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.(ii) For a similar example in a discrete choice model, see http://www.stat.berkeley. edu/users/census/socident.pdf (iii) ... If you are willing to assume that some variables are exogenous, you can test the exogeneity of others. 5. This procedure isanbsp;...
|Publisher||:||Cambridge University Press - 2005-08-08|