Introduction to Probability and Applied Statistics

Catalog description

The goal of this course is to introduce probability and statistics as fundamental building blocks for quantitative political analysis, with regression modeling as a focal application. We will begin with a brief survey of probability theory, types of measurements, and descriptive statistics. The bulk of the course then addresses inferential statistics, covering in detail sampling, methods for estimating unknown quantities, and methods for evaluating competing hypotheses. We will see how to formally assess estimators, and some basic principles that help to ensure optimality. Along the way, we will introduce the use of regression models to specify social scientific hypotheses, and employ our expanding repertoire of statistical concepts to understand and interpret estimates based on our data. Weekly lab exercises require students to deploy the methods both 'by hand' so they can grasp the basic mathematics, and by computer to meet the conceptual demands of non-trivial examples and prepare for independent research. Some time will be spent reviewing algebra, calculus, and elementary logic, as well as introducing computer statistical packages. (GOVT-AM)