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适读人群 ：大学生，大学教师
　　本书是在美国大学中广泛使用的教材，已经再版至第5版，不仅深受广大师生的欢迎，而且有很大的影响，已逐步成为经典。
　　由于篇幅较大，股起英文影印版分为上、下两册。本书深入地介绍了“应用线性统计模型”这门课程中几乎所有的关键知识，但是读起来并不艰深晦涩。书中用深入浅出的方式来讲解相关概念，同时配有大量的例题、习题以及实际案例帮助学生理解知识点。同时在帮助学生独立地解决实际问题方面，本书给人留下很深刻的印象。
　　本书图文并茂，许多例子和习题都是经过精心挑选的，来源于生活和工程实践，丰富的数据也都取材于实际案例。因此，本书不仅适用于统计专业，也可作为商业、计量经济学等专业的参考书。
　　本书叙述比较详尽，内容比国内教材丰富，篇幅较大，因此作为教材时刻适当选取主要内容讲授，其余可作为学生自学使用。

内容简介

　　本书分为三部分：第1部分简单线性回归，内容涉及单个预测变量的线性回归、利用回归和相关分析做推断、诊断和修正测度、回归分析的联合推断和其他论题以及简单线性回归分析的矩阵法等内容；第2部分多重线性回归、内容涉及多重回归Ⅰ，多重回归Ⅱ，定量和定性预测变量的回归模型、构建回归模型Ⅰ、构建回归模型Ⅱ、构建回归模型Ⅲ、时序数据中的自相关等内容；第3部分非线性回归，内容涉及非线性回归的引入和神经网络、Logistic回归、泊松回归和广义线性模型等内容。本书篇幅适中，例子涉及各个应用领域，在介绍统计思想方面比较突出，数据丰富。
　　本书适用于高等院校统计学专业和理工科各专业本科生和研究生作为教材使用。

