This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.
This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.
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.
Inhaltsangabe
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
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
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