Dr. Aaron Ratcliffe
MBA 701 Syllabus
Fall 2014
MBA 701.51
QUANTITATIVE ANALYSIS
FOR DECISION MAKING
FALL 2014:
Bryan 205
Wed. 2:00 – 4:45 PM.
DR. AARON H. RATCLIFFE
Office: Bryan 438
Phone: 336.256.8597
Office hours:
Schedule Appointment
*Email 3 preferred slots
Mon. – Fri.
8/20/14 to 12/5/14
E-mail: aaron.ratcliffe@uncg.edu
Walk-Ins:
Tue. 1:00-2:00 PM;
Wed. 5:00-6:00 PM
Thu. 11:00 AM – 12:00 PM
STUDENT LEARNING OBJECTIVES (SLOS)
This course develops quantitative methods and spreadsheet skills to support management
practice and decision making including: hypothesis testing, confidence intervals, regression
analysis, decision analysis, optimization and simulation modeling. The course goals are: 1)
Demonstrate the wide range of situations in which quantitative analysis improves decision
making and creates competitive advantages; 2) Develop students’ analytical thinking skills. 3)
Develop mastery of analysis using spreadsheet models, and effective communication of results.
Upon completing the course, the student should be able to:
1. Construct effective models of decision making situations using principles of
professional spreadsheet design.
2. Compute optimal solutions to decision making models for the management of a wide
range of situations in which quantitative analysis improves decision making.
3. Analyze spreadsheet simulation models and decisions with uncertain outcomes by using
multiple criteria for optimality and risk.
4. Describe a set of data using histograms, scatter diagrams and summary statistics.
5. Compute statistics from sample data to support confidence interval estimation,
hypothesis testing and regression analysis.
6. Infer the statistical precision of insights derived from confidence interval estimation,
hypothesis testing and regression analysis.
COURSE MATERIALS
REQUIRED
1) Laptop installed with MS
Excel 2010 or later and add-ins:
Solver, Analysis Toolpak, and
MegaStat. Instructions on Canvas.
2) Course Pack. Digital and hard
copies available from Harvard
Business Publishing. See link on
Canvas
RECOMMENDED
OPTION A – 2 TEXTBOOKS
OPTION B – CUSTOM TEXT
3) Essential Statistics in
Quantitative Analysis for
Business and Economics. 2E
Decision Making, V1 & V2.
D. Doane and L. Seward.
McGraw-Hill Create. 2010.
McGraw-Hill/Irwin. 2009.
(ISBN-10: 069779153X
4) Introduction to Management  Select Chapters of (3), (4)
Science. 4E
 No glossaries or indices.
F. Hillier and M. Hillier. Irwin.  V2 includes Ch. 13 and
2010.
appendices from V1.
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COURSE POLICIES
1. Course Format. This course meets for one semester of instruction with time devoted to
lecture, guided computing exercises, in-class lab assignments, and case discussion. Prior to
class, complete the assigned reading and exercises. The first part of the course on
management science covers decision making under constraints and decision making in the
presence of uncertainty (SLOs 1-3). The second part of the course on statistics covers
decision making utilizing information from data (SLOs 4-6).
2. What to bring to class. Bring your laptop to every class for following along with
demonstrations of techniques, and for participating in hands-on exercises. Bring case
materials on days of case discussion. It is preferable to download all relevant files before
class so that you have them available regardless of the UNCG Network status.
3. Course Website. The course website is https://uncg.instructure.com/ (Canvas). Canvas has
similar interface to Blackboard and is being implemented in phases across UNCG.
Announcements, slides, spreadsheets, supplementary notes, assignments and grades will be
posted to Canvas. Please confirm that you are receiving Canvas notifications in your inbox.
4. Electronics Policy. Non-class use of laptops, phones and tablets is prohibited. These
activities are distracting for those around you. Please step out of the classroom to take care of
any urgent emails or phone calls.
5. Schedule of Graded Assignments. Your grade for the course is comprised of two exams, two
problem sets, one analysis project, one online tutorial, four case reports and class
participation according to the following weights. Assignments are due on the dates listed on
Canvas, which should be consistent with the syllabus. You will be notified of any changes.
Assessment
Weight
Due date
PS 1 due 9/10/2014 at 9 AM.
Mmgt. Sci. Problem Sets 15.00% PS 2 due 9/19/2014 at 9 AM
Mgmt Sci. Exam
25.00% 10/1/2014. 2-5 PM. Bryan 205
HBS Post-Test
10.00%
11/7/2014 at 5 PM
Stock Project
15.00%
11/21/2014 at 9 AM
Final Exam
20.00%
12/5/2014 at 6:30 PM
Case Reports
10.00%
-
Case Discussion
5.00%
-
Total 100.00%
6. Grading Criteria.
Your final course average will determine your minimum course grade according to the
following table. You may increase your course grade through good class participation.
