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Linear Models (405-0-20)

Instructors

Nicole E. Wilson
2001 Sheridan Road, 3205
Office Hours: By appointment only

Meeting Info

2001 Sheridan GPC 3111: Mon, Wed 9:30AM - 10:50AM

Overview of class

This course is about linear models (regression), which involve a set of techniques that show up across nearly all areas of statistics. Regression models are used as a powerful tool to describe data, have value for forecasting, and are sometimes pressed into service for work in theory-testing and causal inference. Across these use cases, linear models are everywhere in political science. We will talk about regression as a tool for data summary; discuss ideas about generalization and significance tests in the context of these models; develop skills with common graphical displays; clarify key assumptions and explore available tests of assumptions; practice interpreting the results of regressions; and discuss when/if regression can speak to causal questions. In each of these areas, we will connect our quest for understanding with hands-on statistical computing work.

Registration Requirements

Recommended: Political Science 403 or equivalent

Learning Objectives

By the end of this course, students will be able to:

  • Explain the core logic of how linear regression produces estimates
  • Use R statistical software to estimate linear regression and extensions
  • Compute and communicate the uncertainty of estimates from linear regression
  • Clearly and correctly interpret, explain, and display statistical results
  • Articulate how linear models can be used effectively in political science research

Evaluation Method

Problem sets, quizzes, final project

Enrollment Requirements

Enrollment Requirements: Reserved for Graduate Students.