《经典和现代回归分析及其应用》纯英文影印版，Many volumes have been written by statisticians and scientists with the resultbeing that the arsenal of effective regression methods has increased manyfold. My intent for this second edition is to provide a rather substantial increase inmaterial related to classical regression while continuing to introduce relevant newand modern techniques. I have included major supplements in simple linearregression that deal with simultaneous influence， maximum likelihood estimationof parameters， and the plotting of residuals. In multiple regression， new andsubstantial sections on the use of the general linear hypothesis， indicator variables，the geometry of least squares， and relationship to ANOVA models are added.
CHAPTER 1
INTRODUCTION: REGRESSION ANALYSIS
Regression models
Formal uses of regression analysis
The data base
References
CHAPTER 2
THE SIMPLE LINEAR REGRESSION MODEL
The model description
Assumptions and interpretation of model parameters
Least squares formulation
Maximum likelihood estimation
Partioning total variability
Tests of hypothesis on slope and intercept
Simple regression through the origin （Fixed intercept）
Quality of fitted model
Confidence intervals on mean response and prediction intervals
Simultaneous inference in simple linear regression
A complete annotated computer printout
A look at residuals
Both x and y random
Exercises
References
CHAPTER 3
THE MULTIPLE LINEAR REGRESSION MODEL
Model description and assumptions
The general linear mode] and the least squares procedure
Properties of least squares estimators under ideal conditions
Hypothesis testing in multiple linear regression
Confidence intervals and prediction intervals in multiple regressions
Data with repeated observations
Simultaneous inference in multiple regression
Multicollinearity in multiple regression data
Quality fit， quality prediction， and the HAT matrix
Categorical or indicator variables (Regression models and ANOVA models）
Exercises
References
CHAPTER 4
CRITERIA FOR CHOICE OF BEST MODEL
Standard criteria for comparing models
Cross validation for model selection and determination of model performance
Conceptual predictive criteria (The Cp statistic）
Sequential variable selection procedures
Further comments and all possible regressions
Exercises
References
CHAPTER 5
ANALYSIS OF RESIDUALS 209
Information retrieved from residuals
Plotting of residuals
Studentized residuals
Relation to standardized PRESS residuals
Detection of outliers
Diagnostic plots
Normal residual plots
Further comments on analysis of residuals
Exercises
References
CHAPTER 6
INFLUENCE DIAGNOSTICS
Sources of influence
Diagnostics: Residuals and the HAT matrix
Diagnostics that determine extent of influence
Influence on performance
What do we do with high influence points?
Exercises
References
CHAPTER 7
NONSTANDARD CONDITIONS， VIOLATIONS OF ASSUMPTIONS，AND TRANSFORMATIONS
Heterogeneous variance: Weighted least squares
Problem with correlated errors (Autocorrelation）
Transformations to improve fit and prediction
Regression with a binary response
Further developments in models with a discrete response (Poisson regression）
Generalized linear models
Failure of normality assumption: Presence of outliers
Measurement errors in the regressor variables
Exercises
References
CHAPTER 8
DETECTING AND COMBATING MULTICOLLINEARITY
Multicollinearity diagnostics
Variance proportions
Fu
书单推荐