Letter Grade
F
C C+ B- B B+ A- A
Minimum Numerical Score <66 66 78 80 82 88 90 94
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Syllabus
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The following criteria will apply to the grading of assignments.
A: Work demonstrates clear understanding of the material under study, but also a
superior ability to utilize that material in the assignment. All criteria are met. Work goes
beyond the task and contains additional or outstanding features.
B: Work demonstrates a good understanding of the material under study, and utilizes the
material well in the assignment. The student meets the assignment criteria, with few
errors or omissions.
C: Work minimally demonstrates a basic or technical understanding of the material
under study, and uses some relevant material in the assignment. Work may not address
one or more criteria or may not accomplish what was asked.
F: Work that is incomplete, inappropriate and/or shows little or no comprehension of the
material under study.
7. Guidelines for Submitting Assignments. Completed assignments and exam files will be
uploaded to Canvas. Your grade will be based upon (1) how well you conduct your analysis
and (2) how professionally you communicate your results and ideas.
 Submit your assignments as one MS Word or PDF document and one Excel
workbook showing the steps of your analysis.
 The report should summarize the analysis of each problem or project part including a
brief overview of the task at hand.
 Reference in the body of your report the attached Excel workbook.
 Worksheets should be organized and annotated to readily communicate your results.
 Demonstrate clearly that you understand the principles and techniques being studied.
 Conclusions of the analysis should be explicitly stated. Briefly state the implications of
your analysis and answer any questions asked in the statement of the problem.
8. Exams. The Management Science exam will be conducted during a 3-hour class session on
the date listed in the syllabus. The final exam will be due on the date listed in the syllabus.
 Exams are open note, open book, open laptop, but no communication of any kind is
allowed, either verbal, written, or electronic except with the instructor. Cell phones must
be turned off and stowed away.
 If you have a conflict for the midterm or the final exam, you must notify the instructor at
least 1 week prior. Documented proof may be requested. For legitimate conflicts, a makeup exam may be scheduled.
 Requests to correct grading errors must be made one within one week of the grade being
returned to you.
9. Optimization & Simulation Problem Sets. Details of the optimization and simulation problem
sets are provided in Canvas. You may work in 2-member teams for the problem sets, but
all students should individually attempt and comprehend all of the problems. Each team will
submit one report (MS Word or PDF) and one Excel workbook by the due date. Problem set
reports should include clear answers to each of the questions listed. Reference in the report
any work completed in an attached Excel file.
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10. Stock Project details are provided on Canvas. You will select a company and analyze the
performance of its common stock during the recent past. You may consult with others, but
each student must analyze a different stock and submit an individual assignment. Each
student will submit one report (MS Word or PDF) and one Excel workbook by the due date.
Project reports should begin with an executive summary of the key issues of the project, your
methodology, and your recommendations. Describe your assumptions, methods, and answers
to any project questions in the body of the report. Reference in the report any work
completed in an attached Excel file.
11. Online Tutorial Post-Test instructions will be available via Canvas. You may complete the
tutorial at your own pace but completed exam results must be submitted by the due date. You
may work through the tutorial with others, but you are not to consult with others while
completing the assessment at the end of the tutorial.
12. Case Reports details will be posted on Canvas and must be submitted by the assigned due
date. You may work in 3-member teams for the case project reports, but all students
should be individually prepared to discuss the case in class. Each team will submit one
report (MS Word or PDF) and one Excel workbook by the due date. Case reports should
begin with an executive summary of the key issues of the case, your methodology, and your
recommendations. Describe your assumptions, methods, and answers to any case questions in
the body of the report. Reference in the report any work completed in an attached Excel file.
13. Class Participation includes being prepared for class, being involved in class discussion, and
being engaged with the material outside of class. You are expected to be thoroughly prepared
to discuss assigned readings and cases. You may be called upon at any time to share your
perspective, work with other students, or respond to a question. You are encouraged to attend
office hours and email the instructor with questions and insights. Participation is essential
because: 1) discussion about a business situation is most fruitful with multiple perspectives;
2) articulating your thoughts and questions demands that you be clear and precise; 3) it
promotes critical thinking and maximizes your learning efficiency. Constructive participation
and effective communication are vital business skills in any organization.
The instructor will gage your participation in case discussions and other team work as well as
offer you opportunities for self-evaluation.
14. ALEKS is optional web-based assessment and tutorial system that uses artificial intelligence
to support individualized learning. The system provides additional support beyond the online
tutorial to help you master the statistics topics throughout the semester. To gain access to
ALEKS you need (1) a personal pass code which must be purchased, and (2) a course code
which is displayed in the Canvas. The system is designed to be used without an instruction
manual, but it is advised you read the instructions on pp. 472-494 of the custom text.
