Applied regression analysis and generalized linear models. (2015)

• Record Type: Book
• Other Titles: Applied regression analysis & generalized linear models
• Further Information: Note: John Fox.
• Other Names: Fox, John, 1947-; Fox, John, 1947-
• Contents: Chapter 1: Statistical Models and Social Science; 1.1Statistical Models and Social Reality; 1.2Observation and Experiment; 1.3Populations and Samples; Part I: Data Craft; Chapter 2: What Is Regression Analysis?; 2.1Preliminaries; 2.2Naive Nonparametric Regression; 2.3Local Averaging; Chapter 3: Examining Data; 3.1Univariate Displays; 3.2Plotting Bivariate Data; 3.3Plotting Multivariate Data; Chapter 4: Transforming Data; 4.1The Family of Powers and Roots; 4.2Transforming Skewness; 4.3Transforming Nonlinearity; 4.4Transforming Nonconstant Spread; 4.5Transforming Proportions; 4.6Estimating Transformations as Parameters; Part II: Linear Models and Least Squares; Chapter 5: Linear Least-Squares Regression; 5.1Simple Regression; 5.2Multiple Regression; Chapter 6: Statistical Inference for Regression; 6.1Simple Regression; 6.2Multiple Regression; 6.3Empirical Versus Structural Relations; 6.4Measurement Error in Explanatory Variables; Chapter 7: Dummy-Variable Regression; 7.1A Dichotomous Factor; 7.2Polytomous Factors; 7.3Modeling Interactions; Chapter 8: Analysis of Variance; 8.1One-Way Analysis of Variance; 8.2Two-Way Analysis of Variance; 8.3Higher-Way Analysis of Variance; 8.4Analysis of Covariance; 8.5Linear Contrasts of Means; Chapter 9: Statistical Theory for Linear Models; 9.1Linear Models in Matrix Form; 9.2Least-Squares Fit; 9.3Properties of the Least-Squares Estimator; 9.4Statistical Inference for Linear Models; 9.5Multivariate Linear Models; 9.6Random Regressors; 9.7Specification Error; 9.8Instrumental Variables and 2SLS; Chapter 10: The Vector Geometry of Linear Models; 10.1Simple Regression; 10.2Multiple Regression; 10.3Estimating the Error Variance; 10.4Analysis-of-Variance Models; Part III: Linear-Model Diagnostics; Chapter 11: Unusual and Influential Data; 11.1Outliers, Leverage, and Influence; 11.2Assessing Leverage: Hat-Values; 11.3Detecting Outliers: Studentized Residuals; 11.4Measuring Influence; 11.5Numerical Cutoffs for Diagnostic Statistics; 11.6Joint Influence; 11.7Should Unusual Data Be Discarded?; 11.8Some Statistical Details; Chapter 12: Non-Normality, Nonconstant Variance, Nonlinearity; 12.1Non-Normally Distributed Errors; 12.2Nonconstant Error Variance; 12.3Nonlinearity; 12.4Discrete Data; 12.5Maximum-Likelihood Methods; 12.6Structural Dimension; Chapter 13: Collinearity and Its Purported Remedies; 13.1Detecting Collinearity; 13.2Coping With Collinearity: No Quick Fix; Part IV: Generalized Linear Models; Chapter 14: Logit and Probit Models; 14.1Models for Dichotomous Data; 14.2Models for Polytomous Data; 14.3Discrete Explanatory Variables and Contingency Tables; Chapter 15: Generalized Linear Models; 15.1The Structure of Generalized Linear Models; 15.2Generalized Linear Models for Counts; 15.3Statistical Theory for Generalized Linear Models; 15.4Diagnostics for Generalized Linear Models; 15.5Complex Sample Surveys; Part V: Extending Linear and Generalized Linear Models; Chapter 16: Time-Series Regression and GLS; 16.1Generalized Least-Squares Estimation; 16.2Serially Correlated Errors; 16.3 GLS Estimation With Autocorrelated Errors; 16.4 Diagnosing Serially Correlated Errors; Chapter 17: Nonlinear Regression; 17.1Polynomial Regression; 17.2Piecewise Polynomials and Regression Splines; 17.3Transformable Nonlinearity; 17.4Nonlinear Least Squares; Chapter 18: Nonparametric Regression; 18.1Nonparametric Simple Regression: Scatterplot Smoothing; 18.2Nonparametric Multiple Regression; 18.3Generalized Nonparametric Regression; Chapter 19: Robust Regression; 19.1M Estimation; 19.2Bounded-Inuence Regression; 19.3Quantile Regression; 19.4Robust Estimation of Generalized Linear Models; 19.5Concluding Remarks; Chapter 20: Missing Data in Regression Models; 20.1Missing Data Basics; 20.2Traditional Approaches to Missing Data; 20.3Maximum-Likelihood Estimation for Data Missing at Random; 20.4Bayesian Multiple Imputation; 20.5Selection Bias and Censoring; Chapter 21: Bootstrapping Regression Models; 21.1Bootstrapping Basics; 21.2Bootstrap Confidence Intervals; 21.3Bootstrapping Regression Models; 21.4Bootstrap Hypothesis Tests; 21.5Bootstrapping Complex Sampling Designs; 21.6Concluding Remarks; Chapter 22: Model Selection, Averaging, and Validation; 22.1Model Selection; 22.2Model Averaging; 22.3Model Validation; Part VI: Mixed-Effects Models; Chapter 23: Linear Mixed-Effects Models; 23.1Hierarchical and Longitudinal Data; 23.2The Linear Mixed-Effects Model; 23.3Modeling Hierarchical Data; 23.4Modeling Longitudinal Data; 23.5Wald Tests for Fixed Effects; 23.6Likelihood-Ratio Tests of Variance and Covariance Components; 23.7Centering Explanatory Variables, Contextual Effects, and Fixed-Effects Models; 23.8BLUPs; 23.9Statistical Details; Chapter 24: Generalized Linear and Nonlinear Mixed-Effects Models; 24.1Generalized Linear Mixed Models; 24.2Nonlinear Mixed Models;
• Edition: Third Edition
• Publisher Details: Thousand Oaks : SAGE Publications, Inc
• Publication Date: 2015
• Extent: 1 online resource (816 pages)
• Subjects: 300.1/519536; Regression analysis; Linear models (Statistics); Social sciences -- Statistical methods; Linear models (Statistics); Regression analysis; Social sciences -- Statistical methods
• Languages: English
• ISBNs: 9781483321318; 1483321312
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.