Statistical Models: Theory and Practice

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Statistical Models.rar (1.46 MB) 本附件包括：

• Statistical Models_Theory and Practice.pdf

2009-11-30 01:36:31 上传

Statistical Models: Theory and Practice
By David A. Freedman

Publisher:   Cambridge University Press
Number Of Pages:   456
Publication Date:   2009-04-27
ISBN-10 / ASIN:   0521112435
ISBN-13 / EAN:   9780521112437

Product Description:
This lively and engaging textbook explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The author, David A. Freedman, explains the basic ideas of association and regression, and takes you through the current models that link these ideas to causality. The focus is on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs with sample computer programs. The book is rich in exercises, most with answers. Target audiences include advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modeling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. Features of the book: • authoritative guidance from a well-known author with wide experience in teaching, research, and consulting • careful analysis of statistical issues in substantive applications • no-nonsense, direct style • versatile structure, enabling the text to be used as a text in a course, or read on its own • text that has been thoroughly class-tested at Berkeley • background material on regression and matrix algebra • plenty of exercises, most with solutions • extra material for instructors, including data sets and code for lab projects (available from Cambridge University Press) • many new exercises and examples • reorganized, restructured, and revised chapters to aid teaching and understanding

Summary: Very well-written...very rigorous. Fairly conventional.
Rating: 5
This book is a very well-written, but ultimately fairly conventional textbook on linear models in statistics. It offers a very clear elementary introduction to the mathematics of the material, with an emphasis on both applications and rigor. It is to-the-point and does not cover very much material, instead choosing to cover material thoroughly and demonstrate the application of the material in practical situations.
I have heard this book described as "skeptical". It is not unduly skeptical; the author is just being the way every statistician ought to be. Any statistician who is not "skeptical" in this sense is accepting sloppy work.
The writing style in this book is very clear. Freedman is an outstanding writer! The book makes use of a decent amount of linear algebra and other mathematical notation that can be difficult for people to get through, but Freedman provides a very gentle introduction to the notation both through the text and through exercises (broken into small pieces, with a smooth gradient of difficulty). If you take your time and work through the book, you will not find it difficult to read.
Still, this book is not the be-all and end-all of texts on statistical models. It is particularly lacking on philosophical depth when it comes to the mathematical theory. This book describes techniques that are common practice and teaches you how to use them properly and evaluate them critically. It does not probe very deeply into how or why these techniques were developed. It does not encourage the reader to question the techniques themselves or to create new techniques or new theory. In my opinion, this is a shortcoming worth mentioning.
Also, there are a wide variety of topics that this book seems to ignore. By ignore, I not only mean that it does not cover them but that it is written almost as if these subjects do not exist. These subjects include, among others, causal inference, Bayesian statistics, and decision theory. For example, the book accepts squared error loss as a given, and other options, such as mean absolute error loss leading to quantile regression, are not even mentioned. I think the author should at least acknowledge these other perspectives and branches of statistics, briefly discuss how they relate to the material covered in the book, and point the reader to other texts to cover such material.
Is this a good book? I see it on many peoples' shelves. Personally, I found it immensely useful for learning linear regression properly. It is outstanding for self-study and would make a good textbook as well. But it does not stand on its own, even if all one wants to learn is regression. For what it is, this book is simply amazing; know its limitations, however, before buying it.

Summary: The Best Statistics Book I've Seen
Rating: 5

The Best Statistics Book I've Seen
I spent my life focusing on the errors of statistics and how they sometimes fail us in real life, because of the misinterpretation of what the techniques can do for you. This book is outstanding in the following two aspects: 1) It is of immense clarity, embedding everything in real situations, 2) It uses the real-life situation to critique the statistical model and show you the limit of statistic. For instance, he shows a few anecdotes here and there to illustrate how correlation between two variables might not mean anything causal, or how asymptotic properties may not be relevant in real life.
This is the first statistics book I've seen that cares about presenting statistics as a tool to GET TO THE TRUTH.
Please buy it.
Nassim Nicholas Taleb

Summary: A critical guide to critical thinking
Rating: 5
Whether we know it or not, assertions about causality based on regression models from the social and health fields guide decisionmakers to make or break policies that affect all of us. Students learn the mechanics of how to do a regression model without paying much attention to the assumptions behind the model and when it is appropriate to claim causality. The rest of us use the results without questioning whether the assumptions are justified in a particular case.
You don't have to be a hardcore mathematician to understand David Freedman's explanations about the "how" of statistical modelling, and most importantly, the "why," and the "when" of modelling. Dr. Freedman's writing style is direct and he provides many useful examples of when the techniques are appropriate. He provides exercises followed by detailed explanations of the correct answers. This book is certainly of great value to students but I also recommend it to those who use the causality statements from the models to make decisions.

