MTH (0144) 560 - Regression Analysis

Credits: 3.00

Students will explore simple linear regression, multiple regression, non-linear regression and logistic regression models. Students will study random and mixed effects models and penalized regression. Finally, students will learn analysis of variance models including: within subject designs, mixed models, blocking, Latin Square, path analysis, and models with categorical dependent variables.

Free Note: Open only to students in the MS in Mathematics.

Course Learning Goals: Students will:
•   Explain the context for simple linear regression. This will be assessed by Quiz 1 and the mid-term examination.
•   Evaluate simple linear regression models. This will be assessed by Quiz 1 and the mid-term examination.
•   Explain the assumptions that need to be met for a simple linear regression model to be valid. This will be assessed by Quiz 1 and the mid-term examination.
•   Explain how multiple predictors can be included into a regression model. This will be assessed by the mid-term examination and Quiz 4.
•   Explain the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid. This will be assessed by Quiz 2 and the mid-term examination.
•   Use multiple linear regression model is used to estimate and predict likely values. This will be assessed by Quiz 3 and the mid-term examination.
•   Apply categorical predictors into regression models. This will be assessed by Quiz 10 and the final examination.
•   Transform data in order to deal with problems identified in the regression model. This will be assessed by Quiz 5 and the final examination.
•   Explore strategies for building regression models. This will be assessed by all course assignments.
•   Distinguish between outliers and influential data points and how to deal with these. This will be assessed by Quiz 6 and the final examination.
•   Solve problems typically encountered in regression contexts. This will be assessed by Quiz 7 and the final examination.
•   Apply alternative methods for estimating a regression line besides using ordinary least squares. This will be assessed by Quiz 8 and the final examination.
•   Apply regression models in time dependent contexts. This will be assessed by Quiz 9 and the final examination.
•   Apply regression models in non-linear contexts. This will be assessed by the final examination.