Applied Regression Analysis and Generalized Linear Models
eBook - ePub

Applied Regression Analysis and Generalized Linear Models

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Applied Regression Analysis and Generalized Linear Models

About this book

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. 

Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author?s website.

NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author?s website.

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Yes, you can access Applied Regression Analysis and Generalized Linear Models by John Fox in PDF and/or ePUB format, as well as other popular books in Social Sciences & Research & Methodology in Psychology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Acknowledgements
  4. Title Page
  5. Copyright Page
  6. Brief Contents
  7. Contents
  8. Preface
  9. About the Author
  10. 1 Statistical Models and Social Science
  11. Part I Data Craft
  12. 2 What Is Regression Analysis?
  13. 3 Examining Data
  14. 4 Transforming Data
  15. Part II Linear Models and Least Squares
  16. 5 Linear Least-Squares Regression
  17. 6 Statistical Inference for Regression
  18. 7 Dummy-Variable Regression
  19. 8 Analysis of Variance
  20. 9 Statistical Theory for Linear Models*
  21. 10 The Vector Geometry of Linear Models*
  22. Part III Linear-Model Diagnostics
  23. 11 Unusual and Influential Data
  24. 12 Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
  25. 13 Collinearity and Its Purported Remedies
  26. Part IV Generalized Linear Models
  27. 14 Logit and Probit Models for Categorical Response Variables
  28. 15 Generalized Linear Models
  29. Part V Extending Linear and Generalized Linear Models
  30. 16 Time-Series Regression and Generalized Least Squares*
  31. 17 Nonlinear Regression
  32. 18 Nonparametric Regression
  33. 19 Robust Regression*
  34. 20 Missing Data in Regression Models
  35. 21 Bootstrapping Regression Models
  36. 22 Model Selection, Averaging, and Validation
  37. Part VI Mixed-Effects Models
  38. 23 Linear Mixed-Effects Models for Hierarchical and Longitudinal Data
  39. 24 Generalized Linear and Nonlinear Mixed-Effects Models
  40. Appendix A: Notation
  41. References
  42. Author Index
  43. Index
  44. Author Index
  45. Publisher Note