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Office: Bryan 391
Tel: 336-334-5666
Email: Joy_Bhadury@uncg.edu
Office hours: Mondays 4:30 PM to 6 PM. Additionally, you can meet with me in person or over Skype by scheduling an appointment separately at a mutually convenient time.
Bulletin description: Quantitative methods and spreadsheet skills to support management practice and decision making. Topics include statistical hypothesis testing, confidence intervals, regression analysis, optimization modeling, decision analysis and risk analysis.
Therefore, 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 risk analysis. 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.
Describe a set of data using histograms, scatter diagrams and summary statistics.
2.
Compute statistics from sample data to support confidence interval estimation, hypothesis testing and regression analysis.
3.
Infer the statistical precision of insights derived from confidence interval estimation, hypothesis testing and regression analysis.
4.
Construct effective models of decision making situations using principles of professional spreadsheet design.
5.
Compute optimal solutions to decision making models for the management of a wide range of situations in which quantitative analysis improves decision making.
6.
Analyze spreadsheet simulation models and decisions with uncertain outcomes by using multiple criteria for optimality and risk.
Throughout the course, Microsoft Excel will be used extensively as it is one of the most powerful and widely available tools for quantitative analysis across all functions and industries of business. The course
1 P LEASE READ THIS COURSE SYLLABUS THOROUGHLY BY JANUARY 18, 2016.
I F YOU HAVE ANY QUESTIONS , PLEASE
CONTACT THE INSTRUCTOR BY THAT DATE .
R EMEMBER THAT THIS SYLLABUS WILL SERVE AS A
“
CONTRACT
”
BETWEEN
INSTRUCTOR AND STUDENTS FOR THE DURATION OF THIS COURSE .
MBA 701 (Bhadury) - Spring 2016 – Course Syllabus…………1

itself, however, is not a course in how to use Microsoft Excel, but rather a course in how to analyze and interpret important business problems using quantitative analysis methods. You are expected to begin the class with a foundation of core Excel skills already established and it is your responsibility to improve your
Excel skills on your own outside of class if necessary. Thus, the important prerequisite for the course is a pre-MBA training in spreadsheet skills or a spreadsheet-based undergraduate course in probability and statistics.
This course will be taught primarily from the following textbook:
“Quantitative Analysis for Management (12th Edition)”, by B. Render, R,M. Stair, M.E. Hanna and T.S. Hale. Pearson.
Also, students will need access to a laptop installed with MS Excel 2010 or later and the following add-ins:
Solver, Analysis Toolpak . Please note that all the reading materials for this course are being made available to you solely as a reference for this course and should not be copied or distributed for any purpose other than studying for this course.
This course will make extensive use of the Canvas Learning Management System used by UNCG – see canvas.uncg.edu. Students should check into the course website at least once a day for updates, announcements etc.
The following topics will be covered in this course in the order below.
Topic
Module 1: Introduction to Quantitative
Modeling & Descriptive Statistics
Corresponding Reading Material
Chapter 1 (Introduction to Quantitative Analysis) and
Files on this topic available in Canvas
Module 2: Probability Concepts
Module 3: Estimation & Confidence Intervals Files on this topic available in Canvas
Module 4: Hypotheses Testing Files on this topic available in Canvas
Module 5: Regression Models
Chapter 2 (Probability Concepts and Applications) and
Files on this topic available in Canvas
Module 6: Decision Analysis
Chapter 4 (Regression Models ) and
Files on this topic available in Canvas
Chapter 3 (Decision Analysis) and
Files on this topic available in Canvas
Module 7: Linear Programming and
Applications
Chapter 7 (Linear Programming Models: Graphical
& Computer Methods) and Chapter 8 (Linear
Programming Applications) and
Files on this topic available in Canvas
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Topic
Module 8: Risk Analysis using Simulation
Corresponding Reading Material
Chapter 13 (Simulation Modeling) and
Files on this topic available in Canvas
Note that level and extent of coverage of these topics above will depend on the time available.
Depending on this, some topics may not be covered at all.
This will be taught through face-to-face lectures in the classroom as well as supporting material in the Canvas learning management system. The methods employed to achieve these objectives will vary, but include:
1. Lectures in the classroom.
2. Readings from textbook and other assigned materials.
3. Course Projects.
4. Quizzes/exams.
It is imperative that every student log into Canvas and check on the course at least once every day. Periodic announcements and reminders will be placed there.
Students should bring their laptops to every class to follow along with demonstrations and participate in lab exercises. Students should have access to the slides for every topic either on their laptop or in hard copy for every lecture and also have the capability to take additional notes. Tip: download all relevant files from
Canvas before class so that you have them available regardless of the UNCG Network status.
