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Statistical Models : Theory and Practice by David A. Freedman (2009, Trade Paperback)

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Statistical Models: Theory and Practice

Informazioni su questo prodotto

Product Identifiers

PublisherCambridge University Press

ISBN-100521743850

ISBN-139780521743853

eBay Product ID (ePID)72684850

Product Key Features

Number of Pages458 Pages

Publication NameStatistical Models : Theory and Practice

LanguageEnglish

SubjectProbability & Statistics / General

Publication Year2009

FeaturesRevised

TypeTextbook

Subject AreaMathematics

AuthorDavid A. Freedman

FormatTrade Paperback

Dimensions

Item Height1 in

Item Weight26.5 Oz

Item Length9.6 in

Item Width6.6 in

Additional Product Features

Edition Number2

Intended AudienceScholarly & Professional

LCCN2009-002062

Dewey Edition22

Reviews'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, Professor of Statistics, Vrije Universiteit Amsterdam, '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, a modern introduction to the subject, discusses graphical models and simul-taneous 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, Professor of Statistics, 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, '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, '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, 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, Professor of Mathematics and Statistics, Stanford University, '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, "For three decades, David Freedman has been the conscience of statistics as applied to important scientific, policy, and legal issues. This book is his legacy, and it is our great good fortune to have the new edition. It should be required reading for any user of multivariate models -- statistician or otherwise -- whose ultimate concern is not with statistical technique but rather with the substantive conclusions, if any, licensed by the data and the analysis." James M. Robins, Professor of Epidemiology and Biostatistics, Harvard School of Public Health, '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, "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, "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, '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, Professor of Mathematics and Statistics, Stanford University, "A pleasure to read, this newly revised edition of 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, Professor of Political Science, Yale University, "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, and we are extremely fortunate to now have the revised edition." Persi Diaconis, Professor of Mathematics and Statistics, Stanford 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. This revised edition organizes the chapters differently, making reading much easier. Moreover, it includes many new examples and exercises. In summary, it is a nice and extremely useful addition to the statistical literature." Heleno Balfarine, Mathematical Reviews, 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, Bendheim Professor of Economics and Public Policy, 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, Professor of Statistics, Vrije Universiteit Amsterdam, '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, 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, A. Whitney Griswold Professor of Political Science, Yale 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, Professor of Statistics Emeritus, University of California, Berkeley

IllustratedYes

Dewey Decimal300/.1/519536

Edition DescriptionRevised edition

Table Of Content1. Observational studies and experiments; 2. The regression line; 3. Matrix algebra; 4. Multiple regression; 5. Multiple regression: special topics; 6. Path models; 7. Maximum likelihood; 8. The bootstrap; 9. Simultaneous equations; 10. Issues in statistical modeling.

SynopsisThis 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., This lively and engaging book explains the basic ideas of association and regression, and tells you 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., 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

LC Classification NumberQA279.F74 2009