335 إحص: نماذج خطية معممة Generalized Linear Models

Prerequisite Course: STAT 332 (Regression Analysis)
Concurrent Requirement Course: STAT 337 (Design and analysis of experiments)

Course Description:
1. Review: some matrix algebra, some important distributions, quadratics forms, some statistical inferences methods (estimation, hypotheses testing), model fitting and some principles of statistical modeling, simple linear regression, comparing the means of two groups.
2. Exponential family and generalized linear models.
3. Sampling distribution for score statistics
4. Sampling distribution for the deviance.
5. Normal Linear Models.
6. Binary Variables and logistic Regression.
7. Nominal and ordinal logistic Regression.
8. Poisson Regression and log-linear models.
9. Clustered and Longitudinal Data
Course Main Objectives:
To introduce the theoretical and applied concepts and principles of generalized linear models to the students. This will help them in how to select the appropriate model and how to analyze the data.

Course Contents:
Week List of Topics
1, 2 Review: some matrix algebra, some important distributions, quadratics forms, some statistical inferences methods (estimation, hypotheses testing), model fitting and some principles of statistical modeling, simple linear regression, comparing the means of two groups.
3, 4 Exponential family and generalized linear models.
5 Sampling distribution for score statistics, sampling distribution for the deviance.
6 Normal Linear Models.
7, 8 Binary Variables and logistic Regression.
9 Nominal and ordinal logistic Regression.
10 Poisson Regression and log-linear models.
11 Clustered and Longitudinal Data

Textbook:
An Introduction to Generalized Linear Models, by Annette J. Dobson and Adrian G. Barnett, 4th edition, 2018, CRC Press, Taylor & Francis Group, Chapman & Hall.
References:
1. Generalized Linear Models with Examples in R, by Peter K. Dunn, Gordon K. Smyth, 2018, Springer.
2. Generalized Linear Models: with Applications in Engineering and the Sciences, by Raymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining, and Timothy J. Robison, 2nd Edition, 2010, Wiley.
3. Foundations of Linear and Generalized Linear Models, by Alan Agresti, 2015, Wiley.

Electronic Resources:
1. Access to Internet resources related to Design and Analysis of Experiments (Such as: search engine Google, YouTube, etc.).
2. Statistical software and packages (Minitab, SAS, R, SPSS, Python, etc.).
3. The student is expected to be able to analyze the data of examples and exercises using a computer; Therefore, the student must be well acquainted with one of the statistical packages and programs.

Grading:
1. Homework and Attendance = 10%
2. Quiz = 20%
3. Mid-term Exam = 30%
4. Final Exam = 40%