Regression Analysis I: Introduction


  • Michelle Dion, McMaster University

Participants in this workshop should have mastered at least one semester of basic introductory statistics, including levels of measurement, descriptive statistics, sampling distributions, statistical inference, and hypothesis testing. The workshop will provide an introduction to bivariate (one independent variable) and multivariate (multiple independent variables) linear regression models using elementary algebra. Topics will include the development of the regression model, analysis of variance, parameter estimation, hypothesis testing, applications, interpretation, and the practical implications of violating regression assumptions. Assignments will emphasize hands-on application of the methods learned in the text and class meetings. The level of the course will be approximately that of Lewis-Beck's Applied Regression (Sage) or Berry and Sanders's Understanding Multivariate Research: A Primer for Beginning Social Scientists (Westview Press).

Fees: Consult the fee structure.

Tags: regression, least squares, bivariate regression, ANOVA, R-square, linear models, dummy variables, scalar regression, heteroscedasticity, Specification error, OLS, analysis of residuals

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): June 26 - July 21

Time: 3:00 PM - 5:00 PM


  • Michelle Dion, McMaster University