Fisher College of Business
The Ohio State University
(as of August 13, 2015; Subject to Change)
MBA 6273
Data Analysis for Managers
Autumn, 2015
Gerlach 375
Professor: John Gray
e-mail: gray.402@osu.edu
Office: 612 Fisher Hall
Phone: 247-8021
TA1: Matt Herridge
e-mail: herridge.5@osu.edu
TA2: Kevin Espenschied
e-mail: espenschied.13@osu.edu
This course develops the quantitative thinking and skills needed for managerial data analysis.
Large quantities of data are routinely available in all disciplines of business, from direct
marketers analyzing databases to identify target markets for new promotional material and
cross-selling activities, to investment firms that rely on security prices and economic forecasts to
identify the optimal composition of portfolios. When not available, it often makes sense to collect
data to aid in decision-making. The analysis of data—from large data sets or small—in
accounting, finance, marketing, operations and human resources is based on the same
underlying principles, and this course exposes students to these principles.
Effective learning in this course requires students to both understand how to go about
conducting data analysis, and be able to draw inferences that shed light on specific problems.
Students will develop an intuition about concepts like statistical significance and conditional
probability as they apply to business problems, and will be able to manipulate, graph and learn
from data using software they can bring with them to other classes and into the workplace (the
skills learned are transferable to other software packages) A few key purposes of this class are:
•
Give students experience with realistic problems and challenge them to develop their
intuition, logic and problem-solving skills.
•
Expose students to the use of data analysis across business disciplines.
•
Emphasize practical and marketable skills by developing both data analytic and
spreadsheet skills that will add value in other courses and in their careers.
Given these goals, students will get immediate exposure to graphing and describing the data
with summary measures, including measures of variability and association. Probability and
distributions are then introduced, which are foundational not only to data analysis but also to
many related activities not covered in this class such as simulation. Next, the concept of an
estimator is introduced as a means of making inferences about the broader population.
Confidence intervals and hypothesis tests follow, with the last weeks spent on the very
important topic of regression analysis.
Class Structure
Classes will be a mixture or lecture/discussion, live demonstration of techniques, and
activities/exercises performed by the students. As discussion and exercises will both involve
active learning by students, it is important to have read the assigned material and worked on
problems before class. Exercises will frequently involve the use of Microsoft Excel. If at all
possible, you should bring a laptop to class as we will often work through problems collectively.
These hands-on exercises are an important part of learning the material and honing skills. If
you do not have a laptop, then befriend someone in class who does and see if they will let you
work with them.
1
Textbook and Software:
The textbook for the course is:
Business Analytics: Data Analysis & Decision Making with Microsoft Excel by Albright,
Winston and Zappe, Cengage Learning, Fifth Edition.
ISBN-13: 978-1-133-62960-3
As noted in the pre-term, there are several ways to buy the book; there is also an ebook option
on cengagebrain.com. Please post any questions r/e the book to Carmen’s discussion forum
(the “Course Organization” topic). All analysis in the class will use Excel complemented by the
statistics add-on, StatTools, provided with purchase of the text book (as part of the
DecisionTools Suite). Note that this software is not compatible with Macintosh computers, but
Mac users can install programs such as Parallels to use the software. Students wishing to
perform the work in Minitab, SPSS, or SAS will have that option (including on exams, if we are
notified in advance), but we will not support software questions related to those packages. The
software is available in the computer labs. There is no course packet for this course. Handouts,
notes and other materials will be posted on Carmen, or (less frequently) handed out in class.
Course Web Site
There is a web site set up on Carmen that we will use extensively. It is an important source of
course information (e.g., a copy of this syllabus and the schedule are there). Any changes to
the syllabus or schedule will appear there as well; as well as any other important course
announcements. You should check it regularly (at least once the evening before any class). The
web site will also contain presentation decks, homework sets, homework answers, etc. Also,
the web site will have information regarding your grades in the course. The web site is only
accessible to students registered to this course, and your grades are only accessible to you.
The Web site is available on the Carmen online course page: http://carmen.osu.edu
Getting Help
I realize that this material can be challenging at times for many, so there are several methods to
seek help outside of class:
-
You can post questions on the course web site. I have created several discussion
forums in Carmen. Your question is likely to be one that others have as well, and it can
often answered effectively by one of your fellow learners. Please use this as your
primary option.
-
The TAs and I are available via e-mail (see top of syllabus for e-mail addresses). I am
also available via my office phone (247-8021).
-
One of the TAs will conduct a recitation on TBD from TBD - TBD in Room TBD. One of
the TAs will also be available for questions from TBD - TBD on TBD in Room TBD.
These are strictly optional.
-
I am available to discuss any issues of concern with you on an individual basis by
appointment. I can sometimes handle quick questions just before class, after class, or
between classes, but please understand that sometimes I may not be able to do so. I will
hold office hours for this class from 1:15-2:45 on Mondays. Please save face-to-face
meetings with me for this time block, if possible.
Students have found these methods very effective. However, if you ever have difficulty
reaching us or getting any of your needs met, please let me know.
2
Course Requirements and Grading
There are three exams. The exams are focused on the material since the last exam. The
subject matter, however, often builds on prior material and thus requires an understanding of it.
