STA 4320 - Applied Regression Analysis (4)

Simple linear regression and scatterplots, multiple linear regression, matrix formulation of regression, least squares estimation, interpretation of regression coefficients, hypothesis tests for regression coefficients, confounding and multicollinearity, interaction and effect modification, regression diagnostics and remedial measures, model selection, use of statistical software packages for all techniques discussed.

Prerequisite(s): Graduate standing; OR C or better in STA 2100, STA 2260, TOM 3020, EC 3322, or IME 3140; and C or better in MAT 2240 and MAT 2250.
Grading Basis: Graded Only
Repeat for Credit: May be taken only once
Course Category: Major Course