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.
Required Skills. Students should have completed PS230 or its equivalent with a B or better.
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 not
nd 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.
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