MBA 7025 - Statistical Business Analysis

Spring Semester 2015


Course:    MBA 7025 - Statistical Business Analysis
Classroom:  Aderhold Learning Center 306
Instructor:  Steve S. Wong

(678) 467-8199

Office Hours: By appointment

Please add MBA 7025 to the beginning of the subject line.





Course Overview

This course deals with the basics of converting corporate data into actionable information for managerial decision making. Statistical data analysis techniques in the context of Business Intelligence are covered with applications in various functional areas of business. Specific techniques include data visualization, descriptive statistics, estimation, hypothesis testing modeling relationships, basic forecasting techniques, and optimization techniques for decision supporting the context of corporate performance management.



Algebra and Excel competency



Selected Chapters from the online book: Data Analysis & Decision Making, 4th Edition, Albright, Winston, and Zappe. Duxbury Press, Second Edition, 2011, ISBN-13: 978-0-538-47612-5 (Optional)

Chapter 1 and the table of contents are free



Assignment 1  Descriptive Statistics


Assignment 2 Regression Analysis


Assignment 3 Business Intelligence Solutions


Test 1  Exploratory Data Analysis / Descriptive Statistics 15%
Test 2  Regression Analysis / Time Series Forecasting  15%
Test 3 Business Intelligence / Decision Support Systems 15%
Final Exam  Common Departmental Exam  25%
Total 100%


Plus/Minus Grading Policy



Attendance/Class Participation:

Class attendance is expected and necessary component of class participation. In the event that you must miss class, you are still responsible for material covered, and should make arrangements with fellow classmates to remain current with the class. Assignments remain due on the designated date regardless of class attendance. This is a project-oriented class using various computer application programs which require some time commitment. More than 3 missed class periods will result in your final grade marked down one grade. Additional absences may result in a WF.


General Course Objectives:

Upon completion of the course, the student will be able to build Decision Support Systems (DSS) – apply mathematical, graphical and spreadsheet modeling techniques to business situations to aid decision-making.  Students will go through the process of describing data, building prediction models, using optimization techniques, and simulating key variables.  Overall, the course will provide the student with an analytical foundation for dealing with business situations.


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 be able to:


Exploratory Data Analysis:

·        Distinguish between cross sectional and time ordered data.

·        Construct and interpret histograms, bar charts, and pie charts.

·        Explain the role of histograms in univariate data analysis.

·        Construct and interpret a line graph.

·        Explain the role of line graphs in univariate data analysis

·        Assess if time ordered data are stationary.

·        Determine if a data set is reasonably normally distributed

·        Compute the sample mean and sample standard deviation to summarize a data set.

·        Determine when there are outliers for symmetric data.

·        Explain the role of scatter diagrams in bivariate data analysis

·        Construct and interpret scatter diagrams.

·        Interpret scatter diagrams that contain linear or nonlinear relationships or clusters


Multiple Regression Analysis and Time-Series Modeling

·         Compare and contrast simulation with other types of modeling

·         Explain how a regression model, or equa­tion, helps managers predict, explain, and control.

·         Interpret the sample regression coefficients

·         Explain the decomposition of sum of squares, mean squares, F-statistic, p-value and the R-square statistic.

·         Use p-values to test for significance of independent variables.

·         Explain the role of the standard error of the estimate in prediction.

·         Explain the nature of time-series data. Describe ways to evaluate trend in time-series data.

·         What are some advantages of time series modeling?

·         Explain the use (and possibly misuse) of the R2 statistic.


Model Building and Decision Support Systems

·         Explain the need for decision support models.

·         Draw influence diagrams. Distinguish between outcome, external and decision variables.

·         Design, Develop and Implement decision support models in a spreadsheet (Excel).

·         Use sensitivity analysis to evaluate outcomes.

·         Implement various design considerations into the development of DSS.

·         Describe a framework for enterprise-wide decision support for a business organization.


Risk Analysis (Monte-Carlo Simulation)

·         Explain when Monte Carlo simulation methodology should be used in conjunction with decision support models.

·         Distinguish among the common theoretical distributions used in risk analysis and determine when each distribution is appropriate.

·         Determine the input variables that should be modeled as uncertain variables.

·         Perform Monte-Carlo Simulation and interpret its output.

·         Evaluate the pros and cons of using risk analysis.


Business Intelligence

·         Describe the eBusiness framework for managing organizations.  Use an appropriate framework to integrate various areas.

·         Define/describe each area (Enterprise Resource Planning, Supply Chain Management, CRM and Business Intelligence)

·         Describe the area of Business intelligence and its role in all business applications of IT.

·         What is Data Mining? What are some applications of Data Mining?


Brightspace Skills

You are expected to be proficient in the use of Brightspace and all the assignments need to be submitted via the DropBox in Brightspace.  Specifically, you should be able to read, upload, and download files; read and send e-mail messages, read and post messages on discussion boards.  You are also expected to check the section site daily for any changes, updates, and announcements. A knowledge of these applications is a prerequisite for any course offered by RCB.  The University offers remedial courses in any of these applications.


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.



Exams will be administered in class according to the attached schedule. Exams may be a mixture of short questions, multiple choice and true/false.  Class exams 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 exams 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. 


PowerPoint Slides:

Copies of the PowerPoint slides for this course can be found on this website (see the "Schedule of Classes" Wiki Page or the Quick Launch menu "Class PPT Slides").  To minimize note taking, you should print the slides for each class in advance and bring them to class.