Syllabus Schedule
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MGS
3100 – Business Analysis
Summer
2006 Syllabus
|
Instructor: Dr. Office Hours: By
appointment |
E-Mail: snargundkar@gmail.com
Computer# 52132, ALC 324, 4:45 – 7:30 PM, MW 52134, ALC 324, 7:40 – 10:25 PM, MW |
Prerequisites:
· Math 1070 or equivalent (If you do not have the stated prerequisite, you should drop now and attempt this course only after you have satisfied it.)
Required Text:
Selected Chapters in Business Analysis, Second Edition.
Available only through GSU bookstores
Honors Students: In addition to the detailed objectives stated in this syllabus, you will be responsible for one additional topic: Causal Forecasting. Also, every student will be required to present their project work in class.
Attendance/Class Participation:
You are expected to attend all classes (who knows, you may actually enjoy the class!) Assignments and projects remain due on the designated date regardless of class attendance. If you do miss a class, you are responsible for remaining current.
Projects:
The projects and the instructions for completing them will be discussed in class. Each project is a group assignment. Groups of 3 students (2 students for honors section) each will be organized soon after the beginning of the semester.
Grading:
|
Group Project 1 |
Profitability Analysis |
15% |
|
Group Project 2 |
Forecasting |
15% |
|
Quiz 1 |
Profit Models/Simulation |
20% |
|
Quiz 2 |
Forecasting |
20% |
|
Final Exam |
Comprehensive Departmental Exam |
30% |
Late projects will be penalized at a rate of 10% per day.
No make up quizzes will be given. The final exam score will replace your worst quiz score, unless the final exam score is lower.
Honor Code:
It is more honorable to get any grade with your own work than to get a better grade by using someone else’s work as yours. While discussion with classmates is encouraged, any work submitted must be your own (or your group’s, for group projects). Evidence of plagiarism/cheating on an assignment/exam will result in a failing grade for that assignment/exam, or even for the course.
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:
1. Demonstrate competence in analysis/development of some common models mathematically, graphically, and with a spreadsheet.
2. Be able to interpret model results in the context of the business situation and explain them in plain language.
3. Demonstrate the ability to present information on a simple web page.
Specific Course Objectives:
In order to earn a grade of ‘A’ in the course, the student should, upon completion of the course, be able to:
Overview:
1. Define basic modeling terms, including (but not limited to) Physical model, Analog model, Symbolic model, Deterministic model, Probabilistic model, Decision Variable, Random Variable, exogenous and endogenous variable, Parameter, Performance Variable, Revenue, Fixed Cost, Variable Cost, Overhead Cost, Sunk Cost, Demand, Price, inductive and deductive logic,.
2. Explain the overview of the modeling process, including types of models, data collection, analysis, and interpretation.
Profit Models and Simulation
3. Analyze a business situation to identify revenues, costs, and other parameters relevant to the modeling process.
4. Draw an influence diagram to map the relationships between different variables of interest.
5. Build a basic profit model both with a spreadsheet and without.
6. Perform breakeven analysis algebraically and graphically, both with a spreadsheet and without.
7. Perform Crossover analysis algebraically and graphically, both with a spreadsheet and without.
8. Interpret the results of Breakeven and Crossover analyses.
9. Find the price that maximizes profit, given a demand function, algebraically and graphically, both with a spreadsheet and without.
10. Compare and contrast Simulation with other types of modeling.
11. Determine when simulation is an appropriate technique to use.
12. Use random numbers from a random number table or a spreadsheet function.
13. Construct cumulative probability distributions.
14. Simulate discrete (two valued or many valued) random variables.
15. Graph the results of the simulations and interpret.
Time Series Forecasting
16. Define the types of forecasting – Quantitative (causal and time series) and Qualitative.
17. Forecast using the following methods for time-series data (on a spreadsheet):
a. Naïve
b. Moving Averages
c. Simple Exponential Smoothing
d. Regression (Simple, Quadratic, Logarithmic)
e. Classical Decomposition (Trend and Seasonality)
18. Compute Bias, MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), Standard Error, and R-Squared (for regression only) for each of the forecasting methods.
19. Compare and contrast the different time-series forecasting methods.
20. Interpret the results of the different forecasting methods.
Quality Management
21. Explain the basic concepts of Quality Management.
22. Compare and contrast common cause (natural) variation and special cause (assignable) variation
23. Explain how control charts can be used to help manage by exception
24. Create control charts for attribute (p-chart) and variable measures (Xbar and R charts).
25. Compute a process capability index.
Decision Analysis
26. Differentiate between Decision making under ignorance, risk, and certainty.
27. Define the terms Decision Alternative, States of Nature, Payoff.
28. Compute payoff matrix for a given business scenario.
29. Define the criteria for choosing the best decision.
30. Determine the best decision using the MAXIMAX, MAXIMIN, Laplace-Bayes, MINIMAX-Regret criteria.
31. Compute
Expected Value (EV), EV under Perfect Information (EVUPI), EV of Perfect
Information (EVPI), Expected
32. Explain why the minimum EOL is the same as EVPI.
33. Construct a decision tree.
34. Define decision nodes, chance nodes, branches, payoffs, probabilities, pruning of branches.
35. Compute posterior probabilities using Bayes’ Theorem, and incorporate them into analysis.