 Dummy variables 21 Interaction effects 22 Interactions 22 Centering 23 Significance of interaction effects 23 Interaction terms with categorical dummies 24 Plotting interactions through simple slope analysis 24 Separate regressions 27 Predicted values 28 SPSS 28 SAS 28 Stata 29 Adjusted predicted values 30 Residuals 31 Centering 31 OLS regression in SPSS 32 Example 32 SPSS input 32 SPSS Output 33 The regression coefficient, b 33 Interpreting b for dummy variables 34 Confidence limits on b 35 Beta weights 35 Zero-order, partial, and part correlations 36 R2 and the “Model Summary” table 39 The Anova table 40 Tolerance and VIF collinearity statistics 40 SPSS plots 41 SPSS “Plots” dialog 41 Plot of standardized residuals against standardized predicted values 43 Histogram of standardized residuals 44 Normal probability (P-P) plot 45 OLS regression in SAS 46 Example 46 SAS input 47 SAS output 48 The regression coefficient, b 48 Interpreting b for dummy variables 49 Confidence limits on b 49 Beta weights 50 Zero order, partial, and part correlation 52 R-Squared and the Anova table 53 Tolerance and VIF collinearity statistics 54 SAS Plots 55 SAS plotting options 55 Plot of residuals against predicted values 57 Histogram and kernel density plot of standardized residuals 58 Normal probability (P-P) plot 59 Normal quantile-quantile (Q-Q) plot 60 Other SAS plots 61 OLS regression in Stata 64 Example 64 Stata input 65 Stata output 66 The regression coefficient, b 66 Interpreting b coefficients 67 Confidence limits on b 68 Beta weights 68 R-Squared and the Anova table 68 Zero order, partial, and part correlation 69 Tolerance and VIF collinearity statistics 69 Other Stata postestimation output 70 Stata Plots 71 Stata plotting options 71 Plot of standardized residuals against standardized predicted values 71 Histogram of standardized residuals 73 Normal probability (P-P) plot 74 Margin plots 75 Robust regression 75 Overview 75 When to use robust re

490 pages, Kindle Edition

First published May 1, 2012