2005 StatisticalModels

• (Freedman, 2005a) ⇒ David A. Freedman. (2005). “Statistical Models: theory and practice.” Cambridge University Press. ISBN:0521854830

Subject Headings: Statistical Model Family, Likelihood Function, Latent Variable, Covariance Matrix.

Notes

• http://assets.cambridge.org/052185/4830/frontmatter/0521854830_frontmatter.pdf

Cited By

• ~155 http://scholar.google.com/scholar?cites=16289748293664941985

Quotes

Book Overview

Explaining the things you need to know in order to read empirical papers in the social and health sciences, as well as techniques needed to build personal statistical models, this user-friendly volume includes background material on study design, bivariate regression, and matrix algebra. To develop technique, Freedman also includes computer labs, with sample computer programs, and illustrates the principles and pitfalls of modeling. The book is rich in exercises with answers. Target audiences include undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.

Table of Contents

1 Observational Studies and Experiments
1.1 Introduction 1
1.2 The HIP trial 4
1.3 Snow on cholera 6
1.4 Yule on the causes of poverty 9
Exercise set A 13
1.5 End notes 14

2 The Regression Line
2.1 Introduction 18
2.2 The regression line 18
2.3 Hooke’s law 22
Exercise set A 23
2.4 Complexities 23
2.5 Simple vs multiple regression 25
Exercise set B 26
2.6 End notes 28

3 Matrix Algebra
3.1 Introduction 29
Exercise set A 30
3.2 Determinants and inverses 31
Exercise set B 33
3.3 Random vectors 35
Exercise set C 35
3.4 Positive definite matrices 36
Exercise set D 37
3.5 The normal distribution 38
Exercise set E 39
3.6 If you want a book on matrix algebra 40

4 Multiple Regression
4.1 Introduction 41
Exercise set A 44
4.2 Standard errors 45
Things we don’t need 48
Exercise set B 49
4.3 Explained variance in multiple regression 50
Association or causation? 52
4.4 Generalized least squares 52
4.5 Examples on GLS 55
Exercise set C 56
4.6 What happens to OLS if the assumptions break down? 57
4.7 Normal theory 57
Statistical significance 60
Exercise set D 60
4.8 The F-test 61
“The” F-test in applied work 63
Exercise set E 63
4.9 Data snooping 64
Exercise set F 65
4.10 Discussion questions 65
4.11 End notes 72

5 Path Models
5.1 Stratification 75
Exercise set A 80
5.2 Hooke’s law revisited 81
Exercise set B 82
5.3 Political repression during the McCarthy era 82
Exercise set C 84
5.4 Inferring causation by regression 85
Exercise set D 87
5.5 Response schedules for path diagrams 88
Selection vs intervention 95
Structural equations and stable parameters 95
Ambiguity in notation 96
Exercise set E 96
5.6 Dummy variables 97
Types of variables 98
5.7 Discussion questions 99
5.8 End notes 106

6 Maximum Likelihood
6.1 Introduction 109
Exercise set A 113
6.2 Probit models 114
Why not regression? 117
The latent-variable formulation 117
Exercise set B 118
Identification vs estimation 119
What if the Ui are N(µ, s2)? 120
Exercise set C 120
6.3 Logit models 121
Exercise set D 122
6.4 The effect of Catholic schools 123
More on table 3 126
Latent variables 126
Response schedules 127
The second equation 128
Mechanics: bivariate probit 130
Why a model rather than a cross-tab? 132
Interactions 132
More on the second equation 133
Exercise set E 133
6.5 Discussion questions 135
6.6 End notes 142

7 The Bootstrap
7.1 Introduction 148
Exercise set A 159
7.2 Bootstrapping a model for energy demand 160
Exercise set B 166
7.3 End notes 167

8 Simultaneous Equations
8.1 Introduction 169
Exercise set A 174
8.2 Instrumental variables 174
Exercise set B 177
8.3 Estimating the butter model 177
Exercise set C 178
8.4 What are the two stages? 178
Invariance assumptions 179
8.5 A social-science example: education and fertility 180
More on Rindfuss et al 184
8.6 Covariates 184
8.7 Linear probability models 185
The assumptions 186
The questions 188
Exercise set D 188
8.8 More on IVLS 189
Some technical issues 189
Exercise set E 191
Simulations to illustrate IVLS 191
Further reading on econometric technique 192
8.9 Issues in statistical modeling 192
8.10 Critical literature 195
Response schedules 199
8.11 Evaluating the models in chapters 6–8 200
8.12 Summing up 200,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 StatisticalModels	David A. Freedman			Statistical Models: theory and practice			http://books.google.com/books?id=TUbKc9o4az4C			2005