QMBU301- Quantitative Methods in Business Spring 2014 Instructor: Dr. Evrim Didem Güneş (
[email protected]) Class Time and Location: Section 1: Tuesday- Thursday 9:30 ENGZ15 Section 2: Tuesday- Thursday 12:30 ENG Z15 Office: CAS 247 Office Hours: Tuesday, Thursday 14:30-15:30 or by appointment Phone: (212) 338 1639 Teaching Assistant: TBA URL for OPSM 301: http://home.ku.edu.tr/~egunes/qmbu301 Twitter account: @QMBU301 Textbook
“Business Forecasting”, by J. Hanke and D. Wichern
Overview
Managing any aspect of a business requires making decisions under uncertainty – be it investment portfolio, annual budgeting/hiring, or weekly production decisions. This course introduces students to tools facilitating managerial decision making under uncertainty: Regression and forecasting techniques help identify patterns in the past or current experiences. These patterns are projected onto new situations to enable business decision making and planning. Decision analysis uses these predictions about the consequences different decisions and their likelihood to identify the best course of action. Covered topics are forecasting techniques and applications, regression and decision trees. We focus on forecasting methods that have been most useful in business and administration problems. These include moving averages, exponential smoothing, decomposition methods, transformations, regression with time series. Other methods are mentioned along with their advantages and disadvantages. Business applications from different industries and functional areas are used. Computer based tools are used extensively. Objectives This course is designed to develop the quantitative skills required to make effective decisions under uncertainty, specifically to o identify the right method(s) for the situation o apply the methods correctly o use software tools to facilitate analyses o identify and choose variables o check for model appropriateness o assess validity of analyses Exams: There will be two midterm exams and a final exam. Make-up exams will be given only if there is a valid excuse.
Labs Sessions The lab sessions are intended to give the student a chance to work through problems using computer software. The evaluation of lab performance will be based on attendance and lab exercises. You have to attend the lab session that you are registered in. There is absolutely no make up for missed labs. Attendance: Attendance is not required; however you will receive credit for attendance and participation. Study Exercises will be given for your practice but will not be graded. They focus on applying the methods, evaluating their adequacy and recommendation of a course of action based on analyses. Software: We will use Excel to implement the methods covered in class exercises and examples. Grading: Midterm exams 2x20=40%% Final exam 30% Lab sessions 20% Attendance and participation 10% Total 100% Academic Honesty Copying from others or providing answers or information, written or oral, to others is cheating. Copying from another student’s paper or from another text without written acknowledgment is plagiarism. Unauthorized help from another person or having someone else write one’s paper or assignment is collusion. Cheating, plagiarism and collusion are serious offences resulting in an F grade and disciplinary action. Tentative Course Plan Week
Topic
1-3
A Review of basic statistical concepts Regression Simple linear regression
3-6
7-12
13-14
Multiple regression analysis Midterm 1 Business forecasting Moving averages Simple averages , Moving averages , Double moving averages Smoothing methods Simple exponential smoothing , Holt’s method , Winters’ method Decomposition methods Regression-based methods Midterm 2 Decision making under uncertainty