The exams will be closed book & notes, and in the computer lab and/or on supplied laptops in
the classroom. The exams cannot be retaken or taken at other than the scheduled time except
under extreme circumstances.
There will be problems assigned for every class. Typically, there will be two problems. One
problem will cover the material from the previous class, one problem will cover the material we
are about to cover. Although this work will not be graded, I cannot emphasize enough how
critical it is for you to do these problems, the practice problems, book example problems, and
other problems throughout the course. Further, you will need to do many more problems than
just the two assigned per class to be prepared for exams. Solutions to these problems, as well
as practice problems, will be posted at appropriate times.
Your grade will be determined mostly by your performance on the three exams, as indicated
below. I do reserve the right to deduct any appropriate amount of points for excessive absence,
tardiness, or any behavior not conducive to the learning environment. Grades will be “curved”
and cutoffs will be determined by natural breaks. Please note that a grade greater than 93%
does not guarantee an A, and that lower percentages will almost always result in higher grades
than the OSU standard scheme (e.g., a 75% of total will typically be better than a C). The
cutoffs for the final grades are entirely at my discretion.
Exam 1
Exam 2
Exam 3
Super Crunchers
Total Possible Points:
100
100
100
10
310
Super Crunchers: The book Super Crunchers by Ian Ayres provides compelling arguments for
the value of the material learned in this course, particularly hypothesis testing and regression.
For 10 points, complete the following assignment: Read the book, and provide me a 1-page
summary of the key points of the book and a 1-page outline of a “Super Crunching” opportunity
in your past or desired future workplace.
Grade Appeal Policy: Grades on exams are intended to reflect the overall quality of
performance of the student(s). We will carefully grade the exams, and are as consistent as
possible in giving partial credit where applicable. If you think your grade on an exam does not
reflect the quality of your performance, submit a clear written explanation via e-mail with your
reasoning within one week after the exam grades are posted. The written document need not be
long, but must clearly identify the problem or issue of concern. I will consider all such appeals.
There will be no grading appeals after the one-week deadline has passed.
--------------------------------------------------------------------------------------Academic Misconduct: Material submitted for course grade credit must be your own work. Please be
informed that both you and I must follow Faculty Rule 3335-5-54, which requires that “all instances of
what he or she believes may be academic misconduct" be reported to the University Academic
Misconduct Committee. Academic misconduct is a serious threat to the integrity and value of your
diploma. The main concern for this class is cheating on exams. You will not be allowed to, at any time
during an exam, have a calculator, memory stick, jump drive, cell phone, etc. out of your bag. Doing so is
a violation of course policy and may be referred to academic integrity. PLEASE, don’t do it!
Disability Accommodation: If you need an accommodation based on the impact of a disability, arrange
an appointment with me as soon as possible. I rely on the Office for Disability Services for assistance in
verifying need and developing accommodation strategies. You should start the verification process as
soon as possible. Accommodation requests made too close to the exam may possibly not be granted.
Inclement Weather: If school is open, class will be held. If school is closed, class will be cancelled. Use
your judgment on whether you choose to attend if school is open but the weather makes travel
problematic for you.
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Course Schedule—Data Analysis for Managers (subject to change)
Autumn Semester 2015
Mtg
Date
Day
1
26-Aug
Wed Course Intro / Describing Data
2
31-Aug
Mon
3
2-Sep
Wed Describing Data/Probability & Probability
Distributions
Mon Labor Day: No Class
7-Sep
4
9-Sep
5
Topic
READINGS
Describing Data
1
2-3
4.1-4.3
14-Sep
Wed Probability & Probability Distributions/
Simulation
Super Crunchers Assignments Due
Mon Simulation/Normal Distribution
5.1-5.3
6
16-Sep
Wed Normal/Binomial Distribution
5.4-5.5
7
21-Sep
Mon
8
23-Sep
Wed Binomial Distribution/Review
9
28-Sep
Mon EXAM 1 Chapters 2,3,4,5
10
30-Sep
Wed Sampling & Sampling Distributions
11
5-Oct
Mon
12
7-Oct
Wed Confidence Interval Estimation
13
12-Oct
Mon
14-Oct
Wed Term 1 Exams/Fall Break: No Class
14
19-Oct
Mon
15
21-Oct
Wed Hypothesis Testing
16
26-Oct
Mon
Hypothesis Testing
17
28-Oct
Mon
Application(s)
18
2-Nov
Mon EXAM 2 Chapters 7,8,9
19
4-Nov
Wed Regression
10.1-10.5
20
9-Nov
Mon
11.1-11.3,
11.9
11-Nov
Wed Veteran’s Day: No Class
21
16-Nov
Mon
22
18-Nov
Wed Regression
23
23-Nov
Mon
25-Nov
Wed Thanksgiving Break: No Class
24
30-Nov
Mon
25
2-Dec
Wed Regression
26
7-Dec
Mon
27
9-Dec
Wed Ethics in Data Analysis
11-Dec
Fri
4.4
Binomial Distribution
Sampling & Sampling Dist/Conf Int
Confidence Interval / Hypothesis Testing
7
8.1-8.3
8.5; 8.7-8.9
9.1-9.3
Hypothesis Testing
9.4-9.7
Regression
Regression
10.6-10.8
Regression
11.4-11.8
Regression
Application(s)
EXAM 3 Chapters 10,11
4