Multiple Regression: 2014 Edition (Statistical Associates Blue Book Series 6)

Multiple Regression: 2014 Edition (Statistical Associates Blue Book Series 6)

MULTIPLE REGRESSION<br /><br />An illustrated tutorial and introduction to multiple linear regression analysis using SPSS, SAS, or Stata. Suitable for introductory graduate-level study.<br /><br />The 2014 edition is a major update to the 2012 edition. Among the new features are these:<br /><br />* Now includes worked examples for SPSS, SAS, and Stata.<br />* Was 180 pages with 70 illustrations, now 410 pages with over 300 illustrations.<br />* Thoroughly revised and updated throughout.<br />* Now covers quantile regression, needed for heterosccedastic models<br />* Now covers difference in differences regression.<br />* Now covers robust regression (not just regression w/ robust standard errors)<br />* Greatly expanded coverage of residual analysis.<br />* Greatly expanded coverage of model selection regression<br />* New section on plotting interactions through simple slope analysis<br />* Links to all datasets used in the text. <br /><br />Partial table of contents:<br />Overview13<br />Data examples in this volume16<br />Key Terms and Concepts17<br />OLS estimation17<br />The regression equation18<br />Dependent variable20<br />Independent variables21<br />Dummy variables21<br />Interaction effects22<br />Interactions22<br />Centering23<br />Significance of interaction effects23<br />Interaction terms with categorical dummies24<br />Plotting interactions through simple slope analysis24<br />Separate regressions27<br />Predicted values28<br />SPSS28<br />SAS28<br />Stata29<br />Adjusted predicted values30<br />Residuals31<br />Centering31<br />OLS regression in SPSS32<br />Example32<br />SPSS input32<br />SPSS Output33<br />The regression coefficient, b33<br />Interpreting b for dummy variables34<br />Confidence limits on b35<br />Beta weights35<br />Zero-order, partial, and part correlations36<br />R2 and the “Model Summary” table39<br />The Anova table40<br />Tolerance and VIF collinearity statistics40<br />SPSS plots41<br />SPSS “Plots” dialog41<br />Plot of standardized residuals against standardized predicted values43<br />Histogram of standardized residuals44<br />Normal probability (P-P) plot45<br />OLS regression in SAS46<br />Example46<br />SAS input47<br />SAS output48<br />The regression coefficient, b48<br />Interpreting b for dummy variables49<br />Confidence limits on b49<br />Beta weights50<br />Zero order, partial, and part correlation52<br />R-Squared and the Anova table53<br />Tolerance and VIF collinearity statistics54<br />SAS Plots55<br />SAS plotting options55<br />Plot of residuals against predicted values57<br />Histogram and kernel density plot of standardized residuals58<br />Normal probability (P-P) plot59<br />Normal quantile-quantile (Q-Q) plot60<br />Other SAS plots61<br />OLS regression in Stata64<br />Example64<br />Stata input65<br />Stata output66<br />The regression coefficient, b66<br />Interpreting b coefficients67<br />Confidence limits on b68<br />Beta weights68<br />R-Squared and the Anova table68<br />Zero order, partial, and part correlation69<br />Tolerance and VIF collinearity statistics69<br />Other Stata postestimation output70<br />Stata Plots71<br />Stata plotting options71<br />Plot of standardized residuals against standardized predicted values71<br />Histogram of standardized residuals73<br />Normal probability (P-P) plot74<br />Margin plots75<br />Robust regression75<br />Overview75<br />When to use robust regression76<br />Robust regression in SPSS76<br />Overview76<br />SPSS input77<br />SPSS output77<br />Robust regression in SAS78<br />SAS input78<br />SAS output80<br />Robust regression in Stata81<br />Stata input81<br />Stata output81<br />Hierarchical multiple regression82<br />Overview82<br />Examples83<br />Difference in differences regression83<br />Overview83<br />The parallel trend assumption84<br />Example data85<br />Data setup86<br />The model86<br />Difference modeling in SPSS89<br />SPSS input89<br />Should the dependent variable be linear or logarithmic?90<br />SPSS output92<br />Difference modeling in SAS94<br />SAS input94<br />Should the dependent variable be linear or logarithmic?95<br />SAS output96<br />Difference modeling in Stata98<br />Stata input98<br />Should the dependent variable be linear or logarithmic?98<br />Stata output99<br />Panel data regression101<br />Overview101<br />Types of panel data regression1