COURSE UNIT TITLE

: STATISTICAL DECISION MAKING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIS 5003 STATISTICAL DECISION MAKING COMPULSORY 3 0 0 5

Offered By

Business Information Systems (English)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR SABRI ERDEM

Offered to

Business Information Systems (English)
Business Administration (English)

Course Objective

The course aims to presents the core and advanced statistical techniques as a decision making tool.

Learning Outcomes of the Course Unit

1   Have a knowledge understanding of the use of basic and advanced tools of statistics.
2   Have a knowledge understanding of the significance of multivariate data analysis and model selection criteria.
3   Demonstrate their skills and knowledge to analyze real data by using the appropriate techniques and software with a high level of confidence.
4   Perform a complete statistical analysis and develop solutions to realistic cases and apply their skills in interpreting computer output.
5   Have experience writing a report using the language of modern statistics to communicate the results and explain the managerial implication.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Statistics General Framework of Statistical World
2 Displaying and Summarizing Data Assignment cases to the teams Computer Applications -Lab Exercises
3 Sampling Distributions: Estimation
4 Inferential Statistics: Hypothesis Testing
5 Comparison two or more populations: Analysis of Variance Computer Applications -Lab Exercises
6 Regression Analysis Computer Applications -Lab Exercises
7 Regression - Additional Topics and Model Building Computer Applications -Lab Exercises
8 Midterm Exam
9 Forecasting Computer Applications -Lab Exercises
10 Correlation Analysis/ Manova / Mancova / Ancova Computer Applications -Lab Exercises
11 Factor Analysis (Exploratory + Confirmatory) Computer Applications -Lab Exercises
12 Discriminant Analysis Computer Applications -Lab Exercises
13 Case Study Presentations Submission of Case Analysis Reports Peer Evaluations
14 Case Study Presentations

Recomended or Required Reading

Text Books:
- Statistics, Data Analysis, and Decision Modeling. James R. Evans, 4th ed. or later, 2010, Pearson Education. Statistics for Business and Economics.
- Paul Newbold, W. L. Carlson and B. Thorne, 7th Ed. or later Ed., Prentice-Hall. Multivariate Data Analysis.
- Joseph F. Hair, William C. Black, Barry J. Babin and Rolph E. Anderson, 7th Edition, 2009, Pearson Education.
Additional Materials:
- Software: Minitab, SPSS ® (Statistical Package for Social Sciences)

Planned Learning Activities and Teaching Methods

The course consists of lectures, class discussions, computer applications, assignments and case analysis.

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

Lectures will focus on the transfer of basic and advanced statistical concepts and techniques where comprehension is substantially enhanced by additional elaboration and illustration.

Exams will measure the ability to identify and apply the appropriate statistic and/or method to the real problems. Each exam will cover course materials and include problems like those assigned for homework, questions on lecture materials, and additional items covered in class meetings.

Homework problems will be assigned frequently. It is imperative that a student works and understands these problems to successfully complete the course. It is strongly recommended the students to work all homework problems as a study tool for the exams. By completing homework assignments, each student will enhance analytical skills, as well as, improve competency utilizing a data analysis add-in tool and/or a statistical package for data entry and analysis. By actively participating in class discussions and in-class assignments, each student will improve communication and analytical skills through learning statistical concepts and business applications.

Case Analysis will offer an excellent opportunity for students to perform statistical analysis and develop solutions to realistic situations. For the case analysis, a team including two students should be formed. Any deviation from this target number requires approval of the instructor. The cases will be assigned to each team by the instructor in the beginning of the semester. Topics consist of the statistical analysis of a data set for the problems found in managing a business or government, whether production or service oriented.

By working with a group, students will improve teamwork, analytical, and communication skills through identifying and applying statistical analysis to the real world problems. Case reports will enable students improve their competency using the language of statistics to communicate the results.

The case analysis requires a cooperative effort. It is the responsibility of the team to assure that each team member has contributed approximately equally to the group work. Cases will be graded by the instructor and by the team members. Each member of the group will be asked at the end of the semester to evaluate his or her own contribution, and those of other team members. A peer evaluation form will be supplied during the last week of class.

Case reports will be evaluated for such factors as apparent understanding of the topic, originality of treatment and discussion, accuracy of results, comprehensiveness of the report's content and depth of the analysis, clarity and mechanics of presentation such as organization, format, punctuation, grammar, and quality of exhibits and charts.

Grade for Student Participation will depend on (i) class attendance, (ii) the quality of answers the student provides to questions posed by the instructor during class, and (iii) the general contribution the student makes to the creation of a positive learning environment.

A good attendance record may bring the grade up one level, for grades on the boundary between two grade levels.

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Plagiarism of any kind will result in disciplinary steps being taken.
3. Absence will not be considered an excuse for submitting homework assignments late.
4. Delayed case reports will suffer grade decay equivalent to one letter grade per day late.

Contact Details for the Lecturer(s)

sabri.erdem@deu.edu.tr, aysun.kapucugil@deu.edu.tr

Office Hours

To be announced at the first lecture.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 10 1,5 15
Preparation for midterm exam 1 20 20
Preparation for final exam 1 20 20
Preparing assignments 8 3 24
Preparing presentations 1 5 5
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 129

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1335555
LO.2345535
LO.335535
LO.4535
LO.5555