Normal view MARC view ISBD view

Applied regression analysis and generalized linear models

By: Fox, John.
Material type: materialTypeLabelBookPublisher: 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)
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
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.

Log in to your account to post a comment.

Powered by Koha