内页插图

目录

Contents
preface
PART ONE
SIMPLE LINEAR REGRESSION 1
Chapter 1
Linear Regression with One Predictor
Variable 2
1.1 Relations between Variables 2
Functional Relation between Two
Variables 2
Statistical Relation between Two Variables 3
1.2 Regression Models and Their Uses 5
Historical Origins 5
Basic Concepts 5
Construction of Regression Models 7
Uses of Regression Analysis 8
Regression and Causality 8
Use of Computers 9
1.3 Simple Linear Regression Model
with Distribution of Error Terms
Unspecified 9
Formal Statement of Model 9
Important Features of Model 9
Meaning of Regression Parameters 11
Alternative Versions of Regression Model 12
1.4 Data for Regression Analysis 12
Observational Data 12
Experimental Data 13
Completely Randomized Design 13
1.5 Overview of Steps in Regression
Analysis 13
1.6 Estimation of Regression Function 15
Method of Least Squares 15
Point Estimation of Mean Response 21
Residuals 22
Properties of Fitted Regression Line 23
1.7 Estimation of Error Terms Variance ?2 24
Point Estimator of ?2 24
1.8 Normal Error Regression Model 26
Model 26
Estimation of Parameters by Method
of Maximum Likelihood 27
Cited References 33
Problems 33
Exercises 37
Projects 38
Chapter 2
Inferences in Regression and Correlation
Analysis 40
2.1 Inferences Concerning ?1 40
Sampling Distribution of b1 41
Sampling Distribution of (b1 -?1)/s{b1} 44
Confidence Interval for ?1 45
Tests Concerning ?1 47
2.2 Inferences Concerning ?0 48
Sampling Distribution of b0 48
Sampling Distribution of (b0 -?0)/s{b0} 49
Confidence Interval for ?0 49
2.3 Some Considerations on Making Inferences
Concerning ?0 and ?1 50
Effects of Departures from Normality 50
Interpretation of Confidence Coefficient
and Risks of Errors 50
Spacing of the X Levels 50
Power of Tests 50
2.4 Interval Estimation of E{Yh} 52
Sampling Distribution of ?Y
h 52
Sampling Distribution of
( ?Y
h - E{Yh})/s{ ?Y
h} 54
Confidence Interval for E{Yh} 54
2.5 Prediction of New Observation 55
Prediction Interval for Yh(new) when
Parameters Known 56
Prediction Interval for Yh(new) when
Parameters Unknown 57
Prediction of Mean of m New Observations
for Given Xh 60
2.6 Confidence Band for Regression Line 61
2.7 Analysis of Variance Approach
to Regression Analysis 63
Partitioning of Total Sum of Squares 63
Breakdown of Degrees of Freedom 66
x
Contents xi
Mean Squares 66
Analysis of Variance Table 67
Expected Mean Squares 68
F Test of ?1 = 0 versus ?1 _= 0 69
2.8 General Linear Test Approach 72
Full Model 72
Reduced Model 72
Test Statistic 73
Summary 73
2.9 Descriptive Measures of Linear Association
between X and Y 74
Coefficient of Determination 74
Limitations of R2 75
Coefficient of Correlation 76
2.10 Considerations in Applying Regression
Analysis 77
2.11 Normal Correlation Models 78
Distinction between Regression and
Correlation Model 78
Bivariate Normal Distribution 78
Conditional Inferences 80
Inferences on Correlation Coefficients 83
Spearman Rank Correlation Coefficient 87
Cited References 89
Problems 89
Exercises 97
Projects 98
Chapter 3
Diagnostics and Remedial Measures 100
3.1 Diagnostics for Predictor Variable 100
3.2 Residuals 102
Properties of Residuals 102
Semistudentized Residuals 103
Departures from Model to Be Studied by
Residuals 103
3.3 Diagnostics for Residuals 103
Nonlinearity of Regression Function 104
Nonconstancy of Error Variance 107
Presence of Outliers 108
Nonindependence of Error Terms 108
Nonnormality of Error Terms 110
Omission of Important Predictor
Variables 112
Some Final Comments 114
3.4 Overview of Tests Involving
Residuals 114
Tests for Randomness 114
Tests for Constancy of Variance 115
Tests for Outliers 115
Tests for Normality 115
3.5 Correlation Test for Normality 115
3.6 Tests for Constancy of Error
Variance 116
Brown-Forsythe Test 116
Breusch-Pagan Test 118
3.7 F Test for Lack of Fit 119
Assumptions 119
Notation 121
Full Model 121
Reduced Model 123
Test Statistic 123
ANOVA Table 124
3.8 Overview of Remedial Measures 127
Nonlinearity of Regression
Function 128
Nonconstancy of Error Variance 128
Nonindependence of Error Terms 128
Nonnormality of Error Terms 128
Omission of Important Predictor
Variables 129
Outlying Observations 129
3.9 Transformations 129
Transformations for Nonlinear
Relation Only 129
Transformations for Nonnormality
and Unequal Error Variances 132
Box-Cox Transformations 134
3.10 Exploration of Shape of Regression
Function 137
Lowess Method 138
Use of Smoothed Curves to Confirm Fitted
Regression Function 139
3.11 Case Example—Plutonium
Measurement 141
Cited References 146
Problems 146
Exercises 151
Projects 152
Case Studies 153
xii Contents
Chapter 4
Simultaneous Inferences and Other
Topics in Regression Analysis 154
4.1 Joint Estimation of ?0 and ?1 154
Need for Joint Estimation 154
Bonferroni Joint Confidence Intervals 155
4.2 Simultaneous Estimation of Mean
Responses 157
Working-Hotelling Procedure 158
Bonferroni Procedure 159
4.3 Simultaneous Prediction Intervals
for New Observations 160
4.4 Regression through Origin 161
Model 161
Inferences 161
Important Cautions for Using Regression
through Origin 164
4.5 Effects of Measurement Errors 165
Measurement Errors in Y 165
Measurement Errors in X 165
Berkson Model 167
4.6 Inverse Predictions 168
4.7 Choice of X Levels 170
Cited References 172
Problems 172
Exercises 175
Projects 175
Chapter 5
Matrix Approach to Simple
Linear Regression Analysis 176
5.1 Matrices 176
Definition of Matrix 176
Square Matrix 178
Vector 178
Transpose 178
Equality of Matrices 179
5.2 Matrix Addition and Subtraction 180
5.3 Matrix Multiplication 182
Multiplication of a Matrix by a Scalar 182
Multiplication of a Matrix by a Matrix 182
5.4 Special Types of Matrices 185
Symmetric Matrix 185
Diagonal Matrix 185
Vector and Matrix with All Elements
Unity 187
Zero Vector 187
5.5 Linear Dependence and Rank
of Matrix 188
Linear Dependence 188
Rank of Matrix 188
5.6 Inverse of a Matrix 189
Finding the Inverse 190
Uses of Inverse Matrix 192
5.7 Some Basic Results for Matrices 193
5.8 Random Vectors and Matrices 193
......

前言/序言

　　英文影印版序
　　本书是在美国大学中广泛使用的教材，已经再版至第5版，不仅深受广大师生的欢迎，而且有很大的影响，已逐步成为经典。
　　由于篇幅较大，股起英文影印版分为上、下两册。本书深入地介绍了“应用线性统计模型”这门课程中几乎所有的关键知识，但是读起来并不艰深晦涩。书中用深入浅出的方式来讲解相关概念，同时配有大量的例题、习题以及实际案例帮助学生理解知识点。同时在帮助学生独立地解决实际问题方面，本书给人留下很深刻的印象。
　　本书图文并茂，许多例子和习题都是经过精心挑选的，来源于生活和工程实践，丰富的数据也都取材于实际案例。因此，本书不仅适用于统计专业，也可作为商业、计量经济学等专业的参考书。
　　本书叙述比较详尽，内容比国内教材丰富，篇幅较大，因此作为教材时刻适当选取主要内容讲授，其余可作为学生自学使用。


应用线性统计模型 上册（英文影印版 原书第5版） 电子书 下载 mobi epub pdf txt

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