POL 571: Quantitative Analysis I



This course is the first course in applied statistical methods for social scientists. Students will learn how statistical methods can be used to conduct causal inferences, exploratory data analysis, forecasting, and hypothesis testing. The first half of the course will be devoted to probability theory, which serves as a foundation of statistical theory. The second half covers the linear model in some depth and if time permits also introduces generalized linear models. An emphasis of the course is given to practical data analysis, and students will learn statistical programming as well as basic principles of probability theory and statistical inference. This course assumes the mathematical knowledge taught in POL 502, and prepares students for the next course in the sequence, POL 572. Download the syllabus.


Probability and Independence : Probability, Counting Methods, Conditional Probability and Independence.
Random Variables and Probability Distributions : Random Variables and Distribution Functions, Probability Density and Mass Functions, Random Vector and Joint Distributions.
Expectation and Functions of Random Variables : Expectation and Independence, Moments and Conditional Expectation, Expectation and Inequalities, Functions of Random Variables.
Convergence of Random Variables : Random Sample and Statistics, Convergence of Random Variables.

© Kosuke Imai
  Last modified: Thu May 4 23:02:51 EDT 2006