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.