**Text Applied Linear Statistical Models 4^{th} edition**

**By
Neter,Kutner,Nachtsheim, and Wasserman**

**Credit 3 Hours**

**Prrequisite Math
3063 or equivalent, Math 4113 or equivalent
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**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.**

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**Material Covered**

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**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**

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**Learning Outcomes**

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**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 ) **

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