Regression analysis in R : a comprehensive view for the social sciences Chapter 1. Introduction Relationships and the Issue of CausalityChapter 2. Describing Simple Relationships2.1 Pearson Correlations2.1.1Computation2.1.2 R Examples2.2 Extensions of the Pearson Correlation2.2.1 Point Bi Serial Correlation2.2.2 Phi Coefficient2.2.3 Spearman RhoEnd of Chapter Comprehension ExercisesChapter 3. Linear Regression Analysis3.1 Simple Linear Regression3.1.1 Equations3.1.2 Model Fit Statistics3.1.3 Significance Tests3.2 Multiple Linear Regression3.3 R ExamplesEnd of Chapter Comprehension ExercisesChapter 4. Regression Assumptions and Interpretational Considerations4.1 Statistical Assumptions4.2 Theoretical Assumptions4.3 Interpretational Considerations4.3.1Multicollinearity4.3.2 Restriction of Range4.3.3 VariabilityEnd of Chapter Comprehension ExercisesChapter 5. Dummy Variables and Interactions5.1 Dummy coding5.1.1 Dummy codes for 3 or more levels5.1.2 Interpretation Examples5.2 Interactions5.2.1 Creating Interactions5.2.2 Mean Centering for Interactions5.2.3 Interpretation ExamplesEnd of Chapter Comprehension ExercisesChapter 6. Hierarchical Regression6.1 Types of Hierarchical Regression6.2 Model Comparison Statistics6.3 R ExamplesEnd of Chapter Comprehension ExercisesChapter 7. Moderation and Mediation7.1 Moderation7.2 Mediation7.2.1 Baron and Kenny7.2.2 Tests of Indirect effectsEnd of Chapter Comprehension ExercisesChapter 8. Dealing with Non Linearity8.1 Transformations8.2 Non Linear Terms8.3 Overfitting cross validationEnd of Chapter Comprehension ExercisesChapter 9. Regression Models for Nested Data9.1 Fixed Effects Modeling9.2 Hierarchical Linear ModelingEnd of Chapter Comprehension ExercisesAppendix ABasic R UseAppendix BExercise Answers