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经典和现代回归分析及其应用（第2版）(影印版)

作者：
Raymond H.Myers
定价：
35.50 元
版面字数：
700.00千字
开本：
16开
装帧形式：
平装
页数：
488
最新
印次时间：
暂无
ISBN：
978-7-04-016323-0
物料号：
16323-00
出版时间：
2005-05-10
读者对象：
高等教育
一级分类：
数学与统计学类
二级分类：
统计学专业课
三级分类：
回归分析
暂无
  • 目录
    • CHAPTER 1 INTRODUCTION: REGRESSION ANALYSIS
      • 1.1 Regression models
        • 1.2 Formal uses of regression analysis 1.3 The data base References
        • CHAPTER 2 THE SIMPLE LINEAR REGRESSION MODEL
          • 2.1 The model description
            • 2.2 Assumptions and interpretation of model parameters
              • 2.3 Least squares formulation
                • 2.4 Maximum likelihood estimation
                  • 2.5 Partioning total variability
                    • 2.6 Tests of hypothesis on slope and intercept
                      • 2.7 Simple regression through the origin (Fixed intercept)
                        • 2.8 Quality of fitted model
                          • 2.9 Confidence intervals on mean response and prediction intervals
                            • 2.10 Simultaneous inference in simple linear regression
                              • 2.11 A complete annotated computer printout
                                • 2.12 A look at residuals
                                  • 2.13 Both x and y random
                                    • Exercises
                                      • References
                                      • CHAPTER 3 THE MULTIPLE LINEAR REGRESSION MODEL
                                        • 3.1 Model description and assumptions
                                          • 3.2 The general linear model and the least squares procedure
                                            • 3.3 Properties of least squares estimators under ideal conditions
                                              • 3.4 Hypothesis testing in multiple linear regression
                                                • 3.5 Confidence intervals and prediction intervals in multiple regressions
                                                  • 3.6 Data with repeated observations
                                                    • 3.7 Simultaneous inference in multiple regression
                                                      • 3.8 Multicollinearity in multiple regression data
                                                        • 3.9 Quality fit, quality prediction, and the HAT matrix
                                                          • 3.10 Categorical or indicator variables (Regression models and ANOVA models)
                                                            • Exercises
                                                              • References
                                                              • CHAPTER 4 CRITERIA FOR CHOICE OF BEST MODEL
                                                                • 4.1 Standard criteria for comparing models
                                                                  • 4.2 Cross validation for model selection and determination of model performance
                                                                    • 4.3 Conceptual predictive criteria (The Cp=statistic)
                                                                      • 4.4 Sequential variable selection procedures
                                                                        • 4.5 Further comments and all possible regressions
                                                                          • Exercises
                                                                            • References
                                                                            • CHAPTER 5 ANALYSIS OF RESIDUALS
                                                                              • 5.1 Information retrieved from residuals
                                                                                • 5.2 Plotting of residuals
                                                                                  • 5.3 Studentized residuals
                                                                                    • 5.4 Relation to standardized PRESS residuals
                                                                                      • 5.5 Detection of outliers
                                                                                        • 5.6 Diagnostic plots
                                                                                          • 5.7 Normal residual plots
                                                                                            • 5.8 Further comments on analysis of residuals
                                                                                              • Exercises
                                                                                                • References
                                                                                                • CHAPTER 6 INFLUENCE DIAGNOSTICS
                                                                                                  • 6.1 Sources of influence
                                                                                                    • 6.2 Diagnostics: Residuals and the HAT matrix
                                                                                                      • 6.3 Diagnostics that determine extent of influence
                                                                                                        • 6.4 Influence on performance
                                                                                                          • 6.5 What do we do with high influence points?
                                                                                                            • Exercises
                                                                                                              • References
                                                                                                              • CHAPTER 7 NONSTANDARD CONDITIONS. VIOLATIONS OF ASSUMPTIONS, AND TRANSFORMATIONS
                                                                                                                • 7.1 Heterogeneous variance: Weighted least squares
                                                                                                                  • 7.2 Problem with correlated errors (Autocorrelation)
                                                                                                                    • 7.3 Transformations to improve fit and prediction
                                                                                                                      • 7.4 Regression with a binary response
                                                                                                                        • 7.5 Further developments in models with a discrete response (Poisson regression)
                                                                                                                          • 7.6 Generalized linear models
                                                                                                                            • 7.7 Failure of normality assumption: Presence of outliers
                                                                                                                              • 7.8 Measurement errors in the regressor variables
                                                                                                                                • Exercises
                                                                                                                                  • References
                                                                                                                                  • CHAPTER 8 DETECTING AND COMBATING MULTICOLLINEARITY
                                                                                                                                    • 8.1 Multicollinearity diagnostics
                                                                                                                                      • 8.2 Variance proportions
                                                                                                                                        • 8.3 Further topics concerning multicollinearity
                                                                                                                                          • 8.4 Alternatives to least squares in cases of multicollinearity
                                                                                                                                            • Exercises
                                                                                                                                              • References
                                                                                                                                              • CHAPTER 9 NONLINEAR REGRESSION
                                                                                                                                                • 9.1 Nonlinear least squares
                                                                                                                                                  • 9.2 Properties of the least squares estimators
                                                                                                                                                    • 9.3 The Gauss-Newton procedure for finding estimates
                                                                                                                                                      • 9.4 Other modifications of the Gauss-Newton procedure
                                                                                                                                                        • 9.5 Some special classes of nonlinear models
                                                                                                                                                          • 9.6 Further considerations in nonlinear regression
                                                                                                                                                            • 9.7 Why not transform data to linearize?
                                                                                                                                                              • Exercises
                                                                                                                                                                • References
                                                                                                                                                                • APPENDIX A SOME SPECIAL CONCEPTS IN MATRIX ALGEBRA
                                                                                                                                                                  • A.1 Solutions to simultaneous linear equations
                                                                                                                                                                    • A.2 Quadratic form
                                                                                                                                                                      • A.3 Eigenvalues and eigenvectors
                                                                                                                                                                        • A.4 The inverses of a partitioned matrix
                                                                                                                                                                          • A.5 Sherman-Morrison-Woodbury theorem References
                                                                                                                                                                          • APPENDIX B SOME SPECIAL MANIPULATIONS
                                                                                                                                                                            • B.1 Unbiasedness of the residual mean square
                                                                                                                                                                              • B.2 Expected value of residual sum of squares and mean square for an underspecified model
                                                                                                                                                                                • B.3 The maximum likelihood estimator
                                                                                                                                                                                  • B.4 Development of the PRESS statistic
                                                                                                                                                                                    • B.5 Computation of s●
                                                                                                                                                                                      • B.6 Dominance of a residual by the corresponding model error
                                                                                                                                                                                        • B.7 Computation of influence diagnostics
                                                                                                                                                                                          • B.8 Maximum likelihood estimator in the nonlinear model
                                                                                                                                                                                            • B.9 Taylor series
                                                                                                                                                                                              • B.10 Development of the C,-statistic References
                                                                                                                                                                                              • APPENDIX C STATISTICAL TABLES
                                                                                                                                                                                                • INDEX

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