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| 內容簡介： |
本书是一本优秀的统计模型教材，着重讲解线性模型的应用问题，包括广义最小二乘和两步最小二乘模型，以及二分变量的probit及logit模型的应用，还包括关于研究设计、二分变量回归及矩阵代数的背景知识。
这还是一本鼓舞人心的而又易读的书，无论是老师还是学生都会从中受益。
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| 關於作者： |
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（美）弗里德曼,是加州大学伯克利分校的统计学教授、杰出的数理统计学家。其研究范围包括鞅不等式分析、Markov过程、抽样、自助法等。他是美国科学学院士。在2003年。美国科学院授予他John J.Carty科学进步奖，以表彰他对统计理论和实践做出的贡献。
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| 目錄：
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Foreword to the ReVised Edition
Preface
1 Observational Studies and Experiments
1.1 Introduction
1.2 The HIP trial
1.3 Snow on cholera
1.4 Yule on the causes of poverty
Exercise set A
1.5 End notes
2 The Regression Line
2.1 Introduction
2.2 The regression line
2.3 Hooke''s law
Exercise set A
2.4 Complexities
2.5 Simple vs multiple regression
Exercise set B
2.6 End notes
3 Matrix Algebra
3.1 Introduction
Exercise set A
3.2 Determinants and inverses
Exercise set B
3.3 Random vectors
Exercise set C
3.4 Positive definite matrices
Exercise set D
3.5 The normal distribution
Exercise set E
3.6 If you want a book on matrix algebra
4 Multiple Regression
4.1 Introduction
Exercise set A
4.2 Standard errors
Things we don''t need
Exercise set B
4.3 Explained variance in multiple regression
Association or causation?
Exercise set C
4.4 What happens to OLS if the assumptions break down?
4.5 Discussion questions
4.6 End notes
5 Multiple Regression: Special Topics
5.1 Introduction
5.2 OLSisBLUE
Exercise set A
5.3 Generalized least squares
Exercise set B
5.4 Examples on GLS
Exercise set C
5.5 What happens to GLS if the assumptions break down?
5.6 Normal theory
Statistical significance
Exercise set D
5.7 The F-test
"The" F-test in applied work
Exercise set E
5.8 Data snooping
Exercise set F
5.9 Discussion questions
5.10 End notes
6 Path Models
6.1 Stratification
Exercise set A
6.2 Hooke''s law revisited
Exercise set B
6.3 Political repression during the McCarthy era
Exercise set C
6.4 Inferring causation .by regression
Exercise set D
6.5 Response schedules for path diagrams
Selection vs intervention
Structural equations and stable parameter:Ambiguity in notation
Exercise set E
6.6 Dummy variables
Types of variables
6.7 Discussion questions
6.8 End notes
7 Maximum Likelihood
7.1 Introduction
Exercise set A
7.2 Probit models
Why not regression?
The latent-variable formulation
Exercise set B
Identification vs estimation
What if the Ui are N?
Exercise set C
7.3 Logit models
Exercise set D
7.4 The effect of Catholic schools
Latent variables
Response schedules
The second equation
Mechanics: bivariate probit
Why a model rather than a cross-lab?
Interactions
More on table 3 in Evans and Schwab
More on the second equation
Exercise set E
7.5 Discussion questions
7.6 End notes
8 The Bootstrap
8.1 Introduction
Exercise set A
8.2 Bootstrapping a model for energy demand
Exercise set B
8.3 End notes
9 Simultaneous Equations
9.1 Introduction
Exercise set A
9.2 Instrumental variables
Exercise set B
9.3 Estimating the butter model
Exercise set C
9.4 What are the two stages?
Invariance assumptions
9.5 A social-science example: education and fertility
More on Rindfuss et al
9.6 Covariates
9.7 Linear probability models
The assumptions
The questions
Exercise set D
9.8 More on IVLS
Some technical issues
Exercise set E
Simulations to illustrate IVLS
9.9 Discussion questions
9.10 End notes
10 Issues in Statistical Modeling
10.1 Introduction
The bootstrap
The role of asymptotics
Philosophers'' stones
The modelers'' response
10.2 Critical literature
10.3 Response schedules
10.4 Evaluating the models in chapters 7-9
10.5 Summing up
References
Answers to Exercises
The Computer Labs
Appendix: Sample MATLAB Code
Reprints
Gibson on McCarthy
Evans and Schwab on Catholic Schools
Rindfuss et al on Education and Fertility
Schneider et al on Social Capital
Index
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