Sinopsis:
Statistical Models is a lively and engaging textbook that explains the things you have to know in order to read empirical papers in the social and health sciences, as well as techniques you need to build statistical models of your own. Freedman illustrates the principles of modeling, and the pitfalls.
Críticas:
'... 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' stones' in applied statistics. This is an inspiring book and a very good read, for teachers as well as students.' Professor Gesine Reinert, Oxford 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
'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, Director of the Institution for Social and Policy Studies, Yale University
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