Methods and applications of linear models : regression and the analysis of variance /
This book features innovative approaches to understanding and working with models and theory of linear regression. It provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for...
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| Format: | Book |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2013]
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| Edition: | Third edition. |
| Series: | Wiley series in probability and statistics
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| Subjects: |
Table of Contents:
- Part I. Regression
- 1. Introduction to linear models
- 2. Regression on functions of one variable
- 3. Transforming the data
- 4. Regression on functions of several variables
- 5. Collinearity in multiple linear regression
- 6. Influential observations in multiple linear regression
- 7. Polynomial models and qualitative predictors
- 8. Additional topics
- Part II. The analysis of variance
- 9. Classification models I: introduction
- 10. The mathematical theory of linear models
- 11. Classification models II: multiple crossed and nested factors
- 12. Mixed models I: the AOV method with balanced data
- 13. Mixed models II: the AVE method with balanced data
- 14. Mixed models III: unbalanced data
- 15. Simultaneous inference: tests and confidence intervals
- Appendix A. Mathematics
- Appendix B. Statistics
- Appendix C. Data tables
- Appendix D. Statistical tables.