PMBA 8040: Data Driven Decision Making

Syllabus for Fall 2018

PruittHealth

Instructor: Dr. Satish Nargundkar
Office: 827 Robinson College of Business

Office Hours: By appointment
Phone: (678) 644 6838

E-Mail : snargundkar@gmail.com
Website: www.nargund.com/gsu

Tuesdays 4:30 – 8:45 PM at PruittHealth

Textbook (optional):

Cliff T. Ragsdale, “Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics”. 7^th Edition, Cengage Learning.

ISBN 13: 978-1-285-41868-1

ISBN 10: 1-285-41868-9

Course Catalog Description

Evidence based decision making is critical to an organization’s ability to compete in the global economy. This course explores the role of data in driving decisions made by managers across and within functional boundaries. Specifically, students learn to understand, visualize and present data that supports organizational decision making process. They also learn how to create data driven models such as regression and decision trees to make decisions to address critical challenges faced by organizations and society. The course features hands-on exercises with appropriate software.

Learning Objectives:

Upon successful completion of the course, students will be able to apply analytics to help improve business decision making. The broad goal is to learn to translate business issues into mathematical models, analyze the models algebraically or with the aid of a spreadsheet, interpret the results in the business context, and recommend actions as appropriate. Specifically, the student will learn to do the following:

Profit Models and Simulation

1. Analyze a business situation to identify revenues, costs, and other parameters relevant to the modeling process.

2. Build a basic profit model both with a spreadsheet and without.

3. Perform breakeven analysis algebraically and graphically, and with a spreadsheet.

4. Perform Crossover analysis algebraically and graphically, both with a spreadsheet and without.

5. Interpret the results of Breakeven and Crossover analyses.

Time Series Forecasting

6. Define the types of forecasting – Quantitative (causal and time series) and Qualitative.

7. Forecast using the following methods for time-series data (on a spreadsheet):

8. Naïve

9. Moving Averages

10. Simple Exponential Smoothing

11. Trendline (Regression)

12. Classical Decomposition (Trend and Seasonality)

13. 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.

14. Compare and contrast the different time-series forecasting methods.

15. Interpret the results of the different forecasting methods.

Regression Analysis

16. Build Simple and Multiple Regression models to understand relationships and predict the values of the dependent.

17. Use both continuous and categorical independent variables in models.

18. Interpret results in the context of the issue at hand.

19. Apply to model make predictions.

20. Evaluate regression models using Standard Error, R-squared.

21. Explain the assumptions and limitations of Regression.

Decision Analysis

22. Differentiate between Decision making under ignorance, risk, and certainty.

23. Define the terms Decision Alternative, States of Nature, Payoff.

24. Compute payoff matrix for a given business scenario.

25. Define the criteria for choosing the best decision.

26. Determine the best decision using the MAXIMAX, MAXIMIN, Laplace-Bayes, MINIMAX-Regret criteria.

27. Compute Expected Value (EV), EV under Perfect Information (EVUPI), EV of Perfect Information (EVPI), Expected Opportunity Loss(EOL).

28. Explain why the minimum EOL is the same as EVPI.

29. Construct a decision tree.

30. Define decision nodes, chance nodes, branches, payoffs, probabilities, pruning of branches.

31. Compute posterior probabilities using Bayes’ Theorem, and incorporate them into analysis.

Optimization

32. Formulate product-mix, transportation, and assignment problems as Linear Programming models.

33. Solve the LP problems using Excel’s solver.

34. Interpret the results.

Grading:

		Course Average	Grade	Course Average	Grade
Assignments	20%	94-96, 97+	A, A+	77-79	C+
Quizzes	50%	90-93	A-	73-76	C
Team Project	30%	87-89	B+	70-72	C-
		83-86	B	60-69	D
		80-82	B-	Less than 60	F

MBA Grading Statement:

In general, in MBA and PMBA prefix courses about 35% of the students will receive a grade of A+, A, or A-. Generally, the majority of the remaining students will receive grades of B+, B, or B-. Students who significantly lag in performance shall earn grades at the C level or lower as appropriate.

Project Teams:

Teams of 4 students each will be formed before midterm for project work.

Attendance: Students are expected to attend all classes for the entire duration. Given the compressed nature of the semester and the fact that there will be hands-on quantitative work as well as discussion, it makes sense for you to be in class. Please notify me if you must miss a class due to work related travel or other reasons. In any case, you are responsible for keeping up with anything you might have missed due to an absence.

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.

Ethics and Academic Honesty:

I encourage you to share your work and knowledge, but draw the line at plagiarism and copying the work of others. Work with other students or seek assistance from another person only is specifically allowed. If you are allowed to work with another student (or anyone) on an assignment, acknowledge the collaboration. Never copy another student’s work or allow another student to copy your work. Do not use any prohibited materials.

We take issues of academic honesty very seriously. Students are expected to recognize and uphold standards of intellectual and academic integrity in all work. The university assumes as a basic and minimum standard of conduct in academic matters that students be honest and that they submit for credit only the products of their own efforts. The University policy on academic dishonesty is spelled out in Section 1350 of the Graduate Catalog.

The following are instances of academic dishonesty:

Ø plagiarism (see course and GSU website for specific examples of what constitutes plagiarism),

Ø cheating on examinations,

Ø unauthorized collaboration with others

falsification of materials,

Ø multiple submissions (i.e., submitting the same work for credit in more than one class).

Lack of knowledge is not an acceptable defense to any charge of academic dishonesty. Infractions will result, at a minimum, in a zero for the assignment and can result in expulsion from the university.