- 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.
- Variance stabilizing transformations, Box-Cox method. Ridge`s regression. Weighted Regression.

- Draper, N. and Smith,H. Applied Regression Analysis, 3rd Edition, John Wiley and Sons Series in Probability and Statistics, New York, 1998.
- Montgomery, D., Peck, E., Vining, G. Introduction to Linear Regression Analysis, 5th Edition, John Wiley, New York, 2013
- Sen, A. and Srivastava, M. Regression Analysis Theory, Methods & Applications, 1st Edition, Springer-Verlag Berlin Heidelberg, New York, 1990.
- Kutner, M., Nachtsheim, C., Neter, J. and Li, W. Applied Linear Statistical Models, 5th Edition, McGraw-Hill Companies, Boston, 2005.

Pre-requisite | : | SI 427 (Exposure) (For students from other departments, instructor’s permission will be required) |

Total credits | : | 8 |

Type | : | |

Duration | : | Spring 2023 |

Name(s) of other Academic units to whom the course may be relevant | : | N/A |