There will be two kinds of assessments in this course: 8 Examinations (each worth 8% of the final grade) and
4 Course Projects (each worth 9% of the final grade).
Guidance for Examinations
Each examination will cover one or two modules and consist of multiple choice and numerical questions. Every examination will have to be taken in the Canvas Learning Management System. The topics covered by each examination are mentioned below. Due dates for each will be announced as and when the topics are covered in class. While every attempt will be made to give at least 1 week lead time, shorter lead times may be given if deemed necessary by the instructor. Needless to say, each exam has to be taken individually by a student.
Guidance for Course Projects
Each Course Project will involve implementation of the models covered in one or two modules using real-world data sets and will be available in the Canvas Learning Management System. All implementations should be in Microsoft Excel. Students are required to submit a hard copy of the final report for every project and upload all MS-Excel files as one file into Canvas for a submission to be deemed complete. Please note that email submissions will not be accepted unless as an exception given unforeseen and extenuating circumstances. Students may work on each course project either individually or in groups of no more than 3 students. It is not necessary for a student to be part of the same group for every Course Project; for example, a student may work on one course project individually, another in a group of 2 students and the third one with another group comprising of 3
MBA 701 (Bhadury) - Spring 2016 – Course Syllabus…………3

students. In case a student chooses to work in a group, it is his/her responsibility to ensure that the Course
Project gets completed to his/her satisfaction and submitted by the required due date in the required format. All students who submit a given Course Project as a group will receive the same marks for that
Course Project.
Grades are based on the following "absolute" scale (i.e., there will not be any "curving"). The topics covered by each evaluation is listed in the table below along with the relative weight and due date for the evaluation.
Evaluation
Type
Topic(s) Covered By Evaluation
Total
Percent of Grade
Exam 1
Exam 2
Module 1: Introduction to Quantitative
Modeling & Descriptive Statistics
Module 2: Probability Concepts
8%
8%
Exam 3 Module 3: Estimation & Confidence Intervals 8%
Exam 4 Module 4: Hypotheses Testing 8%
Exam 5 Module 5: Regression Models 8%
Exam 6 Module 6: Decision Analysis 8%
Exam 7
Exam 8
Course
Project 1
Course
Project 2
Course
Project 3
Course
Project 4
Module 7: Linear Programming and
Applications
Module 8: Risk Analysis using Simulation
Module 1: Introduction to Quantitative
Modeling & Descriptive Statistics
Module 3 (Estimation & Confidence
Intervals)
Module 5: Regression Models
Module 7: Linear Programming and
Applications
8%
8%
9%
9%
9%
9%
Total 100.00%
Your letter grade for the course will be determined from your numerical score and the following table.
Percentage
Below 60%
60%-62%
63%-66%
67%-69%
70%-72%
73%-76%
Grade
F
D-
D
D+
C-
C
Percentage
77%-79%
80%-82%
83%-86%
87%-89%
90%-94%
95% and above
Grade
C+
B-
B
B+
A-
A
It will be announced in class after the completion of the appropriate module when the associated Examination and, where applicable, Course Project, will become available and be due. Every attempt will be made to provide at least 1 week for the completion of an Examination and Course Project. However, shorter
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completion times may be required if deemed necessary. As a rule, extensions and re-examinations will not be granted. Any request for extension should be made only for unavoidable and unforeseen circumstances (such as critical illness, summons to jury duty) or and should be backed by the appropriate documents (e.g., medical certificate, etc.) in order to qualify and should be requested before the due date.
Attendance is not mandatory but very strongly suggested for every class, especially given the quantitative and hands-on nature of the course. Additionally, as mentioned above, availability and due dates for
Examinations and Course Projects will all be announced in class. Finally, in class discussions will be the primary mode of communication in this course. If a student misses a class, it is his/her responsibility to ensure that s/he gets informed about all materials covered in a class and announcements made therein.
The instructor will attempt to also post important announcements made in class on Canvas, but this cannot be guaranteed for every announcement. The instructor will attempt to answer emails from students within two working days (does not include weekends and University holidays) between the start and end of the semester. Students are advised to check their official UNCG email for messages from the instructor at least once every day, preferably more.
You must abide by the UNCG Academic Integrity Policy on all assignments (papers, tests, quizzes etc.) that are part of this course. Failure to abide will result in the appropriate consequences. See: http://sa.uncg.edu/handbook/academic-integrity-policy/
The Bryan School has develop a set of guidelines on student behavior and expectations in and out of the classroom as well as what you should expect of me as faculty member. I will encourage you to read through those guidelines by the end of the first week of class. Here is a link to the pdf file for those guidelines: http://www.uncg.edu/bae/faculty_student_guidelines.pdf
While every effort will be made to abide by the rules stipulated in this outline, change may sometimes be necessary at the sole discretion of the instructor. If and when this happens, due care will be taken to give as much notice to the students as possible.
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