Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
LINEAR MODELS 801000725130 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery Oral presentation
Course Coordinator
Instructors
Assistants
Goals To make statistical inference related with linear models
Course Content Linear and quadratic forms, expectation operations for matrices and their functions, distribution of quadratic forms, general linear hypothesis and least squares theory, full and less than full rank models, design models variance components models, estimation, hypothesis testing and correlation analysis, anova and regression, tolerans intervals, simultaneous inference, multiple comparisons
Learning Outcomes 1) Learn the concepts of vector and matrix.
2) Learn the random vectors and vector spaces
3) Have basic information about estimating the parameter vector
4) makes statistical inference about parameters
5) have information about some of the linear model types

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Vector spaces, inner product spaces, orthogonal projections, Generalized inverse of matrices Lecture

 Presentation (Including Preparation Time)
2. Week Multivariate normal distribution, distribution of quadratic forms of normally distributed random vector Lecture

 Presentation (Including Preparation Time)
3. Week Linear Models, Some Examples Linear Models, Experimental Design Models Lecture; Case Study

 Presentation (Including Preparation Time)
4. Week Parameter Estimation, Linear Predictibility, Gauss-Markov Theorem Lecture

 Presentation (Including Preparation Time)
5. Week Hypothesis testing, confidence intervals Lecture

 Presentation (Including Preparation Time)
6. Week Some Linear Model Applications Testing the Hypothesis, residual Analysis Problem Solving; Case Study

 Presentation (Including Preparation Time)
7. Week General Linear Models, Singular Models, false or unknown covariance are correlated errors Lecture

 Presentation (Including Preparation Time)
8. Week Variance Components and Mixed Models, REML Lecture

 Presentation (Including Preparation Time)
9. Week Random Coefficients Linear Models, Longitudinal Data Lecture

 Presentation (Including Preparation Time)
10. Week Measurement Error in Linear Models Case Study

 Presentation (Including Preparation Time)
11. Week Generalized Linear Models, Logistic Model Lecture

 Presentation (Including Preparation Time)
12. Week Log-Linear Models, Proportional Hazard Models Lecture

 Presentation (Including Preparation Time)
13. Week Bayesian Analysis of Linear Models, thick-tailed Linear Models, MCMC Lecture

 Presentation (Including Preparation Time)
14. Week Applications Lecture

 Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
Akdeniz, F. ve Öztürk, F. (1996). Lineer Modeller, Ankara Üniversitesi Fen Fakültesi Yayınları.
Graybill, F.A. (1976). Theory and Application of the Linear Model, Duxbury Press.
Rencher, A.C. and Schaalje, G.B. (2008). Linear Models in Statistics, Wiley-Interscience.
Sengupta, D. and Jammalamadaka, S.R. (2003). Linear Models-An Integrated Approach, World Scientific.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 10
Work Hour outside Classroom (Preparation, strengthening) 14 4
Homework 4 6
Midterm Exam 1 4
Time to prepare for Midterm Exam 1 3
Final Exam 1 10
Time to prepare for Final Exam 1 3
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload