# Applied regression analysis and generalized linear models

##### By: Fox, John.

Material type: BookPublisher: Los Angeles Sage 2016Edition: 3rd ed.Description: xxiv, 791 p.ISBN: 9781452205663.Subject(s): Regression analysis | Linear models (Statistics) | Social sciences - Statistical methodsDDC classification: 300.1519536 Summary: 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. Table of content: Chapter 1: Statistical Models and Social Science 1.1 Statistical Models and Social Reality 1.2 Observation and Experiment 1.3 Populations and Samples Part I: Data Craft Chapter 2: What Is Regression Analysis? 2.1 Preliminaries 2.2 Naive Nonparametric Regression 2.3 Local Averaging Chapter 3: Examining Data 3.1 Univariate Displays 3.2 Plotting Bivariate Data 3.3 Plotting Multivariate Data Chapter 4: Transforming Data 4.1 The Family of Powers and Roots 4.2 Transforming Skewness 4.3 Transforming Nonlinearity 4.4 Transforming Nonconstant Spread 4.5 Transforming Proportions 4.6 Estimating Transformations as Parameters Part II: Linear Models and Least Squares Chapter 5: Linear Least-Squares Regression 5.1 Simple Regression 5.2 Multiple Regression Chapter 6: Statistical Inference for Regression 6.1 Simple Regression 6.2 Multiple Regression 6.3 Empirical Versus Structural Relations 6.4 Measurement Error in Explanatory Variables Chapter 7: Dummy-Variable Regression 7.1 A Dichotomous Factor 7.2 Polytomous Factors 7.3 Modeling Interactions Chapter 8: Analysis of Variance 8.1 One-Way Analysis of Variance 8.2 Two-Way Analysis of Variance 8.3 Higher-Way Analysis of Variance 8.4 Analysis of Covariance 8.5 Linear Contrasts of Means Chapter 9: Statistical Theory for Linear Models 9.1 Linear Models in Matrix Form 9.2 Least-Squares Fit 9.3 Properties of the Least-Squares Estimator 9.4 Statistical Inference for Linear Models 9.5 Multivariate Linear Models 9.6 Random Regressors 9.7 Specification Error 9.8 Instrumental Variables and 2SLS Chapter 10: The Vector Geometry of Linear Models 10.1 Simple Regression 10.2 Multiple Regression 10.3 Estimating the Error Variance 10.4 Analysis-of-Variance Models Part III: Linear-Model Diagnostics Chapter 11: Unusual and Influential Data 11.1 Outliers, Leverage, and Influence 11.2 Assessing Leverage: Hat-Values 11.3 Detecting Outliers: Studentized Residuals 11.4 Measuring Influence 11.5 Numerical Cutoffs for Diagnostic Statistics 11.6 Joint Influence 11.7 Should Unusual Data Be Discarded? 