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.
An understanding of introductory
statistical concepts is needed.
Material Covered
Linear Regression with one predictor variable
Inferences in Regression Analysis
Diagnostics and Remedial Measures
Simultaneous inferences
Multiple regression models
Coefficients of partial determination
Overview of model building
Diagnostics, DFFITS, DFBETAS, and Cook’s distance
Multicollinearity
Learning Outcomes
Students will identify appropriate regression models ( L5, L7, L12)
Students will use diagnostic techniques to identify
violations of assumptions of a model. ( L11, L12)
Students will analyze residuals and their graphs to
validate regression models. ( L5, L11)
Students will interpret coefficients of the models (L5, L11 )