Syllabus Schedule
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MGS 3100 – Business Analysis
Maymester 2012 Syllabus
Dr. Satish Nargundkar
|
Instructor: Dr. Satish Nargundkar Office
Hours: By appointment Website: www.nargund.com/gsu |
E-Mail: snargundkar@gmail.com
Computer# 51402, 51405 ALC 213, 8:00 AM – 10:20 AM ALC 403, 11:00 AM – 1:20
PM |
Prerequisites:
·
Math 1070 or
equivalent basic statistics (If you do not have the stated prerequisite, you
should drop now and attempt this course only after you have satisfied it.)
·
Proficiency in
Microsoft Excel®
Text:
1.
Required Text : Business Analysis Exercises, by Nargundkar, S. & Samaddar, S., (Required
text) available at Alphagraphics,
34 Peachtree St., North of 5 points intersection.
2. You may buy (optional texts) selected
chapters from the following online books:
http://www.cengagebrain.com/isbn/1111532222
Chapter 1 (Profit Models) and
the table of contents are free! Chapter 12 (Simulation) Chapter 13(Decision
Analysis)
http://www.cengagebrain.com/isbn/0538479752
Chapter 1 (Data &
Statistics) is free! Chapter 17 (Time Series Forecasting), Chapters 14/15
(Regression)
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.
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.
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:
|
Activity |
Points |
|
Course Average |
Grade |
Course Average |
Grade |
|
Projects (2) |
30% |
|
94-96, 97+ |
A, A+ |
77-79 |
C+ |
|
Tests |
45% |
|
90-93 |
A- |
73-76 |
C |
|
Final Exam |
25% |
|
87-89 |
B+ |
70-72 |
C- |
|
|
|
83-86 |
B |
60-69 |
D |
|
|
Total |
100% |
|
80-82 |
B- |
Less than 60 |
F |
Late projects will be
penalized at a rate of 5% per day. No
make up tests will be given. The final exam score
will replace your worst quiz score, unless the final exam score is lower. While
effort is needed to perform well, grades are based on performance, not effort. Grades
must be earned with performance, not negotiated for. Asking
me to give you a higher grade than you earned, because it affects a
scholarship, or has some other consequence, is essentially asking me to cheat
on your behalf. My job, to be fair to all students, is to assign each the grade
they earn.
Course
Assessment:
Your constructive assessment of this course plays an
indispensable role in shaping education at Georgia State. Upon completing the
course, please take the time to fill out the online course evaluation.
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, Parameter, Performance
Variable, Revenue, Fixed Cost, Variable Cost, Overhead Cost, Sunk Cost, Demand,
Price.
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, and with a spreadsheet.
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.
Decision Analysis
21. Differentiate between Decision making under ignorance,
risk, and certainty.
22. Define the terms Decision Alternative, States of
Nature, Payoff.
23. Compute payoff matrix for a given business scenario.
24. Define the criteria for choosing the best decision.
25. Determine the best decision using the MAXIMAX,
MAXIMIN, Laplace-Bayes, MINIMAX-Regret criteria.
26. Compute Expected Value (EV), EV under Perfect
Information (EVUPI), EV of Perfect Information (EVPI), Expected Opportunity Loss(EOL).
27. Explain why the minimum EOL is the same as EVPI.
28. Construct a decision tree.
29. Define decision nodes, chance nodes, branches,
payoffs, probabilities, pruning of branches.
30. Compute posterior probabilities using Bayes’ Theorem, and incorporate them into analysis.
Accommodations for students with
disabilities
Georgia State University complies
with Section 504 of the Rehabilitation Act and the Americans with Disabilities
Act. Students with disabilities who seek academic accommodations must first
take appropriate documentation to the Office of Disability Services locate in
Suite 230 of the New Student Center.