15. UNCG Academic Integrity Policy. You are expected to be familiar with and abide by the
UNCG Academic Integrity Policy. The Policy may be found at:
http://sa.uncg.edu/handbook/academic-integrity-policy/
Although you are encouraged to discuss assignments with classmates, you are not to
share details of your work. Specifically, you are not to share computer files or printed output
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from your computer analysis. Prohibited actions also include working together side-by-side
on separate computers. Violations of the Code will result in penalties ranging from an F on
the assignment to an F in the course.
16. Bryan School Faculty Student Guidelines. The Bryan School faculty has approved a set of
guidelines for the conduct of classes. They can be found at the following link
http://bae.uncg.edu/wp-content/uploads/2012/08/faculty_student_guidelines.pdf
Be professional.
Have integrity.
Treat the classroom like a business meeting.
MBA701, Fall 2014
Syllabus
Class Schedule Detailed.
8/20
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***Tentative. Official schedule updated on Canvas.
Introduction to Optimization & Linear Programming: (SLOs 1 & 2)
1) What are optimization and linear programming? Why are they important?
2) Three components of every optimization model
3) Process & Guidelines for Professional Spreadsheet Design
4) Solve Linear Programs using Spreadsheets: the Solver Tool
5) Sensitivity analysis
6) Managerial problems where linear programming can be applied:
a. Resource Allocation / Cost-Benefit Problems
b. Product-Mix Problems
In Class Examples
A2 Liquor
SoyBoy Farms
Pfizer Drug Company
Lab/Practice Exercises
ConZorro
Picasso Frames
National Credit Union
Prior to first class: 1) Update Canvas profile with picture and bio, 2) Install Add-Ins:
Solver, Data Analysis Tool-Pak & MegaStat, 3) Complete Excel refresher worksheet
Reference: Hiller & Hiller. Chapters 2-4.
8/27
Spreadsheet Modeling & Applications: (SLOs 1 & 2)
1) Assignment Problems
2) Scheduling Problems
3) Transportation Problems
4) Debugging Spreadsheet Models
In Class Examples
Midwest Financial
Keystone Police
Curly Crustaceans
Lab/Practice Exercises
Cargo Ships (Problem 3.33)
Staffing A Call Center (Case 2-3)
Medequip Company (Problem 3.16)
Reference: Hiller & Hiller. Chapters 2-4.
9/3
Linear Programming Case Discussion (SLOs 1 & 2)
1) Prudent Pensions (Case 4-1)
2) MacPherson Refrigeration Case (course pack)
In Class Examples
Prudent Pensions
MacPherson/Aggregate Plan
Lab/Practice Exercises
Investments Problem
Aggregate Planning Problem
Linear Programming Case Report due 9/3 at 9 AM.
Reference: Hiller & Hiller. Chapters 2-4.
MBA701, Fall 2014
9/10
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Decision Analysis: (SLOs 2 & 3)
1) Introduction to Probability & Random Variables
2) Three elements of a decision analysis
a. What are payoff tables?
3) Using decision trees to optimize decisions with uncertainty
4) Sensitivity Analysis: quantifying the value of information
5) How attitudes towards risk can affect the analysis.
a. What is risk? Why is it important?
In Class Examples
Techware
Lab/Practice Exercises
SciTools
Better Banking or Bust
Optimization Problem Set due 9/10 at 9 AM.
Reference: Online Tutorial. Class slides. Supplementary reading.
9/17
Introduction to Simulation (SLOs 1 & 3)
1) What is Monte Carlo simulation? Why is it important?
2) What are pseudorandom numbers? How do we generate and use them?
3) Statistical analysis of output
4) Optimization using the Data Table tool
5) How to measure risk in spreadsheet simulation models
6) How to balance multiple objectives under uncertainty
7) Managerial problems where linear programming can be applied:
a. Overage-Underage / Single-Period Inventory Models (Newsvendor)
b. Revenue Management
In Class Examples
Walton Bookstore
Bottle Company
Albatross Air
Lab/Practice Exercises
American Medical Association
Textbook
Harriet Hotel
Reference: Hillier & Hillier. Chapter 12, pp. 686-718.
9/24
Spreadsheet Simulation Models & Applications (SLOs 1 & 3)
1) Using Data Table to analyze multi-period models
2) Using spreadsheet templates to analyze simple queueing systems
In Class Examples
GF Auto
Rustbelt Maintenance
Lab/Practice Exercises
New Store
Hugh’s Repair Shop
Simulation Problem Set due 9/26 at 5 PM.
Reference: Hillier & Hillier. Chapter 12, pp. 686-718.
MBA701, Fall 2014
10/1
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Simulation Case Discussion & Review (SLOs 1 & 3)
1) Merck & Company: Product KL-798
2) Risk Analysis for Merck and Company: Product KL-798
In Class Examples
Merck & Company
Lab/Practice Exercises
TRC Technologies
Simulation Case Report due 10/1 at 9 AM.
