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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6036 MULTIVARIATE STATISTICAL MODELS COMPULSORY 3 0 0 9

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR KADIR ERTAŞ

Offered to

Econometrics

Course Objective

The main objective of the course is to understand and interpret methods of reduction theory of multivariate statistical dimension, to implement methods of reduction of multivariate statistical dimension in social science and sciences.

Learning Outcomes of the Course Unit

1   To be able to form factor model and implement it in random samples by p-variable. 2   To be able to understand discriminant model and implement it in random samples by p-variable. 3   To be able to understand assumptions of canonical correlation analysis, it s theory and disjunction structure of p×1 random vector. 4   To be able to understand and implement clustering analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Maximum Likelihood Theory for Normal Distribution Model with p-variable 2 Maximum Likelihood Theory for Normal Distribution Model with p-variable 3 Factor Analysis and Its implementation 4 Factor Analysis and Its implementation 5 Factor Analysis and Its implementation 6 Discriminant Analysis and Its implementation 7 Discriminant Analysis and Its implementation 8 Mid-term 9 Canonical Correlation Analysis and It s implementation 10 Canonical Correlation Analysis and It s implementation 11 Canonical Correlation Analysis and It s implementation 12 Clustering Analysis and It s implementation 13 Clustering Analysis and It s implementation 14 Clustering Analysis and It s implementation

Recomended or Required Reading

Multivariate Analysis, Academic Press , K.V. Mardia, J.T. Kent, J.M. Biby.

Planned Learning Activities and Teaching Methods

This course will be presented using methods of expression, discussion and solving problem.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.25 + STT * 0.25 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.25 + STT * 0.25 + RST* 0.50 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 4 52 Preparation for midterm exam 1 35 35 Preparation for final exam 1 35 35 Preparing assignments 1 30 30 Preparing presentations 1 30 30 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 227

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 1 LO.2 1 LO.3 1 LO.4 1