Multiple Regression: 2014 Edition (Statistical Associates Blue Book Series 6)
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Multiple Regression: 2014 Edition (Statistical Associates Blue Book Series 6)
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 these:
* 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 contents: Overview13 Data examples in this volume16 Key Terms and Concepts17 OLS estimation17 The regression equation18 Dependent variable20 Independent variables21 Dummy variables21 Interaction effects22 Interactions22 Centering23 Significance of interaction effects23 Interaction terms with categorical dummies24 Plotting interactions through simple slope analysis24 Separate regressions27 Predicted values28 SPSS28 SAS28 Stata29 Adjusted predicted values30 Residuals31 Centering31 OLS regression in SPSS32 Example32 SPSS input32 SPSS Output33 The regression coefficient, b33 Interpreting b for dummy variables34 Confidence limits on b35 Beta weights35 Zero-order, partial, and part correlations36 R2 and the “Model Summary†table39 The Anova table40 Tolerance and VIF collinearity statistics40 SPSS plots41 SPSS “Plots†dialog41 Plot of standardized residuals against standardized predicted values43 Histogram of standardized residuals44 Normal probability (P-P) plot45 OLS regression in SAS46 Example46 SAS input47 SAS output48 The regression coefficient, b48 Interpreting b for dummy variables49 Confidence limits on b49 Beta weights50 Zero order, partial, and part correlation52 R-Squared and the Anova table53 Tolerance and VIF collinearity statistics54 SAS Plots55 SAS plotting options55 Plot of residuals against predicted values57 Histogram and kernel density plot of standardized residuals58 Normal probability (P-P) plot59 Normal quantile-quantile (Q-Q) plot60 Other SAS plots61 OLS regression in Stata64 Example64 Stata input65 Stata output66 The regression coefficient, b66 Interpreting b coefficients67 Confidence limits on b68 Beta weights68 R-Squared and the Anova table68 Zero order, partial, and part correlation69 Tolerance and VIF collinearity statistics69 Other Stata postestimation output70 Stata Plots71 Stata plotting options71 Plot of standardized residuals against standardized predicted values71 Histogram of standardized residuals73 Normal probability (P-P) plot74 Margin plots75 Robust regression75 Overview75 When to use robust regression76 Robust regression in SPSS76 Overview76 SPSS input77 SPSS output77 Robust regression in SAS78 SAS input78 SAS output80 Robust regression in Stata81 Stata input81 Stata output81 Hierarchical multiple regression82 Overview82 Examples83 Difference in differences regression83 Overview83 The parallel trend assumption84 Example data85 Data setup86 The model86 Difference modeling in SPSS89 SPSS input89 Should the dependent variable be linear or logarithmic?90 SPSS output92 Difference modeling in SAS94 SAS input94 Should the dependent variable be linear or logarithmic?95 SAS output96 Difference modeling in Stata98 Stata input98 Should the dependent variable be linear or logarithmic?98 Stata output99 Panel data regression101 Overview101 Types of panel data regression1