Introduction to Regression

Introduction to Regression
Regression, transformations, residuals, indicator variables, variable selection, logistic regression, time series, observational studies, statistical software.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 230 & MATH 112
 RecommendedStat 123 and Stat 124; MATH 113 or concurrent enrollment.
 TaughtFall, Winter
 ProgramsContaining STAT 330
Course Outcomes

Regression Model

Fit a regression model with professional statistical software

Response Variable

Apply appropriate transformations to the response variable to improve agreement with regression assumptions

Residuals and Influence Diagnostics

Use residuals and influence diagnostics to assess model fit, agreement with regression assumptions, and identify outliers and influential observations

Indicator Variables

Create sets of indicator variables for categorical explanatory variables

Stepwise Selection

Apply stepwise selection to identify a subset regression model that selects the most significant explanatory variables from a large data set

Statistical Software

Fit a logistic regression model with professional statistical software