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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：
59.00元
ISBN：
978-7-04-016323-0
版面字数：
700.000千字
开本：
16开
全书页数：
488页
装帧形式：
平装
重点项目：
暂无
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
  • 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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