Statistical Models: Theory and Practice
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.
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0—1 dummy variable age at ﬁrst analysis APaup assume assumptions asymptotic bias biased bivariate probit bootstrap cancer Catholic schools causal inference chapter coefﬁcients Communists correlation covariance matrix deﬁned design matrix Discuss brieﬂy distribution District dummy variable elite endogenous error term Evans and Schwab example Exercise set exogenous variables experiments Family Income ﬁgure ﬁnd ﬁnding ﬁrst birth ﬁt ﬁxed Freedman high school identiﬁable independent inﬂuence instrumental variables intercept intolerance Journal latent variables likelihood function linear mass opinion MATLAB measure normal null hypothesis OLS estimator parents population positive deﬁnite probability probit model public school random error random variables regression equation regression model residuals respondent’s response schedule Rindfuss sample school choice Science simulation social capital speciﬁcation standard errors statistical models Stouffer Suppose test scores theorem theory tolerance unbiased variance 02 women