Statistical Models

Theory and Practice

David Freedman

414 pages, parution le 07/12/2005

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Résumé

This lively and engaging textbook provides the knowledge required to read empirical papers in the social and health sciences, as well as the techniques needed to build statistical models. The author explains the basic ideas of association and regression, and describes the current models that link these ideas to causality. He focuses on applications of linear models, including generalized least squares and two-stage least squares. 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's discussion is organized around published studies, as are the numerous exercises - many of which have answers included. Relevant papers reprinted at the back of the book are thoroughly appraised by the author.

Enormously respected and well-known author
Features plenty of exercises - most with solutions - and includes background material on matrix algebra and regression
Thoroughly class-tested over many years at Berkeley, and has extra material for instructors available from the editor's website.

L'auteur - David Freedman

David Freedman ; University of California, Berkeley

Sommaire

Observational studies and experiments
The regression line
Matrix algebra
Multiple regression
Path models
Maximum likelihood
The bootstrap
Simultaneous equations
Refferences
Answers to exercises
The computer labs
Appendix: sample MATLAB code

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Cambridge University Press
Auteur(s)	David Freedman
Parution	07/12/2005
Nb. de pages	414
Format	16,5 x 24
Couverture	Broché
Poids	670g
Intérieur	Noir et Blanc
EAN13	9780521671057
ISBN13	978-0-521-67105-7