Table of Contents

1. Introduction
2. Review of Elementary Statistical Concepts 
3. Simple Linear Regression Models 
4. Multiple Linear Regression Models 
5. The ANOVA Table and Goodness-of-Fit Statistics 
6. Comparing Linear Regression Models 
7. Indicator Variables in Linear Regression Models 
8. Independence 
9. Homoscedasticity 
10. Collinearity and Multicollinearity 
11. Normality, Linearity, and Interaction Effects 
12. Model Specification 
13. Measurement Errors 
14. Influential Observations: Leverage Points and Outliers 
15. Multilevel Linear Regression Models 
16. A Brief Introduction to Logistic Regression 
17. Conclusions 
Appendix A: Data Management 
Appendix B: Using Simulations to Examine Assumptions of Linear Regression Models 
Appendix C: Formulas 
Appendix C:  User-Written R Packages Employed in Examples

About the Author

John P. Hoffmann is a professor of sociology at Brigham Young University. He holds a PhD in Criminology from the State University of New York at Albany and a Masters of Public Health (MPH) from Emory University. He has worked at the U.S. Centers for Disease Control and Prevention (CDC) and the National Opinion Research Center (NORC) of the University of Chicago; and taught at Hokkaido University and the University of South Carolina. Hoffmann is the author of more than 100 journal articles and book chapters and 10 books on applied statistics, criminology, and the sociology of religion.