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# Students Projects

Project (Fall 2018-2019): Distribution Theory of Simple Linear Regression with Applications
Student: Mariam A. Orabi U14123143
Supervisor: Dr. Luai M. Al-Labadi

Abstract: Regression analysis is a statistical methodology that utilizes the relation between two or more variables so that one variable can be predicted from the other, or the others (Neter, Kutner, Nachtsheim and Wasserman, 1996). Building valid models for real-life data is one of the most important approaches of statistics for analyzing data which is widely used today. Successful applications require understanding of both the underlying theory and the practical problems that are encountered when building models for real-life data. The simplest form of regression is the simple linear regression, which considers only one explanatory variable linearly related to the response variable. Understanding simple linear regression enables the reader to look at many aspects of regression in the simplest possible setting. In this project, we thoroughly demonstrated the concept of simple linear regression. The model parameters are estimated using two methods, namely, least squares method and maximum likelihood method. It is shown that the estimators are equal in both methods, and proven to be good estimates, for which, the proof of Gauss-Markov theorem is provided. In order to check the validity of the model, statistical inferences about the parameters along with diagnostic methods to assure the validity of the model’s assumptions are explained. When encountering specific problems, suggestions and some transformations are provided to overcome such problems. An example is included, and used to apply all the illustrated theory using RStudio software. The R-code and the results are shown in complete details.

Project (Spring 2017-2018): Bayesian Inference: One-parameter Models
Students: Israa F.M. Alhamarna