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Applied regression analysis and generalized linear models / John Fox.

By: Fox, John [author].
Material type: TextTextPublisher: Los Angeles : SAGE, ©2016Edition: 3rd ed.Description: xxiv, 791 p. : illustrations ; 26 cm.ISBN: 9781452205663.Subject(s): Regression analysis | Linear models (Statistics) | Social sciences -- Statistical methodsDDC classification: 000SA.06
Contents:
1. Statistical models and social science -- 2. What is regression analysis? -- 3. Examining data -- 4. Transforming data -- 5. Linear least-squares regression -- 6. Statistical inference for regression -- 7. Dummy-variable regression -- 8. Analysis of variance -- 9. Statistical theory for linear models -- 10. The vector geometry of linear models -- 11. Unusual and influential data -- 12. Diagnosing non-normality, nonconstrant error variance, and nonlinearity -- 13. Collinearity and its purported remedies -- 14. Logit and probit models for categorical response variables -- 15. Generalized linear models -- 16. Time-series regression and generalized least squares -- 17. Nonlinear regression -- 18. Nonparametric regression -- 19. Robust regression -- 20. Missing data in regression models -- 21. Bootstrapping regression models -- 22. Model selection, averaging, and validation -- 23. Linear mixed-effects models for hierarchical and longitudinal data -- 24. Generalized linear and nonlinear mixed-effects models -- Appendices.
Summary: Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.06 F792 (Browse shelf) Available 137356
Total holds: 0

Includes bibliographical references and indexes.

1. Statistical models and social science --
2. What is regression analysis? --
3. Examining data --
4. Transforming data --
5. Linear least-squares regression --
6. Statistical inference for regression --
7. Dummy-variable regression --
8. Analysis of variance --
9. Statistical theory for linear models --
10. The vector geometry of linear models --
11. Unusual and influential data --
12. Diagnosing non-normality, nonconstrant error variance, and nonlinearity --
13. Collinearity and its purported remedies --
14. Logit and probit models for categorical response variables --
15. Generalized linear models --
16. Time-series regression and generalized least squares --
17. Nonlinear regression --
18. Nonparametric regression --
19. Robust regression --
20. Missing data in regression models --
21. Bootstrapping regression models --
22. Model selection, averaging, and validation --
23. Linear mixed-effects models for hierarchical and longitudinal data --
24. Generalized linear and nonlinear mixed-effects models --
Appendices.

Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.

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