11.8 Some Statistical Details Chapter 12: Non-Normality, Nonconstant Variance, Nonlinearity 12.1 Non-Normally Distributed Errors 12.2 Nonconstant Error Variance 12.3 Nonlinearity 12.4 Discrete Data 12.5 Maximum-Likelihood Methods 12.6 Structural Dimension Chapter 13: Collinearity and Its Purported Remedies 13.1 Detecting Collinearity 13.2 Coping With Collinearity: No Quick Fix Part IV: Generalized Linear Models Chapter 14: Logit and Probit Models 14.1 Models for Dichotomous Data 14.2 Models for Polytomous Data 14.3 Discrete Explanatory Variables and Contingency Tables Chapter 15: Generalized Linear Models 15.1 The Structure of Generalized Linear Models 15.2 Generalized Linear Models for Counts 15.3 Statistical Theory for Generalized Linear Models 15.4 Diagnostics for Generalized Linear Models 15.5 Complex Sample Surveys Part V: Extending Linear and Generalized Linear Models Chapter 16: Time-Series Regression and GLS 16.1 Generalized Least-Squares Estimation 16.2 Serially Correlated Errors 16.3 GLS Estimation With Autocorrelated Errors 16.4 Diagnosing Serially Correlated Errors Chapter 17: Nonlinear Regression 17.1 Polynomial Regression 17.2 Piecewise Polynomials and Regression Splines 17.3 Transformable Nonlinearity 17.4 Nonlinear Least Squares Chapter 18: Nonparametric Regression 18.1 Nonparametric Simple Regression: Scatterplot Smoothing 18.2 Nonparametric Multiple Regression 18.3 Generalized Nonparametric Regression Chapter 19: Robust Regression 19.1 M Estimation 19.2 Bounded-Inuence Regression 19.3 Quantile Regression 19.4 Robust Estimation of Generalized Linear Models 19.5 Concluding Remarks Chapter 20: Missing Data in Regression Models 20.1 Missing Data Basics 20.2 Traditional Approaches to Missing Data 20.3 Maximum-Likelihood Estimation for Data Missing at Random 20.4 Bayesian Multiple Imputation 20.5 Selection Bias and Censoring Chapter 21: Bootstrapping Regression Models 21.1 Bootstrapping Basics 21.2 Bootstrap Confidence Intervals 21.3 Bootstrapping Regression Models 21.4 Bootstrap Hypothesis Tests 21.5 Bootstrapping Complex Sampling Designs 21.6 Concluding Remarks Chapter 22: Model Selection, Averaging, and Validation 22.1 Model Selection 22.2 Model Averaging 22.3 Model Validation Part VI: Mixed-Effects Models Chapter 23: Linear Mixed-Effects Models 23.1 Hierarchical and Longitudinal Data 23.2 The Linear Mixed-Effects Model 23.3 Modeling Hierarchical Data 23.4 Modeling Longitudinal Data 23.5 Wald Tests for Fixed Effects 23.6 Likelihood-Ratio Tests of Variance and Covariance Components 23.7 Centering Explanatory Variables, Contextual Effects, and Fixed-Effects Models 23.8 BLUPs 23.9 Statistical Details Chapter 24: Generalized Linear and Nonlinear Mixed-Effects Models 24.1 Generalized Linear Mixed Models 24.2 Nonlinear Mixed Models (http://www.sagepub.com/books/Book237254?siteId=sage-us&prodTypes=any&q=9781452205663&pageTitle=productsSearch#tabview=title)Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library | Slot 223 (0 Floor, West Wing) | Non-fiction | 300.1519536 F6A7-2016 (Browse shelf) | Available | 189642 |

