Simple and multiple linear regression models – estimation, tests and confidence regions.
Simultaneous testing methods- Bonferroni method etc.
Analysis of Variance for simple and multiple regression models. Analysis of residuals. Lack of
fit tests. Checks (graphical procedures and tests) for model assumptions: Normality, homogeneity
of errors, independence, correlation of covariates and errors. Multicollinearity, outliers,
leverage and measures of influence.
Model selection (stepwise, forward and backward, best subset selection) and model validation.
Discussion of algorithms for model selection. Regression models with indicator variables.
Polynomial regression models. Regression models with interaction terms. Transformation of
response variables and covariates.