APPLIED MATHEMATICS (TURKISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
MAT5019 Applied Statistical Analysis Fall
Spring
3 0 3 12
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: Tr
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. İRİNİ DİMİTRİYADİS
Course Objectives: The objective of the course is to provide the student with an understanding of the basic notions of probability and statistics and their use in solving complex realistic situations. The student will also acquire spreadsheet skills.

Learning Outputs

The students who have succeeded in this course;
Completing this course the student will be able to understand the use of random variables and conditional expectation in economic problem solutions, in project financing decisions and in determining company pofit, will be able to apply correlation and regression analysis to data, will know about portfolio optimization and the design of prediction models.

Course Content

Simulation and conditional probability, discrete and continuous random variables and applications,correlation and multivariate random variables and applications, conditional expectation and linear rgeression models, simulation in decision analysis, risk sharing, dynamic models and introduction to GLM.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to analyzing data on Excel,simple simulation model, conditional expectation, probability trees and Baye's rule, advanced spreadsheet techniques.
2) Discrete random variables, simulating discrete random variables, expected value and standard deviation, estimates from sample data, decision criteria.
3) Utility theory with constant risk tolerance, risk aversion, utility analysis from simulation data, certainty equivalence and risk premium.
4) Continuous random variables, Normal distribution, logarithmic and exponential distributions, certainty equivalents of Normal lotteries, other distribution functions.
5) Correlation and multivariate random variables.
6) Portfolio analysis with multivariate normal asset returns, Excel solver and efficient portfolio design.
7) Conditional expectation, Linear Regression models.
8) Optimization of decision variables, general techniques for using simulation in decision analysis, decision trees, analyzing competitive behavior.
9) Risk sharing in finance, optimal risk sharing, risk sharing under moral hazard.
10) Corporate Decision making and asset pricing in the stock market,fundemental ideas of arbitrage pricing theory.
11) Dynamic models of growth; forecasting models with time series, brownian motion growth models, log-optimal investment strategies. Applications.
12) Introduction to generalized linear models, link functions, estimation, testing.
13) Generalized linear models continued.
14) Review and other applications.

Sources

Course Notes: Probability Models for Economic Decisions, Roger, B. Myerson, Duxubury Applied Series.
References:

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 4 % 100
Seminar % 0
Midterms % 0
Preliminary Jury % 0
Final % 0
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 0
PERCENTAGE OF FINAL WORK % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 6 5 30
Presentations / Seminar 0 0 0
Project 4 32 128
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0
Midterms 0 0 0
Paper Submission 0
Jury 0
Final 0 0 0
Total Workload 200

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Ability to assimilate mathematic related concepts and associate these concepts with each other.
2) Ability to gain qualifications based on basic mathematical skills, problem solving, reasoning, association and generalization.
3) Be able to organize events, for the development of critical and creative thinking and problem solving skills, by using appropriate methods and techniques.
4) Ability to make individual and team work on issues related to working and social life.
5) Ability to transfer ideas and suggestions, related to topics about his/her field of interest, written and verball.
6) Ability to use mathematical knowledge in technology.
7) To apply mathematical principles to real world problems.
8) Ability to use the approaches and knowledge of other disciplines in Mathematics.
9) Be able to set up and develope a solution method for a problem in mathematics independently, be able to solve and evaluate the results and to apply them if necessary.
10) To be able to link abstract thought that one has to concrete events and to transfer the solutions and examine and interpret the results scientifically by forming experiments and collecting data.
11) To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively.
12) To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself,