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Course Information

Course Name
Turkish İstatistiksel Modelleme ve Regresyon Analizi
English Statistical Modeling and Regression Analysis
Course Code
MAT 549E Credit Lecture
Semester -
3 3 - -
Course Language English
Course Coordinator Mustafa Nadar
Course Objectives 1. To teach the theory of simple and multiple regression analysis and how to do a model selection.
2. To teach how to do diagnostics methods and perform residual analysis.
3. To teach Logistic and Poisson regression analysis.
4. To teach the generalized linear regression models
5. To teach how to estimate parameters, interpret and validate the results obtained for multiple regression analysis.
Course Description The theory of simple and multiple linear regression analysis and modeling. Regression model selection. Residual analysis. Autocorrelation, multicolinearity. Logistic regression, Poisson regression. Generalized linear models. Applications of these models to data.
Course Outcomes At the end of the course students will have the knowledge on the following concepts and their applications
1. Construction of different types of regression.
2. Checking the assumptions of the regression model.
3. Inference and prediction of regression model.
4. Diagnostic analysis and residual analysis.
5. An understanding of appropriate and relevant, fundamental and applied mathematical and statistical
knowledge, methodologies and modern computational tools.
6. The ability to bring together and flexibly apply knowledge to characterize, analyze and solve a wide range of problems,
Pre-requisite(s) None
Required Facilities
Textbook Neter, J., Kutner, M.H., Nachtsheim, C.J., and Wasserman, W. Applied Linear Statistical Models, McGraw-Hill/Irwin, 2005, 5th edition.
Other References 1. Montgomery,D.C., Peck, E.A., Vining, G.G. Introduction to Linear Regression Analysis, Wiley, 2012, 5th edition.
2. Alan Agresti. An introduction to Categorical Data Analaysis, Wiley 2007, 2nd edition.
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