Predictive Analytics for Business Strategy: R E A S O N I N G F R O M DATA TO AC T I O N A B L E K N O W L E D G E
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ESSENTIALS OF ECONOMICS Brue, McConnell, and Flynn Essentials of Economics Fourth Edition
Mandel Economics: The Basics Third Edition
Schiller Essentials of Economics Tenth Edition
PRINCIPLES OF ECONOMICS Asarta and Butters Principles of Economics, Principles of Microeconomics, Principles of Macroeconomics Second Edition
Colander Economics, Microeconomics, and Macroeconomics Tenth Edition
Frank, Bernanke, Antonovics, and Heffetz Principles of Economics, Principles of Microeconomics, Principles of Macroeconomics Seventh Edition
Frank, Bernanke, Antonovics, and Heffetz Streamlined Editions: Principles of Economics, Principles of Microeconomics, Principles of Macroeconomics Third Edition
Karlan and Morduch Economics, Microeconomics, and Macroeconomics Second Edition
McConnell, Brue, and Flynn Economics, Microeconomics, Macroeconomics Twenty-First Edition
McConnell, Brue, and Flynn Brief Editions: Microeconomics and Macroeconomics Second Edition
Samuelson and Nordhaus Economics, Microeconomics, and Macroeconomics Nineteenth Edition
Schiller The Economy Today, The Micro Economy Today, and The Macro Economy Today Fifteenth Edition
Slavin Economics, Microeconomics, and Macroeconomics Eleventh Edition
ECONOMICS OF SOCIAL ISSUES Guell Issues in Economics Today Eighth Edition
Register and Grimes Economics of Social Issues Twenty-First Edition
ECONOMETRICS AND DATA ANALYSIS Gujarati and Porter Basic Econometrics Fifth Edition
Gujarati and Porter Essentials of Econometrics Fourth Edition
Hilmer and Hilmer Practical Econometrics First Edition
Prince Predictive Analytics for Business Strategy First Edition
MANAGERIAL ECONOMICS Baye and Prince Managerial Economics and Business Strategy Ninth Edition
Brickley, Smith, and Zimmerman Managerial Economics and Organizational Architecture Sixth Edition
Thomas and Maurice Managerial Economics Twelfth Edition
INTERMEDIATE ECONOMICS Bernheim and Whinston Microeconomics Second Edition
Dornbusch, Fischer, and Startz Macroeconomics Twelfth Edition
Frank Microeconomics and Behavior Ninth Edition
ADVANCED ECONOMICS Romer Advanced Macroeconomics Fourth Edition
MONEY AND BANKING Cecchetti and Schoenholtz Money, Banking, and Financial Markets Fifth Edition
URBAN ECONOMICS O’Sullivan Urban Economics Eighth Edition
LABOR ECONOMICS Borjas Labor Economics Seventh Edition
McConnell, Brue, and Macpherson Contemporary Labor Economics Eleventh Edition
PUBLIC FINANCE Rosen and Gayer Public Finance Tenth Edition
ENVIRONMENTAL ECONOMICS Field and Field Environmental Economics: An Introduction Seventh Edition
INTERNATIONAL ECONOMICS Appleyard and Field International Economics Ninth Edition
Pugel International Economics Sixteenth Edition
THE MCGRAW-HILL SERIES ECONOMICS
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Predictive Analytics for Business Strategy: R E A S O N I N G F R O M DATA TO AC T I O N A B L E K N O W L E D G E
Jeffrey T. Prince Professor of Business Economics & Public Policy Harold A. Poling Chair in Strategic Management Kelley School of Business Indiana University
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PREDICTIVE ANALYTICS FOR BUSINESS STRATEGY: REASONING FROM DATA TO ACTIONABLE KNOWLEDGE
Published by McGraw-Hill Education, 2 Penn Plaza, New York, NY 10121. Copyright © 2019 by McGraw-Hill Education. All rights reserved. Printed in the United States of America. No part of this publication may be repro- duced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written consent of McGraw-Hill Education, including, but not limited to, in any network or other electronic storage or transmission, or broadcast for distance learning.
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To Mom and Dad
—Jeffrey T. Prince
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about the author
Jeffrey T. Prince is Professor of Business Economics & Public Policy and Harold A. Poling Chair in Strategic Management at Indiana University’s Kelley School of Business. He received his BA, in economics and BS, in mathematics and statistics from Miami University in 1998 and earned a PhD in economics from Northwestern University in 2004. Prior to joining Indiana University, he taught graduate and undergraduate courses at Cornell University.
Jeff has won top teaching honors as a faculty member at both Indiana University and Cornell, and as a graduate student at Northwestern. He has a broad research agenda within applied economics, having written and published on topics that include demand in technology and telecommunications markets, Internet diffu- sion, regulation in health care, risk aversion in insurance markets, and quality competition among airlines. He is one of a small number of economists to have published in both the top journal in economics (American Economic Review) and the top journal in management (Academy of Management Journal). Professor Prince currently is a co-editor at the Journal of Economics and Management Strategy, and serves on the editorial board for Information Economics and Policy. In his free time, Jeff enjoys activities ranging from poker and bridge to running and racquetball.
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This book is meant to teach students how data analysis can inform strategy, within a framework centered on logical reasoning and practical communication.
The inspiration for this project comes from having taught for more than 20 years at the college level to a wide range of students (in mathematics and economics departments, and in business schools), covering a wide range of quantitative topics. During that time, it has become clear to me that the average business stu- dent recognizes, in principle, that quantitative skills are valuable. However, in practice (s)he often finds those skills intimidating and esoteric, wondering how exactly they will be useful in the workforce.
