BUS-G572 Predictive Analytics for Business Strategy

Spring 2018

Instructor: Jeff Prince

Office: HH 3080P

Email: jeffprin@indiana.edu

Phone: 812-856-2692

Office Hours: Tuesday, 3:00-5:00

Course Description:

The purpose of this course is to teach students how data analysis can inform strategy, within a framework centered on logical reasoning and practical communication. In doing so, we will develop the analytical tools and hands-on experience with data and economic models to optimally utilize information in decision-making. The focus of the material will be on a subdivision of predictive analytics, called active prediction, which is most appropriate when evaluating business strategies. In addition, students will develop communication skills, particularly with regard to quantitative outputs.

Learning Outcomes:

Below is a list of learning outcomes for this course. These learning outcomes support learning

goal 3 listed at the end of the syllabus.

1. Students will be able to distinguish correlation from causality in regression analysis.

2. Students will be able to describe the business analytics model for a firm and the

Analyst’s place within it.

3. Students will be able to describe the scientific method and articulate how and why it is

well suited for establishing causality.

4. Students will be able to perform and explain both deductive and inductive reasoning in

the context of data analysis.

5. Students will be able to distinguish data mining from causal analysis, as well as passive

prediction from active prediction.

6. Students will be able to execute analysis suitable for active predictions.

7. Students will be able to write clearly and concisely about data analysis utilizing

regression techniques to establish causal relationships among strategic variables and

outcomes.

8. Students will be able to orally explain to a non-technical audience the key components

of data analysis establishing causality and its implications.

Required Textbook: Predictive Analytics for Business Strategy by Jeffrey Prince.

Recommended Textbook: Mostly Harmless Econometrics by Joshua Angrist and Jorn-Steffen

Pischke.

Evaluation: Quizzes (30%), Exam (35%), Homework (30%), Participation (5%).

Grading Policy: Students wishing a re-grade on an exam or quiz should present their concern in

person with me during office hours or an appointed time. The statute of limitations on re-

grade requests (not the actual meeting for the re-grade) is one week from the time the exam

was made available. Any granted re-grade request will result in a re-grade of the entire

document.

Attendance: Attendance for each class is expected. Frequent absence will have a negative

impact on the Participation grade.

Exams: The final exam and quizzes will be closed book. Exam and quiz attendance is required,

and a make-up exam/quiz will only be given for valid documented reasons.

Homework: Homework is due by end-of-day on Thursday (11:59pm), unless otherwise noted.

Course Materials: I will be using Canvas to post materials for this class. A soft copy of this

syllabus will be posted along with all announcements, class notes, data, and reading

assignments. I have also posted an outline detailing topics for each lecture, and the exam and

quiz dates.

Special Circumstances: Students requiring special accommodations for disability must contact

me outside class and present to me the memorandum of accommodation from the Office of

Disability Services for Students. Request for accommodation must be made two weeks in

advance of need, and must be authorized and acknowledged by me. Students who require

accommodations for religious belief, scheduling conflict, or other causes must make a written

request. No authorization should be assumed without a confirmation email from me. For

emergency situations you should provide any available evidence to support your request.

Grade Scale: Below is my grading scale for this class. These are the guaranteed grades for the

full range of possible point accumulation by semester’s end. The exam and quizzes may be

curved upward via an across-the-board point addition (e.g., +5 points) if deemed appropriate.

Points Grade

97+ A+

93-96 A

90-92 A-

87-89 B+

83-86 B

80-82 B-

77-79 C+

73-76 C

70-72 C-

67-69 D+

63-66 D

60-62 D-

<60 F

Proctoring Announcement: Portions of this course may be subject to electronic proctoring.

Video cameras may be used to monitor the room during student assessment activities,

including but not limited to, exams, tests, and quizzes. Video recordings may be used to

investigate or support disciplinary action. All access to and use of video equipment and

recordings will follow applicable IU policies.

Outline of Course Topics

I. The Business Analytics Model

II. The Roles of Data and Predictive Analytics in Business

a. Data Uses in Business

b. Data Features

c. Basic Uses of Data Analysis for Business

d. Data Analysis for the Past, Present, and Future

e. Active and Passive Prediction

III. Reasoning with Data

a. What is Reasoning?

b. Deductive Reasoning

c. Inductive Reasoning

IV. Reasoning from Sample to Population

a. Statistics Review

b. Confidence Intervals

c. Hypothesis Testing

d. The Interplay between Deductive and Inductive Reasoning in Active Predictions

V. The Scientific Method

a. Definition and Details

b. The Scientific Method and Causal Inference

c. Data Analysis Using the Scientific Method

d. Experimental vs. Non-Experimental Data

VI. Linear Regression as a Fundamental Predictive Tool

a. The Regression Line for a Dichotomous Treatment

b. The Regression Line for a Multi-Level Treatment

c. Sample Moments and Least Squares

d. Regression for Multiple Treatments

VII. Correlation vs. Causality in Regression Analysis

a. Regression Analysis for Correlation

b. Passive Prediction Using Regression

c. Regression Analysis for Causality

d. Active Prediction Using Regression

e. The Relevance of Model Fit for Passive and Active Prediction

VIII. Basic Methods for Establishing Causal Inference

a. Assessing Key Assumptions within a Causal Model

b. Control Variables

c. Proxy Variables

d. Form of the Determining Function

Kelley Learning Goals

This course directly or indirectly works to accomplish the following Kelley School of Business MBA Program Learning Goals:

Learning Goal 1: Internal Structures and Operations

Students who earn the MBA degree will demonstrate a thorough understanding of the internal structures and operations of businesses ranging in size from small to multinational

Learning Goal 2: External Environments

Students who earn the MBA degree will demonstrate a thorough understanding of the relationship between an organization and its external market and economic environment. Furthermore, students will comprehend how management decisions affect relevant stakeholders inside and outside of the firm.

Learning Goal 3: Integration of Tools and Techniques of Business

Students who earn the MBA degree will be able to integrate and apply the tools and techniques of business, drawing on a broad-based knowledge of the major functions (accounting, economics, finance, information systems, marketing, operations management, and strategy) to solve complex business problems and make sound business decisions. Learning Goal 4: Professional Skills

Students who earn the MBA degree will demonstrate micro-social and leadership skills necessary for lifelong career success. These skills reflect effective self-assessment, communication, and collaboration within an organization

Learning Goal 5: Legal and Ethical Considerations

Students who earn the MBA degree will think and articulate critically about ethical and legal considerations pertinent to the art of management and the execution of a business enterprise.