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Regression Analysis by Example 3rd Revised edition [Kõva köide]

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(Cornell University USA), , Revised by ,
  • Formaat: Hardback, 384 pages, kõrgus x laius: 245x162 mm, kaal: 652 g, illustrations
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 29-Nov-1999
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471319465
  • ISBN-13: 9780471319467
Teised raamatud teemal:
Regression Analysis by Example 3rd Revised editionSuurem pilt
  • Formaat: Hardback, 384 pages, kõrgus x laius: 245x162 mm, kaal: 652 g, illustrations
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 29-Nov-1999
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471319465
  • ISBN-13: 9780471319467
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471319467.html
Märksõnad:
A textbook for students who have some background in statistics but are not specialists in it, and who use regression analysis quite extensively in their work. Students who are specializing in statistics might find it a useful balance to the abstract approach of most standard textbooks. Chatterjee (statistics, New York U.), Ali S. Hadi (statistical and computing sciences, Cornell U.) and consultant Bertram Price emphasize analyzing data rather than formulas, testing hypotheses, or confidence intervals, and therefore make no attempt to derive the techniques they describe. They mention no date for earlier editions, but have extensively rewritten the third edition and expanded it from nine chapters to twelve. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Regression analysis provides a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Third Edition explains the principles underlying exploratory data analysis, emphasizing data analysis rather than statistical theory. This is not just another edition of the book; it is a major rewriting and reorganization of the previous edition. The new edition is expanded and updated to reflect recent advances in the field, offering in-depth treatment of diagnostic plots, time series regression, multicollinearity, and logistic regression.

Suitable for anyone with an understanding of elementary statistics, Regression Analysis by Example, Third Edition illustrates methods of regression analysis, with examples containing the types of irregularities commonly encountered in the real world. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. Each of the methods described can be carried out with most currently available statistical software packages.
Preface xiii
Introduction
1(20)
What Is Regression Analysis?
1(1)
Publicly Available Data Sets
2(1)
Selected Applications of Regression Analysis
3(4)
Steps in Regression Analysis
7(10)
Scope and Organization of the Book
17(4)
Exercises
18(3)
Simple Linear Regression
21(30)
Introduction
21(1)
Covariance and Correlation Coefficient
21(5)
Example: Computer Repair Data
26(3)
The Simple Linear Regression Model
29(1)
Parameter Estimation
30(3)
Tests of Hypotheses
33(4)
Confidence Intervals
37(1)
Predictions
38(2)
Measuring the Quality of Fit
40(3)
Regression Line Through the Origin
43(1)
Trivial Regression Models
44(1)
Bibliographic Notes
45(6)
Exercises
45(6)
Multiple Linear Regression
51(34)
Introduction
51(1)
Description of the Data and Model
51(1)
Example: Supervisor Performance Data
52(3)
Parameter Estimation
55(1)
Interpretations of Regression Coefficients
56(3)
Properties of the Least Squares Estimators
59(1)
Multiple Correlation Coefficient
60(1)
Inference for Individual Regression Coefficients
61(2)
Tests of Hypotheses in a Linear Model
63(11)
Predictions
74(1)
Summary
74(11)
Exercises
75(5)
Appendix
80(5)
Regression Diagnostics: Detection of Model Violations
85(38)
Introduction
85(1)
The Standard Regression Assumptions
85(3)
Various Types of Residuals
88(2)
Graphical Methods
90(3)
Graphs Before Fitting a Model
93(4)
Graphs After Fitting a Model
97(1)
Checking Linearity and Normality Assumptions
97(1)
Leverage, Influence, and Outliers
98(5)
Measures of Influence
103(4)
The Potential-Residual Plot
107(1)
What to Do with the Outliers?
