Text Applied Linear Statistical Models 4th edition
By Neter,Kutner,Nachtsheim, and Wasserman
Credit 3 Hours
Prrequisite Math 3063 or equivalent, Math 4113 or equivalent
Description In this course, simple linear regression as well as multiple regression will be studied. Attention will be given to methods of model building, diagnostics for examining the appropriateness of a model, and remedial measures that may be helpful when the model is not appropriate. There will be extensive use of the statistical package Minitab. An understanding of introductory statistical concepts is needed.
Linear Regression with one predictor variable
Inferences in Regression Analysis
Diagnostics and Remedial Measures
Multiple regression models
Coefficients of partial determination
Overview of model building
Diagnostics, DFFITS, DFBETAS, and Cook’s distance
Students will identify appropriate regression models ( L5, L7, L12)
Students will use diagnostic techniques to identify violations of assumptions of a model and use remedial measures to correct the violations. ( L11, L12)
Students will analyze residuals and their graphs to validate regression models. ( L5, L11)
Students will interpret coefficients of the models (L5, L11 )
Students will build regression models using computer-assisted selection procedures.
3 exams 60%
A project will be required of all graduate students. The project will consist of the analysis of a data set of the appendix through a collection of problems found at the end of each chapter.