Summary: NOW i get it
Rating: 5
The formal reviews say this book is very well written. That is an understatement. Freedman uses plain English and interesting examples to explain the concepts behind the statistical jargon. This book is certainly good for those who will go on to advanced statistics and those who can read mathematical notation more easily than words. For those of us who need to apply the results of statistical studies but who do not wish to gain graduate degrees in statistics, Freedman gives us the background to understand studies we have to use, an understanding of whether regression is an appropriate model for specific situations, and the tools to ensure we are making appropriate comparisons. This book IS well written because it leads to understanding concepts rather than mechanical memorization.

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关键词：Statistical statistica statistic Practice practic Statistical Theory models Practice

好书，多谢楼主分享

令人头疼的统计学啊。。还是要乖乖看书
   谢~~

thanks a lot for sharing!

多谢分享!!!

多谢分享
本文来自: 人大经济论坛 详细出处参考：http://www.pinggu.org/bbs/viewth ... &from^^uid=272437

好书 多谢lz分享

Table of Contents
Foreword to the Revised Edition xi
Preface xiii
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 26
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
vi STATISTICAL MODELS
4 Multiple Regression
4.1 Introduction 41
Exercise set A 44
4.2 Standard errors 45
Things we don’t need 49
Exercise set B 49
4.3 Explained variance in multiple regression 51
Association or causation? 53
Exercise set C 53
4.4 What happens to OLS if the assumptions break down? 53
4.5 Discussion questions 53
4.6 End notes 59
5 Multiple Regression: Special Topics
5.1 Introduction 61
5.2 OLS is BLUE 61
Exercise set A 63
5.3 Generalized least squares 63
Exercise set B 65
5.4 Examples on GLS 65
Exercise set C 66
5.5 What happens to GLS if the assumptions break down? 68
5.6 Normal theory 68
Statistical significance 70
Exercise set D 71
5.7 The F-test 72
“The” F-test in applied work 73
Exercise set E 74
5.8 Data snooping 74
Exercise set F 76
5.9 Discussion questions 76
5.10 End notes 78
6 Path Models
6.1 Stratification 81
Exercise set A 86
6.2 Hooke’s law revisited 87
Exercise set B 88
6.3 Political repression during the McCarthy era 88
Exercise set C 90
TABLE OF CONTENTS vii
6.4 Inferring causation by regression 91
Exercise set D 93
6.5 Response schedules for path diagrams 94
Selection vs intervention 101
Structural equations and stable parameters 101
Ambiguity in notation 102
Exercise set E 102
6.6 Dummy variables 103
Types of variables 104
6.7 Discussion questions 105
6.8 End notes 112
7 Maximum Likelihood
7.1 Introduction 115
Exercise set A 119
7.2 Probit models 121
Why not regression? 123
The latent-variable formulation 123
Exercise set B 124
Identification vs estimation 125
What if the Ui are N(μ, σ2)? 126
Exercise set C 127
7.3 Logit models 128
Exercise set D 128
7.4 The effect of Catholic schools 130
Latent variables 132
Response schedules 133
The second equation 134
Mechanics: bivariate probit 136
Why a model rather than a cross-tab? 138
Interactions 138
More on table 3 in Evans and Schwab 139
More on the second equation 139
Exercise set E 140
7.5 Discussion questions 141
7.6 End notes 150
8 The Bootstrap
8.1 Introduction 155
Exercise set A 166
viii STATISTICAL MODELS
8.2 Bootstrapping a model for energy demand 167
Exercise set B 173
8.3 End notes 174
9 Simultaneous Equations
9.1 Introduction 176
Exercise set A 181
9.2 Instrumental variables 181
Exercise set B 184
9.3 Estimating the butter model 184
Exercise set C 185
9.4 What are the two stages? 186
Invariance assumptions 187
9.5 A social-science example: education and fertility 187
More on Rindfuss et al 191
9.6 Covariates 192
9.7 Linear probability models 193
The assumptions 194
The questions 195
Exercise set D 196
9.8 More on IVLS 197
Some technical issues 197
Exercise set E 198
Simulations to illustrate IVLS 199
9.9 Discussion questions 200
9.10 End notes 207
10 Issues in Statistical Modeling
10.1 Introduction 209
The bootstrap 211
The role of asymptotics 211
Philosophers’ stones 211
The modelers’ response 212
10.2 Critical literature 212
10.3 Response schedules 217
10.4 Evaluating the models in chapters 7–9 217
10.5 Summing up 218
References 219
Answers to Exercises 235
TABLE OF CONTENTS ix

谢谢楼主，好书！

好书，我顶