This is a rst course on statistical inference and modeling for use in social science research. It covers probability and the theory of statistical inference, justications for and problems with common statistical procedures, and how to apply procedures to empirical social science data to draw conclusions relevant to positive social theory. We will pay particular attention to the motivation for statistical inference and modeling from the standpoint of social science. Lectures and reading will primarily cover theory and simple examples. Problem sets will cover both simple theoretical extensions and applications of tools we develop to real data.
Students should have a working knowledge of arithmetic, algebra, and elementary calculus. The course is suitable for students with a large range of prior exposure to statistics and mathematics. Students with Ph.D.-level training in mathematical statistics from a statistics department will note that it pushes their capabilities; students with less background than this should nd at least some challenges, conceptual or technical. All students capable of gaining admission to a Berkeley Ph.D. program can fully succeed in this class regardless of prior technical preparation other than the required skills listed above.
Please note that description is from Fall 2014