Gsu Robinson College of Business Administration

Gsu Robinson College of Business Administration


MGS3100 - Business Analysis

Spring 2010 Course Syllabus – K. Levine

Classes:CRN#: 17012, Section #045, Monday, 4:30-7:00pm, Classroom South 306

CRN#: 14090, Section #035, Friday, 1:00-3:30pm, GCB300

Instructor: Dr. Kenneth C. Levine

E-Mail: (Note: e-mail is the best form of contact!)

Office: RCB 1047 - by appointment

Phone:770-633-9322 (cell)

Instructor (then select MGS3100)

Departmental Course website:

Course Overview

This course provides a framework for using models in support of decision-making in an enterprise. Some of the commonly used modeling approaches and principles are introduced. Topics covered include general modeling concepts, spreadsheet modeling, simulation, forecasting, quality management, statistical process control, and decision analysis. The course emphasizes hands-on application of the techniques using commonly available software, and demonstrates the value of these approaches in a variety of functional settings.


Math1070 or the equivalent; Algebra and Excel competency

Required Text:

Selected Chapters on Business Analysis, 2nd Ed., Pearson Publishing, 2004 (ISBN: 0-536-83481-4)

Attendance/Class Participation/Homework:

Your class participation grade will be based on attendance only. All homework assignments will be reviewed in class, but homework will not be collected. You are expected to attend classes. Class attendance will be taken in the beginning of class. If you do miss a class, you are responsible for obtaining notes and remaining current. It is not possible to repeat lectures for students missing class. One “free” absence is allowed. There are no “excused absences.”

Late students are responsible for signing the class roll before leaving. Otherwise, you will be considered absent. Excessively late students will be penalized. If you arrive late, it is your responsibility to remember to sign the roll before you leave.

All pagers and cell phones should be turned off or muted during class.

General Course Objectives:

To demonstrate the application of models in support of decision making in an enterprise, using some of the most commonly used modeling approaches and principles. Upon completion of the course, the student should:

  • Demonstrate competence in analysis/development of some common models mathematically
  • Demonstrate competence in analysis/development of some common models graphically
  • Demonstrate competence in using a spreadsheet for analysis
  • Interpret model results in the context of the business situation and explain in plain language

Specific Course Objectives:

In order to earn a grade of ‘A’ in the course, the student should, upon completion, be able to:

General Modeling:

  • Define basic modeling terms, including (but not limited to) Physical model, Analog model, Symbolic model, Deterministic model, Probabilistic model, Decision Variable, Random Variable, Parameter, Performance measure, Objective function, Revenue, Fixed Cost, Variable Cost, Overhead Cost, Sunk Cost, Demand, Price, etc.
  • Explain the modeling process, including model types, data collection, analysis, interpretation
  • Analyze a business situation to identify revenues, costs, and other relevant parameters
  • Draw an influence diagram to map the relationships between different variables of interest
  • Build a basic profit model both with a spreadsheet and without
  • Perform Breakeven and Crossover analysis algebraically and graphically, both with a spreadsheet and without, and interpret the results of each


  • Compare and contrast simulation with other types of modeling
  • Determine when simulation is an appropriate technique to use
  • Use random numbers from a random number table or a spreadsheet function
  • Apply simulation techniques to machine break-down, queuing, and inventory problems
  • Graph and interpret the results of the simulations


  • Define the types of forecasting - Quantitative (causal and time series) and Qualitative.
  • Forecast using the following methods for time-series data (on a spreadsheet):
  • Naïve
  • Moving Averages
  • Simple Exponential Smoothing
  • Trend (linear only)
  • Seasonal Analysis (simplified approach)
  • Regression
  • Compute Bias, MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), Standard Error, and R-Squared
  • Compare, contrast, and interpret the different forecasting methods

Quality Management

  • Understand the basic concepts of Quality Management, including Six Sigma
  • Understand the difference between common cause and special cause variation
  • Understand how control charts can be used to help manage by exception
  • Create control charts for attribute and variable measures
  • Calculate process capability; understand how to determine the “sigma” level of a process

Decision Analysis

  • Differentiate between decision making under ignorance, risk, and certainty
  • Define the terms Decision Alternative, States of Nature, Payoff
  • Compute payoff matrix for a given business scenario
  • Define the criteria for choosing the best decision
  • Determine the best decision using the MAXIMAX, MAXIMIN
  • Compute Expected Value (EV or ER), EV under/with Perfect Information (EVUPI or EVwPI), and EV of Perfect Information (EVPI)
  • Construct and solve a decision tree by assigning payoffs to branches, pruning of branches at decision nodes, and assigning probabilities and calculating expected values at chance nodes
  • Combine sample data with prior probabilities using Bayes’ Theorem, and incorporate these “posterior” probabilities into a decision tree analysis

Grading: Points

Competency Exercise (Excel Spreadsheet Analysis) - Required! 0

Tests 3 Tests(20 pts. each) 60

Projects2 Projects (10 pts. each) 20

Homework Various (not collected) 0

Attendance 10

Final ExamCommon Departmental Exam 10


The grading scale for this class is as follows: A+: 97-100; A: 94-96; A-: 90-93.9; B+: 86-89.9; B: 82-85.9;

B-: 78-81.9; C+: 74-77.9; C: 70-73.9; C-: 66- 69.9; D: 60.0 - 65.9; F:< 60.0

Professional and personal circumstances that preclude you from performing at satisfactory levels will not be considered in the determination of the course grade. The effect of your grade on overall GPA, eligibility for graduation, loss of scholarship, loss of a United States resident card, placement on academic probation, etc., are not considered in the determination of your grade. There are no extra credit assignments. Individual requests for alternative ways to improve your course grade will not be considered.

