Linear Models (405-0-20)
Instructors
Mary Caroline McGrath
Meeting Info
Scott Hall 212: Thurs 4:00PM - 5:00PM
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
Recommended prerequisite class: Pol_Sci 403 (Introduction to Probability and Statistics) 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
Weekly lecture; weekly discussion meeting/R lab; weekly TA section
Evaluation Method
Weekly problem sets; midterm exam; final exam.
Class Materials (Required)
None
Enrollment Requirements
Enrollment Requirements: Reserved for Graduate Students.
Associated Classes
DIS - Scott Hall 212: Tues 10:00AM - 10:50AM