Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of "classical" regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. The book further pays particular attention to methods that have become…mehr
Handbook and reference guide for students and practitioners of statistical regression-based analyses in R
Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of "classical" regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data.
The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: _ Regularization methods _ Smoothing methods _ Tree-based methods
In the new edition of the Handbook, the data analyst's toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website.
Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Samprit Chatterjee, PhD, is Professor Emeritus of Statistics at New York University. A Fellow of the American Statistical Association, Dr. Chatterjee has been a Fulbright scholar in both Kazakhstan and Mongolia. He is the coauthor of multiple editions of Regression Analysis By Example, Sensitivity Analysis in Linear Regression, A Casebook for a First Course in Statistics and Data Analysis, and the first edition of Handbook of Regression Analysis, all published by Wiley. Jeffrey S. Simonoff, PhD, is Professor of Statistics at the Leonard N. Stern School of Business of New York University. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He has authored, coauthored, or coedited more than one hundred articles and seven books on the theory and applications of statistics.
Inhaltsangabe
Preface to the Second Edition xv
Preface to the First Edition xix
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 ß 12
1.3.4 Fitted Values and Predictions 13
1.3.5 Checking Assumptions Using Residual Plots 14
1.4 Example --Estimating Home Prices 15
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 Example--Estimating Home Prices (continued) 31
2.4 Indicator Variables and Modeling Interactions 38
2.4.1 Example--Electronic 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 63
4 Transformations and Linearizable Models 67
4.1 Introduction 67
4.2 Concepts and Background Material: The Log-Log 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 77
5 Time Series Data and Autocorrelation 79
5.1 Introduction 79
5.2 Concepts and Background Material 81
5.3 Methodology: Identifying Autocorrelation 83
5.3.1 The Durbin-Watson Statistic 83
5.3.2 The Autocorrelation Function (ACF) 84
5.3.3 Residual Plots and the Runs Test 85
5.4 Methodology: Addressing Autocorrelation 86
5.4.1 Detrending and Deseasonalizing 86
5.4.2 Example-- e-Commerce Retail Sales 87
5.4.3 Lagging and Differencing 93
5.4.4 Example-- Stock Indexes 94
5.4.5 Generalized Least Squares (GLS): The Cochrane-Orcutt Procedure 99
5.4.6 Example-- Time Intervals Between Old Faithful Geyser Eruptions 100
5.5 Summary 104
Part III Categorical Predictors
6 Analysis of Variance 109
6.1 Introduction 109
6.2 Concepts and Background Material 110
6.2.1 One-Way ANOVA 110
6.2.2 Two-Way ANOVA 111
6.3 Methodology 113
6.3.1 Codings for Categorical Predictors 113
6.3.2 Multiple Comparisons 118
6.3.3 Levene's Test and Weighted Least Squares 120
