Students will gain an understanding of the linear model and gain practical experience applying linear model to analyze real-life data. The course objective are:
- Understand the statistical foundation of the linear model including major distributions.
- Understand how to interpret the results of regression analysis.
- Assessing the quality of the regression model.
- Understand how to set the model structure in analysis of variance.
Evaluation of Students
Student will evaluated based on homework, projects, midterm and final examinations.
- Introduction to Linear Models
- Inference in Linear Regression
- Regression Diagnostics
- Regression in Matrix Notation
- Multiple Regression
- Qualitative Predictor Variables
- Model Building Strategies
- Single Factor Analysis of Variance
- Analysis of Covariance
- Two Factor Analysis of Variance
Textbooks & Software
Neter, Kutner, Nachtsheim and Wasserman, Applied Linear Statistical Models
Submitted by: M. R. Kafai
Date: February 9, 2006