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Course Details

Fall 2017-2018
* SOC 400 (QR)   No Audit

Applied Social Statistics

Matthew J. Salganik

An introduction to basic concepts in probability and statistics with applications to social science research. We cover descriptive statistics, sampling distributions, statistical inference (including point estimation, confidence intervals and tests of hypotheses), the comparison of two or more groups, linear regression, and designs for causal inference. Throughout the course we use the open-source statistical package R to illustrate and apply the techniques. The course is intended to prepare students to take Advanced Social Statistics the following term.

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
20637 S01 1:30 pm - 2:50 pm M W   Wallace Hall   165   Enrolled:12 Limit:23
20638 B01 9:00 am - 10:50 am Th   Wallace Hall   165   Enrolled:12 Limit:23