Linear Models (405-0-20)

Instructors

Nicole Elizabeth Wilson

Meeting Info

Scott Hall 212: Tues, Thurs 9:30AM - 10:50AM

Overview of class

This course is about linear models, the major workhorses of statistics for description and prediction, and one of the most common quantitative methods in political science. We will use a linear models framework to discuss significance tests, graphical displays, tests of assumptions, interpretation of coefficients and interactions, and questions of causal inference. We will also work through statistical computing skills such that students can use all of the above in their own work.

Registration Requirements

Political Science 403 or equivalent

Learning Objectives

In this course we will build from probability theory to understand how linear regression produces estimates of conditional expectations. By the end of the course, students will be able to use R statistical software to estimate linear regressions and extensions upon the linear model, characterize the uncertainty of those estimates, conduct tests, and present results. Students will be able to interpret the results and discuss their relevance to political science research.

Teaching Method

Lecture and discussion/lab

Evaluation Method

Problem sets, quizzes, final project

Enrollment Requirements

Enrollment Requirements: Reserved for Graduate Students.

Associated Classes

DIS - Scott Hall 212: Thurs 11:00AM - 11:50AM