**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. There will be extensive use of the
statistical package Minitab. 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 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.**

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**Grading **

**3 exams 60%**

**Project 20%**

**Final
20%**

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**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.**

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