Linear regression models : applications in R
Author(s)
Bibliographic Information
Linear regression models : applications in R
（Statistics in the social and behavioral sciences series）
CRC Press, 2022
- : pbk
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Note
Includes bibliographical references (p. 399-410) and index
Description and Table of Contents
Description
*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied.
*Uses numerous graphs in R to illustrate the model's results, assumptions, and other features.
*Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient.
*Provides many examples using real world datasets relevant to various academic disciplines.
*Fully integrates the R software environment in its numerous examples.
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
by "Nielsen BookData"
