Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
BİS604 Linear Models 927001 1 1 6
Level of Course Unit
Second Cycle
Objectives of the Course
Teaching definition of models, creating statistical models, its differencies, usage areas and performing
Name of Lecturer(s)
Learning Outcomes
  1. Learning model definition and structer of statistical modelling
  2. Learning differencies between regression models and experimental designing models
  3. Analysing covariance models
  4. Learning differencies between random model and fixed models
  5. Learning hypothesis testing, usage areas and interpretations
Mode of Delivery
Formal Education
Prerequisites and co-requisities
Recommended Optional Programme Components
Recommended or Required Reading
1. Rencher, Alvin C., 2007. Linear models in statistics/Alvin C. Rencher, G. Bruce Schaalje. – 2nd ed. p. cm.Includes bibliographical references. ISBN 978-0-471-75498-5 (cloth). John Wiley & Sons, Inc.2. N.H. Bingham • John M. Fry., 2010. Regression, Linear Models in Statistics, Springer-Verlag London Limited 2010.3. C. Radhakrishna Rao. Helge Toutenburg. 1999. Linear Models: Least Squares and Alternatives, Second Edition. Springer-Verlag, New York Inc.
Planned Learning Activities and Teaching Methods
Language of Instruction
Work Placement(s)
Course Contents
Matrix operations and linear model analyis, linear regression models, variance and covariance matrix, generalized linear models. Fixed and random models, expected values. quadratik form and hypothesis testing, covariance analysis models
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Definition of model, and statistical modellig
2.Using matrix operations for analysing linear models
3.Variance and covariance matrix
4.Matrix types of multivaraite analysis (R, S and SSCP matrixes), generalized variance.
5.Full models and reduced models, usage areas
6.Simple limear regression
7.Multiple linear regression models
8.Hypothesis tests for regression models
9.Analysis of variances, fixed and random models
10.Parameter estimation methods (OLS, ML, RML vs)
11.Estimation of expected values
12.Creating design matrix and its properties
13.Covariances models and analysing
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination130
Attending Lectures1430
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination133
Final Examination133
Attending Lectures14342