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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 5050 APPLIED MULTIPLE VARIABLE DATA ANALYSIS ELECTIVE 3 0 0 4

Offered By

Econometrics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ISTEM KESER

Offered to

Econometrics

Course Objective

The main object of the course, the most widely used multivariate statistical techniques and their basic concepts, assumptions and practices to give.

Learning Outcomes of the Course Unit

1   The relationship between the two variables simultaneously present with canonical correlation analysis 2   Discriminant (separation) or by using logistic regression techniques to classify groups of units, a large number of pre-determined according to the specification 3   Basic component of a multi-dimensional variable space or factor space of more abstract interpretation by using Principal Component Analysis and Factor Analysis 4   On different techniques to examine the assumptions of multivariate statistical techniques 5   Factor analysis of a case study defines the structure of a multi-dimensional variable space hidden behind

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Multivariate Hypotesis Testing 2 Principal Component Analysis 3 Factor Analysis 4 Factor Rotation Methods 5 Multidimensional Scaling 6 Multiple Linear Regression 7 Logistic Regression Analysis 8 Mid-term 9 Separation (Discriminant) Analysis 10 Cluster Analysis 11 Path Analysis 12 Canonical Correlation Analysis 13 Application of multivariate statistical techniques by using statistical software 14 Application of multivariate statistical techniques by using statistical software more

Recomended or Required Reading

1- Suggested Sources for the Course: Johnson R.A., Wichern D.W., Applied Multivariate Statistical Analysis, Prentice Hall, New Jersey 2- Alpar R., Uygulamalı Çok Değişkenli Istatistiksel Yöntemler, Detay Yayıncılık, Ankara.

Planned Learning Activities and Teaching Methods

Discussing, Problem solving, Lecture Method, Question-Answer Method

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 * 0.30 + STT * 0.20 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + 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 2 26 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 111

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 LO.1 1 LO.2 1 LO.3 1 1 1 LO.4 1 1 1 LO.5 1 1 1 1