Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
QMT 3001	BUSINESS FORECASTING	ELECTIVE	4	0	0	6

Offered By

BUSINESS ADMINISTRATION

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR SABRI ERDEM

Offered to

International Business and Trade
BUSINESS ADMINISTRATION

Course Objective

The course aims to provide students the core issues of generating and implementing business forecasts. Focus is on modern statistical methods that are widely used to generate business forecasts. Specific applications to business include forecasting sales, production, inventory, macroeconomic factors such as interest rates and exchange rates, and other aspects of both short- and long-term business planning.

Learning Outcomes of the Course Unit

1	Have a knowledge understanding of the use of basic tools of forecasting and basic time series analysis techniques.
2	Have a knowledge understanding of the significance of data analysis and model selection criteria.
3	Demonstrate a good understanding of moving average and exponential smoothing forecasting techniques, and the Box-Jenkins method of forecasting.
4	Apply their skills and knowledge to forecast real economic, business and financial time series by moving average and exponential smoothing techniques and the Box-Jenkins method using a statistical package program.
5	Demonstrate their ability to analyze time series in the business environment using the appropriate methods with a high level of confidence.
6	Have experience developing a complete forecast and apply their skills in interpreting computer output and report writing.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

ECN 1904 - PRINCIPLES OF MACROECONOMICS

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to Forecasting
2	A review of Basic Statistical Concepts
3	Simple Regression
4	Multiple Regression Analysis
5	Multiple Regression Analysis
6	Regression with Time Series Data
7	Exploring Data Patterns and Forecasting Techniques
8	Moving Averages and Smoothing Methods
9	Time Series and Their Components
10	The Box-Jenkins (ARIMA)-Methodology
11	The Box-Jenkins (ARIMA)-Methodology
12	Judgmental Forecasting and Forecast Adjustments

Recomended or Required Reading

1.Text Book:
Business Forecasting. John E. Hanke and Dean W. Wichern, 9th Edition, Pearson Education, 2009.

2. References
Business Forecasting. J. Holton Wilson and Barry Keating, 6th Edition, Irwin/McGraw-Hill, 2009.
Forecasting Methods and Applications. Spyros Makridais, Steven C. Wheelwright and Rob. J. Hyndman, 3th Edition or later. John Wiley and Sons Inc.

3. Software:
Minitab
SPSS ® (Statistical Package for Social Sciences)
Excel

4. Calculator:
Students will need a scientific calculator for various calculation problems in and out of class, and during exams.

Planned Learning Activities and Teaching Methods

1. Lectures
Class lecture is highly interactive and format is direct. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic tools of forecasting and basic time series analysis techniques where comprehension is substantially enhanced by additional elaboration and illustration.
2. Text Readings
Each week, readings from the text will introduce new forecasting concepts and quantitative techniques. Readings provide both the theoretical background and technical skills necessary to generate and interpret business forecasts at an advanced level.
3. Review Sessions and Class Discussions
Review sessions will be handled frequently by the instructor. In-class assignments and homework assignments are the basis of problems to be solved in these sessions. Individual participation by students in classroom discussion is strongly encouraged.
4. Computer Applications
In the laboratory component, a particular statistical package will be introduced to perform analyses of real economic, business and financial time series data.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MT	Midterm
2	ASS	Assignment
3	FN	Final
4	FCG	FINAL COURSE GRADE	MT * 0.25 + ASS * 0.50 +FN * 0.25
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MT * 0.25 + ASS * 0.50 + RST * 0.25

Further Notes About Assessment Methods

1. Assignments
There will be four assignments. Assignments will measure students' ability to use computer to solve forecasting problems. Assignments will be graded based on only the results.

2. Exams
Exams will measure students' ability to interpret and use computer outputs that they generated for the assignments. Students are required to complete the assignments to answer the questions in the midterm and final exams. These exams will based on their own assignments.

Important: Students are obliged to bring their assignment preperation papers to the exams. If not, they will not be able to answer the questions. Assignment that are not submitted until the due date will not count towards the assignment grades; however, students who have not submitted the assignments on time can still work on their assignments for the midterm and final exams.

Assessment Criteria

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Violations of Plagiarism of any kind will result in disciplinary steps being taken.
3. Students are required to have their own calculator for this course. It will not be allowed to share a calculator during exams. Cellular phones cannot be used as a calculator during an exam.

Contact Details for the Lecturer(s)

Prof. Dr. Sabri Erdem
Email: sabri.erdem@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)