Handbook of regression analysis
 Responsibility
 Samprit Chatterjee, Jeffrey S. Simonoff.
 Language
 English.
 Publication
 Hoboken, New Jersey : Wiley, 2013.
 Physical description
 1 online resource.
Access
Available online
 dx.doi.org Wiley Online Library
More options
Creators/Contributors
 Author/Creator
 Chatterjee, Samprit, 1938
 Contributor
 Simonoff, Jeffrey S.
Contents/Summary
 Bibliography
 Includes bibliographical references and index.
 Contents

 Preface xi Part I The Multiple Linear Regression Model 1 Multiple Linear Regression 3 1.1 Introduction 3 1.2 Concepts and Background Material 4 1.2.1 The Linear Regression Model 4 1.2.2 Estimation Using Least Squares 5 1.2.3 Assumptions 8 1.3 Methodology 9 1.3.1 Interpreting Regression Coefficients 9 1.3.2 Measuring the Strength of the Regression Relationship 10 1.3.3 Hypothesis Tests and Confidence Intervals for  12 1.3.4 Fitted Values and Predictions 13 1.3.5 Checking Assumptions Using Residual Plots 14 1.4 Example  Estimating Home Prices 16 1.5 Summary 19 2 Model Building 23 2.1 Introduction 23 2.2 Concepts and Background Material 24 2.2.1 Using hypothesis tests to compare models 24 2.2.2 Collinearity 26 2.3 Methodology 29 2.3.1 Model Selection 29 2.3.2 ExampleEstimating Home Prices (continued) 31 2.4 Indicator Variables and Modeling Interactions 38 2.4.1 ExampleElectronic Voting and the 2004 Presidential Election 40 2.5 Summary 46 Part II Addressing Violations of Assumptions 3 Diagnostics for Unusual Observations 53 3.1 Introduction 53 3.2 Concepts and Background Material 54 3.3 Methodology 56 3.3.1 Residuals and Outliers 56 3.3.2 Leverage Points 57 3.3.3 Influential Points and Cook's Distance 58 3.4 Example  Estimating Home Prices (continued) 60 3.5 Summary 64 4 Transformations and Linearizable Models 67 4.1 Introduction 67 4.2 Concepts and Background Material: the LogLog Model 69 4.3 Concepts and Background Material: Semilog models 69 4.3.1 Logged response variable 70 4.3.2 Logged predictor variable 70 4.4 Example  Predicting Movie Grosses After One Week 71 4.5 Summary 78 5 Time Series Data and Autocorrelation 81 5.1 Introduction 81 5.2 Concepts and Background Material 83 5.3 Methodology: Identifying Autocorrelation 85 5.3.1 The DurbinWatson Statistic 86 5.3.2 The Autocorrelation Function (ACF) 87 5.3.3 Residual Plots and the Runs Test 87 5.4 Methodology: Addressing Autocorrelation 88 5.4.1 Detrending and Deseasonalizing 88 5.4.2 Example  eCommerce Retail Sales 89 5.4.3 Lagging and Differencing 96 5.4.4 Example  Stock Indexes 96 5.4.5 Generalized Least Squares (GLS): the CochraneOrcutt Procedure 101 5.4.6 Example  Time Intervals Between Old Faithful Eruptions 104 5.5 Summary 107 Part III Categorical Predictors 6 Analysis of Variance 113 6.1 Introduction 113 6.2 Concepts and Background Material 114 6.2.1 Oneway ANOVA 114 6.2.2 Twoway ANOVA 115 6.3 Methodology 117 6.3.1 Codings for categorical predictors 117 6.3.2 Multiple comparisons 122 6.3.3 Levene's test and weighted least squares 124 6.3.4 Membership in multiple groups 127 6.4 Example  DVD Sales of Movies 129 6.5 HigherWay ANOVA 134 6.6 Summary 136 7 Analysis of Covariance 139 7.1 Introduction 139 7.2 Methodology 139 7.2.1 Constant shift models 139 7.2.2 Varying slope models 141 7.3 Example  International Grosses of Movies 141 7.4 Summary 145 Part IV Other Regression Models 8 Logistic Regression 149 8.1 Introduction 149 8.2 Concepts and Background Material 151 8.2.1 The logit response function 151 8.2.2 Bernoulli and binomial random variables 152 8.2.3 Prospective and retrospective designs 153 8.3 Methodology 156 8.3.1 Maximum likelihood estimation 156 8.3.2 Inference, model comparison, and model selection 157 8.3.3 GoodnessofFit 159 8.3.4 Measures of association and classification accuracy 161 8.3.5 Diagnostics 163 8.4 Example  Smoking and Mortality 163 8.5 Example  Modeling Bankruptcy 167 8.6 Summary 173 9 Multinomial Regression 177 9.1 Introduction 177 9.2 Concepts and Background Material 178 9.2.1 Nominal Response Variable 178 9.2.2 Ordinal Response Variable 180 9.3 Methodology 182 9.3.1 Estimation 182 9.3.2 Inference, model comparisons, and strength of fit 183 9.3.3 Lack of fit and violations of assumptions 184 9.4 Example  City Bond Ratings 185 9.5 Summary 189 10 Count Regression 191 10.1 Introduction 191 10.2 Concepts and Background Material 192 10.2.1 The Poisson random variable 192 10.2.2 Generalized linear models 193 10.3 Methodology 194 10.3.1 Estimation and inference 194 10.3.2 Offsets 195 10.4 Overdispersion and Negative Binomial Regression 196 10.4.1 Quasilikelihood 196 10.4.2 Negative Binomial Regression 197 10.5 Example  Unprovoked Shark Attacks in Florida 198 10.6 Other Count Regression Models 206 10.7 Poisson Regression and Weighted Least Squares 208 10.7.1 Example  International Grosses of Movies (continued) 209 10.8 Summary 211 11 Nonlinear Regression 215 11.1 Introduction 215 11.2 Concepts and Background Material 216 11.3 Methodology 218 11.3.1 Nonlinear least squares estimation 218 11.3.2 Inference for nonlinear regression models 219 11.4 Example  MichaelisMenten Enzyme Kinetics 220 11.5 Summary 225 Bibliography 227 Index 231.
 (source: Nielsen Book Data)9780470887165 20160610
 Publisher's Summary
 Written by an established expert in the field, the purpose of this handbook is to provide a practical, onestop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or "refresher" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by handson examples. Software routines are available via an authormaintained web site.
(source: Nielsen Book Data)9780470887165 20160610
Subjects
Bibliographic information
 Publication date
 2013
 Note
 "Written by an established expert in the field, the purpose of this handbook is to provide a practical, onestop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or "refresher" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by handson examples. Software routines are available via an authormaintained web site" Provided by publisher.
 Available in another form
 Print version: Chatterjee, Samprit, 1938 Handbook of regression analysis Hoboken, New Jersey : Wiley, 2013 ( 9780470887165 )
 ISBN
 9781118532843 (electronic bk.)
 1118532848 (electronic bk.)
 9781118532812 (electronic bk.)
 1118532813 (electronic bk.)
 9781118532836
 111853283X
 9781118532829
 1118532821
 9780470887165 (hardback)