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Course Evaluation Results

Course Details

Fall 2018-2019
* SOC 400 (QR)   No Audit

Applied Social Statistics

Brandon M. Stewart

A rigorous first course in regression with applications to social science. Assuming only basic math, the course covers probability, inference from random samples and multiple regression. Throughout we provide an introduction to programming with the open-source statistical package R, provide examples from current social science research and give an introduction to modern causal inference techniques.

Sample reading list:
John Fox, Applied Regression Analysis and Generalized Linear Models
Joshua Angrist & Jorn-Steffen Pischke, Mostly Harmless Econometrics: An Empiricist's Companion
Norman Matloff, The Art of R Programming: A Tour of Statistical Software
Blitzstein & Hwang, Introduction to Probability

Reading/Writing assignments:
Weekly problem sets (including two which disallow collaboration) and a final exam.

Requirements/Grading:
Take Home Final Exam - 30%
Problem set(s) - 70%

Other Requirements:
Statistical, design or other software use required

Prerequisites and Restrictions:
No formal prerequisites, but students will benefit from a background in basic calculus and linear algebra. Undergraduates interested in the course are advised to take an introductory statistics course first such as POL 345/SOC 305 or comparable courses in another department..

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
21132 S01 08:30:00 am - 09:50:00 am M W   Green Hall   0-S-6   Enrolled:19 Limit:45
21133 B01 09:00:00 am - 10:50:00 am Th   Green Hall   0-S-6   Enrolled:19 Limit:45