Honor Code:

Plagiarism in any form is not acceptable. While discussion with classmates regarding homework and projects is encouraged, all work submitted must be your own. Evidence of plagiarism on an assignment/exam will result in a failing grade for that assignment/exam.


Tests will be administered in class according to the attached schedule. Tests may be a mixture of multiple choice and true/false. Class tests and the common final will test both your understanding of concepts and problem solving ability, and will also include questions about the use of Excel to solve problems in this course.

For in-class tests and the common final exam, you will need to bring a calculator (with a square root button!) and one 8.5”x11” page of notes (two-sided). Students are required to provide their own pencils and scratch paper. All material needed for tests and the final exam will be covered in class. A sample final exam and answer key can be found on the departmental web site (see page one of this syllabus). All students are required to take the final exam.

Individual Student Projects:

Individual class projects will be discussed in class. These are not group projects! Projects are to be submitted on paper by each student by the designated date, including data output and formulas. No diskettes will be accepted, as they are easily misplaced and damaged. Late projects will be penalized at a rate of 5% per calendar day. In addition, once the deadline has passed, no further feedback will be given. Use the “fit to one page” option to print your output on 8.5x11” sheets. No report covers, please! You may discuss projects with your classmates, but the work you turn in must be your own!

PowerPoint Slides:

Copies of the PowerPoint slides for this course can be found on my website (see page one of this syllabus). To minimize note taking, you should print the slides for each class in advance and bring them to class. Slides for the first class will be provided.

Schedule: The following is a tentative schedule; deviations may be necessary. Supplementary homework assignments will be added as the course progresses.

Date (M,F): / Topic: / Detailed Outline: / Chapter: / Assignments:
General Modeling Fundamentals/Simulation
1. Jan. 11, 15 / Overview of
Modeling for Decision Making / Course introduction and overview;
The nature of modeling for decision-making; Implementation issues.
Discuss Excel Competency Exercise / Course Syllabus
11 / HW1 (Discussion questions): 1-1, 1-2, 1-4, 1-6, 1-12, 1-18
(not collected)
2. Jan. 25, 22 / Business Modeling
Spreadsheet Techniques / Effective use of spreadsheets for modeling; Sensitivity/what-if analysis;
Review of key Excel functions;
Financial Models; Influence diagrams / 2 (skim only) / HW2: Excel Competency Exercise (on website)
3. Feb. 1, Jan. 29 / Spreadsheet Techniques;
Monte Carlo Simulation / Breakeven and crossover analysis;
Introduction to Simulation; Random numbers; Probability distributions; Machine breakdown problem; Queuing applications / 2 (skim only);
BE/CO lecture;
9 (excl. pp.164-67, 174-82, 192, 210)
4. Feb. 8, 5 / Monte Carlo Simulation / Building a spreadsheet simulation:
Inventory applications;
Discuss Project #1 (Simulation);
Review for Quiz#1 (See Sample Final and Key on departmental website.) / 9 (excl. p.164-67, 174-82, 192-210)
5. Feb. 15, 12 / Quiz #1
Forecasting/Quality Management
5. Feb. 15, 12 / Forecasting / Intro to Forecasting; Qualitative Models / 13 (excluding pp. 280-4, 300-2, 307-9)
6. Feb. 22, 19 / Forecasting / Go over Quiz #1; Time-series forecasting models: Naïve forecasts; Moving averages;
Error measures: Bias, MAD, MAPE. / 13 (excluding pp. 280-4, 300-2, 307-9)
7. March 1, Feb. 26 / Forecasting / Time-series forecasting models: Simple exponential smoothing; Trend analysis. / 13 (excluding pp. 280-4, 300-2, 307-9) / Project#1 due
8. Mar. 15, 5 / Forecasting / Time series decomposition/seasonality;
Causal modeling/Regression;
Discuss Project #2 (Forecasting). / 13 (excluding pp. 280-4, 300-2, 307-9)
9. Mar. 22, 19 / TQM/SPC / TQM Overview; Six Sigma philosophy; Process improvement tools. / 15 (excluding pp.339-41, 357)
10. Mar. 29, 26 / TQM/SPC / SPC Overview: Process measurement; Control charts; Process capability. / 15 (excluding
pp.339-41, 357)
11. Apr. 5, 2 / Quiz #2
Decision Analysis
11. Apr. 5, 2 / Decision Analysis / Basic concepts; Ignorance, risk, and certainty; Payoff tables; Alternatives; States of nature; Payoffs; Decision making under ignorance: Maximax, Maximin / 8 (excluding pp.92, 94-101, 109-111)
12. Apr. 12, 9 / Decision Analysis / Go over Quiz #2;
Decision making under risk; Expected value; Expected value of perfect information; Creating payoff matrices. / 8 (excluding pp.92, 94-101, 109-111) / Project#2 due
13. Apr. 19, 16 / Decision Analysis / Sequential decisions and decision trees; Conditional probability and Bayes ’ Theorem; Expected value of sample information; Efficiency / 8 (excluding pp.92, 94-101, 109-111)
14. Apr. 26, 23 / Decision Analysis / Sequential decisions and decision trees; Conditional probability and Bayes’ Theorem
(continued) / 8 (excluding pp.92, 94-101, 109-111)
15. May 3, Apr. 30 / Decision Analysis / Quiz #3;
Review for Final
16. May 10, 7 / Final Exam / Comprehensive Departmental Final Exam: 5/10:5:00-7:00pm; 5/7: 12:30-2:30pm