Reference: Hillier & Hillier. Chapter 12, pp. 686-718.
10/8
Management Science Exam
2:00 – 4:45 PM in Bryan 205
10/15
Introduction to Statistics (SLOs 4 & 5)
1) What is statistics?
2) Collecting data: sampling concepts and methods
3) Describing data visually using Excel
4) Descriptive statistics: central tendency and dispersion of random quantities
In Class Examples
Stock Price
Surgical Recovery
Expense Ratio
Lab/Practice Exercises
Rice Krispies (4.39)
Ads as % of Sales (4.59 – Data Set A)
Airline Days (4.27)
Work corresponding sections of Online Tutorial
Reference: Doane & Seward. Chapters 1-4.
10/22
Sampling & Estimation: Confidence Intervals (SLOs 5 & 6)
1) The Normal probability distribution
2) The Central Limit Theorem
3) Properties of the sample mean
4) Estimating a confidence interval for the population mean or proportion
5) Selecting sample size to control confidence interval width
In Class Examples
Subway (7.57 and 7.74)
Chrysler MPG (8.39)
Ameritech Pages (8.38)
Lab/Practice Exercises
UVA Children’s & Procyon (7.65, 7.63)
Bushing Diameter (8.03)
SOX Requirements (8.48)
Work corresponding sections of Online Tutorial
Reference: Doane & Seward. Chapters 7-8.
MBA701, Fall 2014
10/29
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Hypothesis Testing (SLOs 5 & 6)
1) What are the steps in testing hypotheses?
I. State the null and alternative hypotheses
II. Specify the decision rule
III. Collect data
IV. Calculate the test statistic or p-value
V. Make decision
VI. Take action based on the decision
2) One-sample tests for the mean & proportion
3) Tests to compare two means or two proportions
4) Selecting sample size to control confidence interval width
In Class Examples
Noodles Service Times (9.61)
Single-Earner Mortages (9.54)
Superintendent Salary (10.53)
Vale Ski Hats (10.57)
Lovastatin (10.44)
Lab/Practice Exercises
Beer Taste (9.64)
Bushing Diameter (8.03)
Shoe Size (10.6)
Home Value (10.64)
Debit Card Purchases (10.17)
Work corresponding sections of Online Tutorial
Reference: Doane & Seward. Chapters 9-10.
11/5
Regression: Estimating Relationships (SLOs 4, 5, & 6)
1) Least squares method: finding an equation to describe a linear relationship
2) Steps in Regression Analysis
I.
Prior to performing the regression
i. Is data cross-sectional or time series?
ii. Assess linear relationship. Scatterplots. Correlation matrix
iii. Model selection. Which explanatory variables to include?
iv. State a priori your hypotheses about signs of predictors
II.
Perform the regression.
i. Assess the goodness of fit of the model
ii. Assess overall model significance: F-Test. 𝑅 2 .
iii. How accurate is the model? Standard error and CoV
III.
Assess the significance of the drivers
i. Interpret p-values and confidence intervals for coefficients
IV.
Check the assumptions of your model
i. Multicollinearity: Correlation matrix and VIFs.
ii. Normality of residuals: outliers, skewness, Normal prob. plot.
iii. Heteroscedasticity:
iv. Autocorrelation for time series: Durbin Watson test.
V. Using the model for prediction
i. Write the estimated regression equation in standard form
ii. 95% prediction intervals for the model
iii. Validate the model using additional data
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3) Binary predictors: dummy variables and categorical variables
4) Regression modeling techniques: using regression creatively
In Class Examples
Hybrid Sales
Salary Discrimination
Lab/Practice Exercises
Sporting Woman
Noodles & Company Sales
ONLINE TUTORIAL POST-TEST due 11/7 at 5 PM!!!
Reference: Doane & Seward. Chapters 9-10.
11/12
Statistics Case Discussion 1: (SLOs 4, 5, & 6)
1) Pedigree vs. Grit (Case) (course pack)
2) MBA Starting Salaries Case
In Class Examples
Pedigree vs. Grit
MBA Starting Salaries Case
Lab/Practice Exercises
Auto MPG
Case Report due 11/12 at 9 AM.
Reference: Doane & Seward. Chapters 1-4, 7-10, 12-13.
11/19
Statistics Case Discussion 2 & Review: (SLOs 4, 5, & 6)
1) Deconstructing the Price of Diamonds
2) Pilgrim Bank (A) Customer Profitability
In Class Examples
Price of Diamonds
Pilgrim Bank
Lab/Practice Exercises
Medical Office Value
Case Report due 11/19 at 9 AM.
Stock Project due 11/21 at 9 AM
Reference: Doane & Seward. Chapters 1-4, 7-10, 12-13.
12/5
FINAL EXAM
Due at 6:30 PM