Executive MBA
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

General course introduction information

Course Code:

E-MBA522

Course Name:

Statistical Analysis and Decision Making

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	8

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator :

Dr.Öğr.Üyesi İREM YALKI

Course Lecturer(s):

Dr.Öğr.Üyesi İLKER CALAYOĞLU

Course Assistants:

Course Objective and Content

Course Objectives:	The purpose of this course is to know the general aspects of quantitative analysis In this course, research methods, The quantitative analysis approach, regression models, forecasting, linear programming, goal programming, decision analysis, game theory will be explained in details.
Course Content:	Statistical Analysis and Decision Making Course is to teach all the techniques and methods that enable decision making under uncertainty

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Identify and define variables in statistical events. It specifies how to specify, collect and edit statistical data.
2 - Skills
Cognitive - Practical
1) Selects the appropriate methodology for data analysis. It provides the necessary skills for optimal decision making process.
3 - Competences
Communication and Social Competence
Learning Competence
1) Analyzes the decision making processes of companies. Applies research methods in various departments of companies.
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	• Discuss the differences between quantitative and qualitative analysis. • Describe the problem. • Analyzes model development.
2)	• Explain the advantages of the mathematical model. • Explain mathematical models grouped by race. • Describe the problem. • Develops models. • Obtain data entries. • Develops solutions. • Tests the solution. • Analyzes the results.
3)	• Explain the basic concepts of probability. • Lists the types of probability. • Discuss the difference between statistically dependent and independent events. • Analyzes the probability distribution of the intermittent random variable. • Analyzes the probability distribution of continuous random variables.
4)	• Solve questions with the Binomial formula. • Solve questions with Binomial tables. • Calculates the area under the normal curve. • Explains how to use the standard normal distribution chart.
5)	• Describe decision theorem. • Define decision-making under uncertainty based on optimism criteria. • Define decision-making under pessimism criteria under uncertainty. • Define decision-making under the criterion of reality (Hurwicz Criterion) under uncertainty. • Define decision making under unequal probability (laplace). • Define decision making under the minimax criterion under uncertainty.
6)	• Describes and explains sensitivity analysis. • Bayesian analysis shows how the probability values are predicted. • Calculates revised probabilities. • Discuss possible problems in the survey results. • Demonstrate measuring and forming the benefit curve.
7)	• Classify regression models. • Explain how to interpret the scatter diagrams. • Measures the suitability of the regression model. • Explain the assumption of the regression model. • Tests the meaning of the model. • Evaluates multiple regression models.
8)	• mid-term exam. • Explain the advantages of the mathematical model. • Explain mathematical models grouped by race. • Analyzes the probability distribution of the intermittent random variable. • Calculates the area under the normal curve. • Explains how to use the standard normal distribution chart. • Define decision-making under the criterion of reality (Hurwicz Criterion) under uncertainty. • Define decision making under unequal probability (laplace). • Bayesian analysis shows how the probability values are predicted. • Explain the assumption of the regression model. • Tests the meaning of the model.
9)	• Defines time series models. • Defines causality models. • Describe qualitative models. • Explain how scatter diagrams can be used to interpret time series. • Analyzes trend projections. • Analyzes seasonal changes. • Analyzes seasonal changes with trends.
10)	• Graphical representation of how constraints are used. • Isoprofit analyzes the line solution. • Analyze the corner point dissolution method. • Explain idle and residual concepts.
11)	• Analyzes the 'non-solvable' special case in linear programming. • In linear programming, 'infinity' analyzes the special case. • Analyzes the 'now' special case in linear programming. • In linear programming, the 'alternative optimal solution' analyzes the special case. • The minimization solves the problem. • Analyzes four special cases in linear programming. • Describes and explains sensitivity analysis.
12)	• Identify and explain marketing practices. • Identify and explain production practices. • Identify and explain business planning practices. • Identify and disclose their financial applications. • Identifies and explains the component mixing applications. • Identify and explain transportation practices.
13)	• Decodes the model with Binary (0-1) values. • Identify and explain nonlinear objective function and linear constraints. • Identify and explain nonlinear objective function and nonlinear constraints. • Identify and explain linear objective function and nonlinear constraints.
14)	• Identifies and explains the terminology of the game concept. • Identify and explain the Minimax criteria. • Identify and explain pure strategy games. • Identify and explain mixed strategy games. • Identify and explain dominant strategy games.
15)	Final Exam

Sources

Course Notes / Textbooks:	Statistics for Business and Economics, 9th Global Edition Paul Newbold, William Carlson and Betty Thorne 2020 Pearson.
References:	Statistics for Business and Economics, 9th Global Edition Paul Newbold, William Carlson and Betty Thorne 2020 Pearson.

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3
Program Outcomes
1) To gain basic knowledge about the basic functions in the field of business administration and to apply them in real life
2) To use scientific research methods and technology in decision processes of business
3) Evaluating the effects of political, legal, geographical, economic and technological factors on the competitive structure of the business area with team work and finding the necessary solutions
4) to be aware of current issues in the field of business and to gain sensitivity to problems
5) Transmission of written documents such as reports, printouts, internal correspondence orally in an effective manner
6) defining and analyzing the problem by forming a team within the framework of professional expertise and conceptual knowledge and using leadership qualities
7) to be able to relate to other areas of business administration, to be aware of global and social ethical norms

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To gain basic knowledge about the basic functions in the field of business administration and to apply them in real life	3
2)	To use scientific research methods and technology in decision processes of business	5
3)	Evaluating the effects of political, legal, geographical, economic and technological factors on the competitive structure of the business area with team work and finding the necessary solutions	3
4)	to be aware of current issues in the field of business and to gain sensitivity to problems	3
5)	Transmission of written documents such as reports, printouts, internal correspondence orally in an effective manner	5
6)	defining and analyzing the problem by forming a team within the framework of professional expertise and conceptual knowledge and using leadership qualities	5
7)	to be able to relate to other areas of business administration, to be aware of global and social ethical norms

Learning Activity and Teaching Methods

Expression
Brainstorming/ Six tihnking hats
Individual study and homework
Lesson
Group study and homework
Reading
Q&A / Discussion
Case Study

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Oral Examination
Individual Project
Group project
Presentation

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Workload
Course Hours	15	45
Study Hours Out of Class	15	90
Homework Assignments	15	27
Midterms	4	37
Paper Submission	4	39
Total Workload		238