COURSE UNIT TITLE

: ADVANCED STATISTICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EKO 5087 ADVANCED STATISTICS COMPULSORY 3 0 0 4

Offered By

Econometrics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Econometrics

Course Objective

The main objective of the course is to define methods for linear programming and mathemmatical modelling as decision-support aid in companies.

Learning Outcomes of the Course Unit

1   To be able to define relations between science and Operations Research
2   To be able to define basic principle of Operations Research
3   To be able to classify Simplex methods
4   To be able to explain concepts of linear programming and duality
5   To be able to explain Goal programming method
6   To be able to define Integer Programming methods

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Linear Programming
2 Linear Programming methods: Simplex method
3 Application of Simplex methods
4 Optional Optimal Solution
5 Duality and Sensitivity Analysis
6 Dual Simplex method
7 Economic Interpretation of Dual Problem and Shadow Prices
8 Mid-term
9 Sensitivity Analysis
10 Sensitivity Analysis
11 Goal Programming
12 Integer Programming
13 Branch and Bound Techniques
14 Gomory Cutting Plane Method

Recomended or Required Reading

Operations Research, An Introduction, Hamdy A. Taha, Pearson

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

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 * 0.30 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40


*** 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 12 3 36
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 111

Contribution of Learning Outcomes to Programme Outcomes

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