Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
GENERALIZED LINEAR MODELS 803400815121 3 + 0 3.0 10.0

Prerequisites None

Language of Instruction English
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals Learning types of discrete multivariate and categorical data; and appropriate analysis
Course Content Learning types of discrete multivariate and categorical data; and appropriate analysis
Learning Outcomes 1) Simple matrix and vector operations, transpoze notasyonları will be learned
2) Expected values and variances of matrices and vectors in quadratic form obtains distributions of some special quadratic forms
3) Estimator of variance in full rank models, knows how to obtain confidence intervals of estimators and their functions

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Basic matrix operations, transpose and notations of vectors Lecture

 Homework
2. Week Orthogonality and inverses of matrices, eigenvalues and eigenvectors Lecture

 Homework
3. Week Ranks, traces of matrices and idempotent matrices Lecture

 Homework
4. Week Quadratic forms, expectation of random vector or matrix and variance-covariance matrix of random vectors, distributions of some special quadratic forms Lecture

 Homework
5. Week Chi-square, student-t and F distributions, independence of quadratic forms Lecture

 Homework
6. Week Matrix formulation of the full rank models, parameter estimation of the full rank models Lecture

 Homework
7. Week Estimation of variance for the full rank models, confidence intervals of estimators and their functions Lecture

 Homework
8. Week Joint confidence region for regression coefficients in the full rank models Lecture

 Homework
9. Week Hypothesis testing for regression coefficients in the full rank models, partial and squential tests and hypothesis test for subvectors of regression coefficients Lecture

 Homework
10. Week Week Parameter estimation and hypothesis tests in less than full rank models Lecture

 Homework
11. Week Reparameterization in in less than full rank models Lecture

 Homework
12. Week To find and use of generalize (conditional) inverses in less than full rank models Lecture

 Homework
13. Week Estimations of variance and confidence intervals for in less than full rank models Lecture

 Homework
14. Week Estimability for in less than full rank models Lecture

 Homework

Sources Used in This Course
Recommended Sources
"Faraway, Julian J. (2016) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition, Chapman & Hall/CRC Texts in Statistical Science, ISBN-10: 9781498720960; ISBN-13: 978-1498720960 "
"McCullagh, P., Nelder. John A. (1989) Generalized linear models, Chapman and Hall/CRC; 2 edition, ISBN-10: 0412317605; ISBN-13: 978-0412317606 "
"McCulloch, Charles E., Searle, Shayle R., Neuhaus, John M. (2008) Generalized, Linear, and Mixed Models, Wiley-Interscience; 2 edition, ISBN-10: 0470073713; " "Myers ve Milton (1991) A First Course in the Theory of Linear Statistical Models, PWS-KENT; ISBN-10: 0534916457, ISBN-13: 978-0534916459 "

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15555
PY25555
PY35555

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 9
Homework 1 5
Presentation (Including Preparation Time) 1 20
Project (Including Preparation and presentation Time) 1 5
Report (Including Preparation and presentation Time) 1 5
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 1 5
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 1 5
Seminar 1 5
Internship 1 1
Quiz 1 1
Time to prepare for Quiz 1 5
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 1
Time to prepare for Final Exam 1 40
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload