
- 830 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
About this book
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least square
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Yes, you can access Regression Modeling by Michael Panik in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Front cover
- Contents
- Preface
- Author
- Chapter 1. Review of Fundamentals of Statistics
- Chapter 2. Bivariate Linear Regression and Correlation
- Chapter 3. Misspecified Disturbance Terms
- Chapter 4. Nonparametric Regression
- Chapter 5. Logistic Regression
- Chapter 6. Bayesian Regression
- Chapter 7. Robust Regression
- Chapter 8. Fuzzy Regression
- Chapter 9. Random Coefficients Regression
- Chapter 10. L1 and q-Quantile Regression
- Chapter 11. Regression in a Spatial Domain
- Chapter 12. Multiple Regression
- Chapter 13. Normal Correlation Models
- Chapter 14. Ridge Regression
- Chapter 15. Indicator Variables
- Chapter 16. Polynomial Model Estimation
- Chapter 17. Semiparametric Regression
- Chapter 18. Nonlinear Regression
- Chapter 19. Issues in Time Series Modeling and Estimation
- Appendix A
- References
- Index
- Back cover