Applied regression analysis and generalized linear models / John Fox.
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TextPublication details: Los Angeles : SAGE, ©2016.Edition: 3rd edDescription: xxiv, 791 p. : illustrations ; 26 cmISBN: - 9781452205663
- 000SA.06 23 F792
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.06 F792 (Browse shelf(Opens below)) | Available | 137356 |
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| 000SA.06 D259 Quantile regression : theory and applications / | 000SA.06 Eh33 Theory of ridge regression estimation with applications/ | 000SA.06 F158 Regression : | 000SA.06 F792 Applied regression analysis and generalized linear models / | 000SA.06 G183 Introduction to mixed modelling : beyond regression and analysis of variance/ | 000SA.06 H296 Regression modeling strategies : | 000SA.06 K11 Bayesian analysis of failure time data using P-Splines / |
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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