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 STA 2100, STA 2260, TOM 3020, EC 3322, IME 3140, STA 341, STA 326, TOM 302, EC 322, or IME 314; and MAT 2250 or MAT 208.
Component(s): Lecture
Grading Basis: Graded Only
Repeat for Credit: May be taken only once