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Handbook of Regression Analysis [Kõva köide]

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  • Formaat: Hardback, 252 pages, kõrgus x laius x paksus: 241x163x20 mm, kaal: 472 g, Tables: 0 B&W, 0 Color; Graphs: 75 B&W, 0 Color
  • Sari: Wiley Handbooks in Applied Statistics
  • Ilmumisaeg: 18-Jan-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470887168
  • ISBN-13: 9780470887165
Teised raamatud teemal:
Handbook of Regression AnalysisSuurem pilt
  • Formaat: Hardback, 252 pages, kõrgus x laius x paksus: 241x163x20 mm, kaal: 472 g, Tables: 0 B&W, 0 Color; Graphs: 75 B&W, 0 Color
  • Sari: Wiley Handbooks in Applied Statistics
  • Ilmumisaeg: 18-Jan-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470887168
  • ISBN-13: 9780470887165
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470887165.html
Märksõnad:
"Written by two established experts in the field, the purpose of this handbook is to provide a practical, one-stop 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 hands-on examples. Software routines are available via an author-maintained web site"--



Written by established experts in the field, the purpose of this handbook is to provide a practical, one-stop 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 hands-on examples. Software routines are available via an author-maintained web site.

Arvustused

I would be happy to recommend this nice handy book as a reference to my students. The clarity of the writing and proper choices of examples allows the presentations of many statistical methods shine. (The American Statistician, 1 February 2015)

Overall, a valuable user-friendly resource. Summing Up: Highly recommended. Upper-division undergraduates through professionals.  (Choice, 1 October 2013)

All in all, I also very much like the Handbook and if I were not to retire this year, I would be happy to tell my students that it is a very nice and handy book.  (International Statistical Review, 15 February 2013)

