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El. knyga: Statistical Models: Theory and Practice

4.21/5 (76 ratings by Goodreads)
(University of California, Berkeley)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 27-Apr-2009
  • Leidėjas: Cambridge University Press
  • Kalba: eng
  • ISBN-13: 9781107384415
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  • Formatas: EPUB+DRM
  • Išleidimo metai: 27-Apr-2009
  • Leidėjas: Cambridge University Press
  • Kalba: eng
  • ISBN-13: 9781107384415
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This lively and engaging book 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 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 modelling, 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. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.

Recenzijos

'At last, a second course in statistics that is serious, correct, and interesting. The book teaches regression, causal modeling, maximum likelihood, and the bootstrap. Everyone who analyzes real data should read this book.' Persi Diaconis, Stanford University 'This book is outstanding for the clarity of its thought and writing. It prepares readers for a critical assessment of the technical literature in the social and health sciences, and provides a welcome antidote to the standard formulaic approach to statistics.' Erich L. Lehmann, University of California, Berkeley 'In Statistical Models, David Freedman explains the main statistical techniques used in causal modeling - and where the skeletons are buried. Complex statistical ideas are clearly presented and vividly illustrated with interesting examples. Both newcomers and practitioners will benefit from reading this book.' Alan Krueger, Princeton University 'Regression techniques are often applied to observational data with the intent of drawing causal conclusions. In what circumstances is this justified? What are the assumptions underlying the analysis? Statistical Models answers these questions. The book is essential reading for anybody who uses regression to do more than summarize data. The treatment is original, and extremely well written. Critical discussions of research papers from the social sciences are most insightful. I highly recommend this book to anybody who engages in statistical modeling, or teaches regression, and most certainly to all of my students.' Aad van der Vaart, Vrije Universiteit Amsterdam 'A pleasure to read, Statistical Models shows the field's most elegant writer at the height of his powers. While most textbooks hurry past core assumptions in order to explicate technique, this book places the spotlight on the core assumptions, challenging readers to think critically about how they are invoked in practice.' Donald Green, Yale University 'Statistical Models, a modern introduction to the subject, discusses graphical models and simultaneous equations among other topics. There are plenty of instructive exercises and computer labs. Especially valuable is the critical assessment of the main 'philosophers's stones' in applied statistics. This is an inspiring book and a very good read, for teachers as well as students.' Gesine Reinert, Oxford University 'Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation.' Mathematical Reviews

