Summer 2024 Quantitative Methods: Social Sciences S5019 section 001


Call Number 10314
Day & Time
MW 9:00am-12:10pm
503 Hamilton Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Gregory M Eirich
Method of Instruction In-Person
Course Description

This course is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. Topics to be covered include various data structures, basic descriptive statistics, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, data visualization, models for binary outcomes, models for ordered data, first difference analysis, factor analysis, and cluster analysis. Through a variety of lab assignments, students will be able to generate and interpret quantitative data in helpful and provocative ways. Only relatively basic mathematics skills are assumed, but some more advanced math will be introduced as needed. A previous introductory statistics course that includes linear regression is helpful, but not required.

Web Site Vergil
Subterm 05/20-06/28 (A)
Department Summer Session (SUMM)
Enrollment 25 students (50 max) as of 1:07PM Sunday, July 14, 2024
Subject Quantitative Methods: Social Sciences
Number S5019
Section 001
Division Summer Session
Section key 20242QMSS5019S001