Preface xi
Part I The Multiple Linear Regression Model
1 Multiple Linear Regression
3(20)
1.1 Introduction
3(1)
1.2 Concepts and Background Material
4(5)
1.2.1 The Linear Regression Model
4(1)
1.2.2 Estimation Using Least Squares
5(3)
1.2.3 Assumptions
8(1)
1.3 Methodology
9(7)
1.3.1 Interpreting Regression Coefficients
9(1)
1.3.2 Measuring the Strength of the Regression Relationship
10(2)
1.3.3 Hypothesis Tests and Confidence Intervals for β
12(1)
1.3.4 Fitted Values and Predictions
13(1)
1.3.5 Checking Assumptions Using Residual Plots
14(2)
1.4 Example---Estimating Home Prices
16(3)
1.5 Summary
19(4)
2 Model Building
23(30)
2.1 Introduction
23(1)
2.2 Concepts and Background Material
24(5)
2.2.1 Using Hypothesis Tests to Compare Models
24(2)
2.2.2 Collinearity
26(3)
2.3 Methodology
29(9)
2.3.1 Model Selection
29(2)
2.3.2 Example---Estimating Home Prices (continued)
31(7)
2.4 Indicator Variables and Modeling Interactions
38(8)
2.4.1 Example---Electronic Voting and the 2004 Presidential Election
40(6)
2.5 Summary
46(7)
Part II Addressing Violations of Assumptions
3 Diagnostics for Unusual Observations
53(14)
3.1 Introduction
53(1)
3.2 Concepts and Background Material
54(2)
3.3 Methodology
56(4)
3.3.1 Residuals and Outliers
56(1)
3.3.2 Leverage Points
57(1)
3.3.3 Influential Points and Cook's Distance
58(2)
3.4 Example--Estimating Home Prices (continued)
60(4)
3.5 Summary
64(3)
4 Transformations and Linearizable Models
67(14)
4.1 Introduction
67(2)
4.2 Concepts and Background Material: The Log-Log Model
69(1)
4.3 Concepts and Background Material: Semilog Models
69(2)
4.3.1 Logged Response Variable
70(1)
4.3.2 Logged Predictor Variable
70(1)
4.4 Example--Predicting Movie Grosses After One Week
71(7)
4.5 Summary
78(3)
5 Time Series Data and Autocorrelation
81(32)
5.1 Introduction
81(2)
5.2 Concepts and Background Material
83(2)
5.3 Methodology: Identifying Autocorrelation
85(3)
5.3.1 The Durbin-Watson Statistic
86(1)
5.3.2 The Autocorrelation Function (ACF)
87(1)
5.3.3 Residual Plots and the Runs Test
87(1)
5.4 Methodology: Addressing Autocorrelation
88(19)
5.4.1 Detrending and Deseasonalizing
88(1)
5.4.2 Example--e-Commerce Retail Sales
89(7)
5.4.3 Lagging and Differencing
96(1)
5.4.4 Example--Stock Indexes
96(5)
5.4.5 Generalized Least Squares (GLS): The Cochrane-Orcutt Procedure
101(3)
5.4.6 Example--Time Intervals Between Old Faithful Eruptions
104(3)
5.5 Summary
107(6)
Part III Categorical Predictors
6 Analysis of Variance
113(26)
6.1 Introduction
113(1)
6.2 Concepts and Background Material
114(3)
6.2.1 One-Way ANOVA
114(1)
6.2.2 Two-Way ANOVA
115(2)
6.3 Methodology
117(12)
6.3.1 Codings for Categorical Predictors
117(5)
6.3.2 Multiple Comparisons
122(2)
6.3.3 Levene's Test and Weighted Least Squares
124(3)
6.3.4 Membership in Multiple Groups
127(2)
6.4 Example--DVD Sales of Movies
129(5)
6.5 Higher-Way ANOVA
134(2)
6.6 Summary
136(3)
7 Analysis of Covariance
139(10)
7.1 Introduction
139(1)
7.2 Methodology
139(2)
7.2.1 Constant Shift Models
139(2)
7.2.2 Varying Slope Models
141(1)
7.3 Example--International Grosses of Movies
141(4)
7.4 Summary
145(4)
Part IV OTHER REGRESSION MODELS
8 Logistic Regression
149(28)
8.1 Introduction
8.2 Concepts and Background Material
151(5)
8.2.1 The Logit Response Function
151(1)
8.2.2 Bernoulli and Binomial Random Variables
152(1)
8.2.3 Prospective and Retrospective Designs
153(3)
8.3 Methodology
156(7)
8.3.1 Maximum Likelihood Estimation
156(1)
8.3.2 Inference, Model Comparison, and Model Selection
157(2)
8.3.3 Goodness-of-Fit
159(2)
8.3.4 Measures of Association and Classification Accuracy
161(2)
8.3.5 Diagnostics
163(1)
8.4 Example--Smoking and Mortality
163(4)
8.5 Example--Modeling Bankruptcy
167(6)
8.6 Summary
173(4)
9 Multinomial Regression
177(14)
9.1 Introduction
177(1)
9.2 Concepts and Background Material
178(4)
9.2.1 Nominal Response Variable
178(2)
9.2.2 Ordinal Response Variable
180(2)
9.3 Methodology
182(7)
9.3.1 Estimation
182(1)
9.3.2 Inference, Model Comparisons, and Strength of Fit
183(1)
9.3.3 Lack of Fit and Violations of Assumptions
184(5)
9.4 Example--City Bond Ratings
189(1)
9.5 Summary
189(2)
10 Count Regression
191(24)
10.1 Introduction
191(1)
10.2 Concepts and Background Material
192(2)
10.2.1 The Poisson Random Variable
192(1)
10.2.2 Generalized Linear Models
193(1)
10.3 Methodology
194(2)
10.3.1 Estimation and Inference
194(1)
10.3.2 Offsets
195(1)
10.4 Overdispersion and Negative Binomial Regression
196(2)
10.4.1 Quasi-likelihood
196(1)
10.4.2 Negative Binomial Regression
197(1)
10.5 Example--Unprovoked Shark Attacks in Florida
198(8)
10.6 Other Count Regression Models
206(2)
10.7 Poisson Regression and Weighted Least Squares
208(7)
10.7.1 Example--International Grosses of Movies (continued)
205(6)
Summary
211(4)
11 Nonlinear Regression
215(12)
11.1 Introduction
215(1)
11.2 Concepts and Background Material
216(2)
11.3 Methodology
218(2)
11.3.1 Nonlinear Least Squares Estimation
218(1)
11.3.2 Inference for Nonlinear Regression Models
219(1)
11.4 Example--Michaelis-Menten Enzyme Kinetics
220(5)
11.5 Summary
225(2)
Bibliography 227(4)
Index 231
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 Regression Analysis by Example, Sensitivity Analysis in Linear Regression, and A Casebook for a First Course in Statistics and Data 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 or coauthored more than ninety articles and five books on the theory and applications of statistics.