Daugiau informacijos

Explains the basic ideas of association and regression, taking you through the current models that link these ideas to causality.
Foreword to the Revised Edition xi
Preface xiii
Observational Studies and Experiments
Introduction
1(3)
The HIP trial
4(2)
Snow on cholera
6(3)
Yule on the causes of poverty
9(5)
Exercise set A
13(1)
End notes
14(4)
The Regression Line
Introduction
18(1)
The regression line
18(4)
Hooke's law
22(1)
Exercise set A
23(1)
Complexities
23(3)
Simple vs multiple regression
26(2)
Exercise set B
26(2)
End notes
28(1)
Matrix Algebra
Introduction
29(2)
Exercise set A
30(1)
Determinants and inverses
31(4)
Exercise set B
33(2)
Random vectors
35(1)
Exercise set C
35(1)
Positive definite matrices
36(2)
Exercise set D
37(1)
The normal distribution
38(2)
Exercise set E
39(1)
If you want a book on matrix algebra
40(1)
Multiple Regression
Introduction
41(4)
Exercise set A
44(1)
Standard errors
45(6)
Things we don't need
49(1)
Exercise set B
49(2)
Explained variance in multiple regression
51(2)
Association or causation?
53(1)
Exercise set C
53(1)
What happens to OLS if the assumptions break down?
53(1)
Discussion questions
53(6)
End notes
59(2)
Multiple Regression: Special Topics
Introduction
61(1)
OLS is BLUE
61(2)
Exercise set A
63(1)
Generalized least squares
63(2)
Exercise set B
65(1)
Examples on GLS
65(3)
Exercise set C
66(2)
What happens to GLS if the assumptions break down?
68(1)
Normal theory
68(4)
Statistical significance
70(1)
Exercise set D
71(1)
The F-test
72(2)
``The'' F-test in applied work
73(1)
Exercise set E
74(1)
Data snooping
74(2)
Exercise set F
76(1)
Discussion questions
76(2)
End notes
78(3)
Path Models
Stratification
81(6)
Exercise set A
86(1)
Hooke's law revisited
87(1)
Exercise set B
88(1)
Political repression during the McCarthy era
88(3)
Exercise set C
90(1)
Inferring causation by regression
91(3)
Exercise set D
93(1)
Response schedules for path diagrams
94(9)
Selection vs intervention
101(1)
Structural equations and stable parameters
101(1)
Ambiguity in notation
102(1)
Exercise set E
102(1)
Dummy variables
103(2)
Types of variables
104(1)
Discussion questions
105(7)
End notes
112(3)
Maximum Likelihood
Introduction
115(6)
Exercise set A
119(2)
Probit models
121(7)
Why not regression?
123(1)
The latent-variable formulation
123(1)
Exercise set B
124(1)
Identification vs estimation
125(1)
What if the Ui are N (μ, σ2)?
126(1)
Exercise set C
127(1)
Logit models
128(2)
Exercise set D
128(2)
The effect of Catholic schools
130(11)
Latent variables
132(1)
Response schedules
133(1)
The second equation
134(2)
Mechanics: bivariate probit
136(2)
Why a model rather than a cross-tab?
138(1)
Interactions
138(1)
More on table 3 in Evans and Schwab
139(1)
More on the second equation
139(1)
Exercise set E
140(1)
Discussion questions
141(9)
End notes
150(5)
The Bootstrap
Introduction
155(12)
Exercise set A
166(1)
Bootstrapping a model for energy demand
167(7)
Exercise set B
173(1)
End notes
174(2)
Simultaneous Equations
Introduction
176(5)
Exercise set A
181(1)
Instrumental variables
181(3)
Exercise set B
184(1)
Estimating the butter model
184(2)
Exercise set C
185(1)
What are the two stages?
186(1)
Invariance assumptions
187(1)
A social-science example: education and fertility
187(5)
More on Rindfuss et al
191(1)
Covariates
192(1)
Linear probability models
193(4)
The assumptions
194(1)
The questions
195(1)
Exercise set D
196(1)
More on IVLS
197(3)
Some technical issues
197(1)
Exercise set E
198(1)
Simulations to illustrate IVLS
199(1)
Discussion questions
200(7)
End notes
207(2)
Issues in Statistical Modeling
Introduction
209(3)
The bootstrap
211(1)
The role of asymptotics
211(1)
Philosophers' stones
211(1)
The modelers' response
212(1)
Critical literature
212(5)
Response schedules
217(1)
Evaluating the models in chapters 7-9
217(1)
Summing up
218(1)
References
219(16)
Answers to Exercises
235(59)
The Computer Labs
294(16)
Appendix: Sample MATLAB Code
310(5)
Reprints
Gibson on McCarthy
315(28)
Evans and Schwab on Catholic Schools
343(34)
Rindfuss et al on Education and Fertility
377(25)
Schneider et al on Social Capital
402(29)
Index 431
David A. Freedman is Professor of Statistics at the University of California, Berkeley. He has also taught in Athens, Caracas, Jerusalem, Kuwait, London, Mexico City, and Stanford. He has written several previous books, including a widely used elementary text. He is one of the leading researchers in probability and statistics, with 200 papers in the professional literature. He is a member of the American Academy of Arts and Sciences. In 2003, he received the John J. Carty Award for the Advancement of Science from the National Academy of Sciences, recognizing his 'profound contributions to the theory and practice of statistics'. Freedman has consulted for the Carnegie Commission, the City of San Francisco, and the Federal Reserve, as well as several departments of the US government. He has testified as an expert witness on statistics in law cases that involve employment discrimination, fair loan practices, duplicate signatures on petitions, railroad taxation, ecological inference, flight patterns of golf balls, price scanner errors, sampling techniques, and census adjustment.