We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

STATISTICS FOR SOCIOLOGISTS II

C&ESOC/SOC 361
Course Description

Applied linear regression modeling for social scientists. Bivariate and multiple regression, dummy variables, interactions, nonlinear relationships, indirect effects and omitted variable bias, outliers, heteroskedasticity, and mulitcollinearity; associated diagnostics and corrections. Use of Stata and/or SAS for dataset creation and analysis.

Prerequisites

C&ESOC/SOC 360 , ECON 310 , PSYCH 210 , GEOG 360, MATH/STAT 310 , STAT 301 , or graduate/professional standing

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.5

4.57% from Historical

Completion Rate
96.55%

-0.29% from Historical

A Rate
48.28%

26.93% from Historical

Class Size
29

42.47% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

Similar Courses

ECONOMIC STATISTICS AND ECONOMETRICS II

ECON 710

Extensions of the linear regression model; introduction to multiple equation models.

STATISTICS FOR SOCIOLOGISTS III

SOC 362

Generalized linear models with selected applications to social science data. Topics: Review of multiple regression; properties of estimators; general linear restrictions; instrumental variables; two-stage least squares; panel data; fixed and random effects; logit, probit, and related models.

SEMINAR-MATHEMATICAL AND STATISTICAL APPLICATIONS IN SOCIOLOGY

SOC 952

Selected topics in measurement and model construction in social research.

STATISTICS FOR SOCIOLOGISTS I

C&ESOC/SOC 360

Presentation of sociological data; descriptive statistics; probability theory and statistical inference; estimation and tests of hypotheses; regression and correlation and the analysis of contingency tables.

APPLIED REGRESSION ANALYSIS

STAT 333

An introduction to regression with emphasis on the practical aspects. Topics include: straight-line model, role of assumptions, residual analysis, transformations, multiple regression (with some use of matrix notation), multicollinearity, subset selection, and a brief introduction to mixed models.