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.

Table of content:

Chapter 1: Statistical Models and Social Science

1.1 Statistical Models and Social Reality

1.2 Observation and Experiment

1.3 Populations and Samples

Part I: Data Craft

Chapter 2: What Is Regression Analysis?

2.1 Preliminaries

2.2 Naive Nonparametric Regression

2.3 Local Averaging

Chapter 3: Examining Data

3.1 Univariate Displays

3.2 Plotting Bivariate Data

3.3 Plotting Multivariate Data

Chapter 4: Transforming Data

4.1 The Family of Powers and Roots

4.2 Transforming Skewness

4.3 Transforming Nonlinearity

4.4 Transforming Nonconstant Spread

4.5 Transforming Proportions

4.6 Estimating Transformations as Parameters

Part II: Linear Models and Least Squares

Chapter 5: Linear Least-Squares Regression

5.1 Simple Regression

5.2 Multiple Regression

Chapter 6: Statistical Inference for Regression

6.1 Simple Regression

6.2 Multiple Regression

6.3 Empirical Versus Structural Relations

6.4 Measurement Error in Explanatory Variables

Chapter 7: Dummy-Variable Regression

7.1 A Dichotomous Factor

7.2 Polytomous Factors

7.3 Modeling Interactions

Chapter 8: Analysis of Variance

8.1 One-Way Analysis of Variance

8.2 Two-Way Analysis of Variance

8.3 Higher-Way Analysis of Variance

8.4 Analysis of Covariance

8.5 Linear Contrasts of Means

Chapter 9: Statistical Theory for Linear Models

9.1 Linear Models in Matrix Form

9.2 Least-Squares Fit

9.3 Properties of the Least-Squares Estimator

9.4 Statistical Inference for Linear Models

9.5 Multivariate Linear Models

9.6 Random Regressors

9.7 Specification Error

9.8 Instrumental Variables and 2SLS

Chapter 10: The Vector Geometry of Linear Models

10.1 Simple Regression

10.2 Multiple Regression

10.3 Estimating the Error Variance

10.4 Analysis-of-Variance Models

Part III: Linear-Model Diagnostics

Chapter 11: Unusual and Influential Data

11.1 Outliers, Leverage, and Influence

11.2 Assessing Leverage: Hat-Values

11.3 Detecting Outliers: Studentized Residuals

11.4 Measuring Influence

11.5 Numerical Cutoffs for Diagnostic Statistics

11.6 Joint Influence

11.7 Should Unusual Data Be Discarded?

11.8 Some Statistical Details

Chapter 12: Non-Normality, Nonconstant Variance, Nonlinearity

12.1 Non-Normally Distributed Errors

12.2 Nonconstant Error Variance

12.3 Nonlinearity

12.4 Discrete Data

12.5 Maximum-Likelihood Methods

12.6 Structural Dimension

Chapter 13: Collinearity and Its Purported Remedies

13.1 Detecting Collinearity

13.2 Coping With Collinearity: No Quick Fix

Part IV: Generalized Linear Models

Chapter 14: Logit and Probit Models

14.1 Models for Dichotomous Data

14.2 Models for Polytomous Data

14.3 Discrete Explanatory Variables and Contingency Tables

Chapter 15: Generalized Linear Models

15.1 The Structure of Generalized Linear Models

15.2 Generalized Linear Models for Counts

15.3 Statistical Theory for Generalized Linear Models

15.4 Diagnostics for Generalized Linear Models

15.5 Complex Sample Surveys

Part V: Extending Linear and Generalized Linear Models

Chapter 16: Time-Series Regression and GLS

16.1 Generalized Least-Squares Estimation

16.2 Serially Correlated Errors

16.3 GLS Estimation With Autocorrelated Errors

16.4 Diagnosing Serially Correlated Errors

Chapter 17: Nonlinear Regression

17.1 Polynomial Regression

17.2 Piecewise Polynomials and Regression Splines

17.3 Transformable Nonlinearity

17.4 Nonlinear Least Squares

Chapter 18: Nonparametric Regression

18.1 Nonparametric Simple Regression: Scatterplot Smoothing

18.2 Nonparametric Multiple Regression

18.3 Generalized Nonparametric Regression

Chapter 19: Robust Regression

19.1 M Estimation

19.2 Bounded-Inuence Regression

19.3 Quantile Regression

19.4 Robust Estimation of Generalized Linear Models

19.5 Concluding Remarks

Chapter 20: Missing Data in Regression Models

20.1 Missing Data Basics

20.2 Traditional Approaches to Missing Data

20.3 Maximum-Likelihood Estimation for Data Missing at Random

20.4 Bayesian Multiple Imputation

20.5 Selection Bias and Censoring

Chapter 21: Bootstrapping Regression Models

21.1 Bootstrapping Basics

21.2 Bootstrap Confidence Intervals

21.3 Bootstrapping Regression Models

21.4 Bootstrap Hypothesis Tests

21.5 Bootstrapping Complex Sampling Designs

21.6 Concluding Remarks

Chapter 22: Model Selection, Averaging, and Validation

22.1 Model Selection

22.2 Model Averaging

22.3 Model Validation

Part VI: Mixed-Effects Models

Chapter 23: Linear Mixed-Effects Models

23.1 Hierarchical and Longitudinal Data

23.2 The Linear Mixed-Effects Model

23.3 Modeling Hierarchical Data

23.4 Modeling Longitudinal Data

23.5 Wald Tests for Fixed Effects

23.6 Likelihood-Ratio Tests of Variance and Covariance Components

23.7 Centering Explanatory Variables, Contextual Effects, and Fixed-Effects Models

23.8 BLUPs

23.9 Statistical Details

Chapter 24: Generalized Linear and Nonlinear Mixed-Effects Models

24.1 Generalized Linear Mixed Models

24.2 Nonlinear Mixed Models

(http://www.sagepub.com/books/Book237254?siteId=sage-us&prodTypes=any&q=9781452205663&pageTitle=productsSearch#tabview=title)

There are no comments for this item.