Applied linear modelling emphasizing data analysis using software including statistical inference review, visualization, multiple regression, logistic regression, and extensions. Core topics include assumptions, estimation, confidence/prediction intervals, hypothesis testing, diagnostics, indicator variables, cross validation, prediction, model building and model assessment. Other topics may include random effects or smoothing methods.

Antirequisite(s): Statistical Sciences 3859A/B, Statistical Sciences 3860A/B.

Prerequisite(s): A minimum mark of 60% in Statistical Sciences 2858A/B or a minimum mark of 70% in one of Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Biology 2244A/B, Economics 2222A/B, MOS 2242A/B, Psychology 2812A/B or the former Psychology 2810. Enrollment in a module offered by either the Department of Statistical and Actuarial Sciences, Mathematics or Applied Mathematics. Pre-or Corequisite(s): Statistical Sciences 2864A/B.

Extra Information: 3 lecture hours.