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Fisher College of Business The Ohio State University (as of August 17, 2015; Subject to Change)

MBA 6273 Data Analysis for Managers

Autumn, 2015 Gerlach 375

Professor: John Gray TA1: Matt Herridge e-mail: gray.402@osu.edu e-mail: herridge.5@osu.edu Office: 612 Fisher Hall TA2: Kevin Espenschied Phone: 247-8021 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.

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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 Fridays in Room 375. In Term 1, they will be offered twice: 8:30-10 and 10:15-11:45. In Term 2, one session will be offered from 10:15-11:45. One of the TAs will also be available for office hours from TBD - TBD on TBD in Room TBD. Both the TA recitations and office hours 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 1-2:30 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.

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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 1 100 Exam 2 100 Exam 3 100

Super Crunchers 10 Total Possible Points: 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 Topic READINGS

1 26-Aug Wed Course Intro / Describing Data 1

2 31-Aug Mon Describing Data 2-3

3 2-Sep Wed Describing Data/Probability & Probability Distributions

4.1-4.3

7-Sep Mon Labor Day: No Class

4 9-Sep Wed Probability & Probability Distributions/ Simulation Super Crunchers Assignments Due

4.4

5 14-Sep Mon Simulation/Normal Distribution 5.1-5.3

6 16-Sep Wed Normal/Binomial Distribution 5.4-5.5

7 21-Sep Mon Binomial Distribution

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 7

11 5-Oct Mon Sampling & Sampling Dist/Conf Int 8.1-8.3

12 7-Oct Wed Confidence Interval Estimation 8.5; 8.7-8.9

13 12-Oct Mon Confidence Interval / Hypothesis Testing 9.1-9.3

14-Oct Wed Term 1 Exams/Fall Break: No Class

14 19-Oct Mon Hypothesis Testing 9.4

15 21-Oct Wed Hypothesis Testing 9.5-9.7

16 26-Oct Mon Hypothesis Testing

17 28-Oct Mon Hypothesis Testing

18 2-Nov Mon EXAM 2 Chapters 7,8,9

19 4-Nov Wed Regression 10.1-10.5

20 9-Nov Mon Regression 11.1-11.3, 11.9

11-Nov Wed Veteran’s Day: No Class

21 16-Nov Mon Regression 10.6-10.8

22 18-Nov Wed Regression

23 23-Nov Mon Regression 11.4-11.8

25-Nov Wed Thanksgiving Break: No Class

24 30-Nov Mon Regression

25 2-Dec Wed Regression

26 7-Dec Mon Regression

27 9-Dec Wed Regression

11-Dec Fri EXAM 3 Chapters 10,11