108(1)
Role of Variables in a Regression Equation
109(4)
Effects of an Additional Predictor
113(3)
Robust Regression
116(7)
Exercises
116(7)
Qualitative Variables as Predictors
123(30)
Introduction
123(1)
Salary Survey Data
124(3)
Interaction Variables
127(4)
Systems of Regression Equations
131(9)
Other Applications of Indicator Variables
140(1)
Seasonality
141(1)
Stability of Regression Parameters Over Time
142(11)
Exercises
144(9)
Transformation of Variables
153(28)
Introduction
153(2)
Transformations to Achieve Linearity
155(2)
Bacteria Deaths Due to X-Ray Radiation
157(4)
Transformations to Stabilize Variance
161(5)
Detection of Heteroscedastic Errors
166(2)
Removal of Heteroscedasticity
168(2)
Weighted Least Squares
170(1)
Logarithmic Transformation of Data
170(4)
Power Transformation
174(2)
Summary
176(5)
Exercises
176(5)
Weighted Least Squares
181(20)
Introduction
181(1)
Heteroscedastic Models
182(3)
Two-Stage Estimation
185(2)
Education Expenditure Data
187(10)
Fitting a Dose-Response Relationship Curve
197(4)
Exercises
199(2)
The Problem of Correlated Errors
201(24)
Introduction: Autocorrelation
201(1)
Consumer Expenditure and Money Stock
202(2)
Durbin-Watson Statistic
204(2)
Removal of Autocorrelation by Transformation
206(2)
Iterative Estimation With Autocorrelated Errors
208(1)
Autocorrelation and Missing Variables
209(1)
Analysis of Housing Starts
210(4)
Limitations of Durbin-Watson Statistic
214(2)
Indicator Variables to Remove Seasonality
216(3)
Regressing Two Time Series
219(6)
Exercises
220(5)
Analysis of Collinear Data
225(38)
Introduction
225(1)
Effects on Inference
226(6)
Effects on Forecasting
232(4)
Detection of Multicollinearity
236(6)
Centering and Scaling
242(3)
Principal Components Approach
245(4)
Imposing Constraints
249(3)
Searching for Linear Functions of the β's
252(3)
Computations Using Principal Components
255(3)
Bibliographic Notes
258(5)
Exercises
258(2)
Appendix: Principal Components
260(3)
Biased Estimation of Regression Coefficients
263(22)
Introduction
263(1)
Principal Components Regression
264(2)
Removing Dependence Among the Predictors
266(2)
Constraints on the Regression Coefficients
268(1)
Principal Components Regression: A Caution
269(2)
Ridge Regression
271(2)
Estimation by the Ridge Method
273(3)
Ridge Regression: Some Remarks
276(3)
Summary
279(6)
Exercises
279(2)
Appendix: Ridge Regression
281(4)
Variable Selection Procedures
285(34)
Introduction
285(1)
Formulation of the Problem
286(1)
Consequences of Variables Deletion
286(2)
Uses of Regression Equations
288(1)
Criteria for Evaluating Equations
289(2)
Multicollinearity and Variable Selection
291(1)
Evaluating All Possible Equations
291(1)
Variable Selection Procedures
292(2)
General Remarks on Variable Selection Methods
294(1)
A Study of Supervisor Performance
295(4)
Variable Selection With Collinear Data
299(1)
The Homicide Data
300(3)
Variable Selection Using Ridge Regression
303(1)
Selection of Variables in an Air Pollution Study
303(7)
A Possible Strategy for Fitting Regression Models
310(2)
Bibliographic Notes
312(7)
Exercises
312(4)
Appendix: Effects of Incorrect Model Specifications
316(3)
Logistic Regression
319(16)
Introduction
319(1)
Modeling Qualitative Data
320(1)
The Logit Model
320(2)
Example: Estimating Probability of Bankruptcies
322(3)
Logistic Regression Diagnostics
325(2)
Determination of Variables to Retain
327(1)
Judging the Fit of a Logistic Regression
328(2)
Classification Problem: Another Approach
330(5)
Exercises
331(4)
Appendix: Statistical Tables 335(12)
References 347(8)
Index 355
SAMPRIT CHATTERJEE, PhD, is Professor of Statistics at New York University. A well-known research scientist and Fulbright scholar, Dr. Chatterjee has coauthored Sensitivity Analysis in Linear Regression (with Dr. Hadi) and A Casebook for a First Course in Statistics and Data Analysis, both available from Wiley. ALI S. HADI, PhD, is the Stephen H. Weiss Presidential Fellow and Professor of Statistical and Computing Sciences at Cornell University. The author/coauthor of three other books, Dr. Hadi is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. BERTRAM PRICE, PhD, is President of Price Associates, a consulting firm in Washington D.C. Dr. Price's experience spans both academia and business, including work as a business scientist at IBM, and he has served as an associate editor of The American Statistician.