As of this writing, there are many econometrics books and many operations/data mining/business analytics books in the market. However, these books are generally geared toward the specialist, who needs to know the full methodological details. Hence, they are not especially approachable or appealing to the business student looking for a conceptual, broad-based understanding of the mate- rial. And in their design, it can be easy for students—specialists and nonspecialists alike—to “lose sight of the forest for the trees.”
As I see it, the problem with regard to data analysis is as fol- lows: There is a large group of future businesspeople, both future analysts and managers, who recognize data analysis can be valu- able. However, taking a course that is essentially a treatise on methodology and statistics causes the future managers to narrow their view toward simply “getting through” the course. In contrast, the future analysts may enjoy the material and often emerge understanding the methods and statistics, but lacking key skills to communicate and explain to managers what their results mean.
This book is designed to address the problem of the dual audi- ence, by focusing on the role of data analysis in forming business strategy via predictive analytics. I chose this focus since all busi- nesses, and virtually all management-level employees, must be mindful of the strategies they are following. Assessing the relative merit among a set of potential strategic moves generally requires one to forecast their future implications, often using data. Further, this component of predictive analytics contributes toward develop- ment of critical thinking about analytical findings. Both inside and outside of business, we are bombarded with statements with the following flavor: “If you do X, you should expect Y to happen.” (Commercials about the impact of switching insurance providers immediately come to mind.) A deep understanding of how data can inform strategy through predictive analytics will allow students to critically assess such statements.
Given its purpose, I believe this book can be the foundation of a course that will benefit both future analysts and managers. The course will give managers a basic understanding of what data can do in an important area of business (strategy formation) and present it in a way that doesn’t feel like a taxonomy of models and their statistical properties. Managers will thus develop a deeper understanding of the fundamental reasoning behind how and why data analysis can generate actionable knowledge, and be able to think critically about whether a given analysis has merit or not. Consequently, this course could provide future managers some valuable data training without forcing them to take a highly technical econometrics or data mining course. It will also serve as a natural complement to the strategy courses they take.
This course will give future analysts a bigger-picture under- standing of what their analysis is trying to accomplish, and the con- ditions under which it can be deemed successful. It will also give them tools to better reason through these ideas and communicate them to others. Hence, it will serve as a valuable complement to the other, more technically focused, analytics courses they take.
KEY PEDAGOGICAL FEATURES This text includes many features designed to ease the learning experience for students and the teaching process for instructors.
Data Challenges Each chapter opens by presenting a challenging data situation. In order for students to properly and effectively rise to the challenge, they must understand the material presented in that chapter.
At the end of the chapter, a concluding section titled Rising to the Data Challenge discusses how the challenge can be confront- ed and overcome using some of the newly acquired knowledge and skills that chapter develops.
These challenges, which bookend the chapter, are designed to motivate the reader to acquire the necessary skills by learning the chapter’s material and understanding how to apply it.
Learning Objectives Learning objectives in each chapter orga- nize the chapter content and enhance the learning experience.
Communicating Data Through real-world applications or expla- nations of text material in “layman’s terms,” Communicating Data examples demonstrate how to describe and explain data, data methods, and/or data results in a clear, intuitive manner. These
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examples are designed to enhance the reader’s ability to com- municate with a wide audience about data issues.
Reasoning Boxes Reasoning Boxes summarize main concepts from the text in the context of deductive and inductive reason- ing. By understanding reasoning structure, readers will be better equipped to draw and explain their own conclusions using data and to properly critique others’ data-based conclusions.
Demonstration Problems Beyond the opening Data Challenge, each chapter includes Demonstration Problems that help target and develop particular data skills. These are largely focused on primary applications of chapter material.
Key Terms and Marginal Definitions Each chapter ends with a list of key terms and concepts. These provide an easy way for instructors to assemble material covered in each chapter and for students to check their mastery of terminology. In addition, marginal definitions will appear as signposts throughout the text.
End-of-Chapter Problems Each chapter ends with two types of problems to test students’ mastery of the material. First are Conceptual Questions, which test students’ conceptual understand- ing of the material and demand pertinent communication and reasoning skills. Second are Quantitative Problems, which test stu- dents’ ability to execute and explain (within a logical framework) per- tinent data analytical methods. The Quantitative Problems are sup- ported by Excel datasets available through McGraw-Hill Connect®.
Applications The material in this book is sufficient for any course that exclusively uses quizzes, homework, and/or exams for evalua- tion. However, to allow for a more enhanced, and applied, under- standing of the material, the book concludes with an Applications section. This section has three parts. The first is “Critical Analysis of Data-Driven Conclusions.” This section presents several real- world data applications that explicitly or implicitly lead to action- able conclusions, and then challenges students to critically assess these conclusions using the reasoning and data knowledge presented throughout the book. The second section is “Written Explanations of Data Analysis and Active Predictions.” This sec- tion presents students with several mini-cases of data output, and challenges them to examine and explain the output in writing with appropriate reasoning. The third section is “Projects: Combining Analysis with Reason-based Communication.” This last section pro- vides three versions of a mini-project, based on projects Professor Prince has assigned in his own classes for several years. These projects require students to work from dataset to conclusions in a controlled, but realistic, environment. The projects are accom- panied by datasets in Excel format, which can be easily tailored to instructors’ needs. A key merit of these projects is flexibility, in that they can be used for individual- and/or group-level assessment.
CHAPTER LEARNING OBJECTIVES The organization of each chapter reflects common themes out- lined by six to eight learning objectives listed at the beginning of each chapter. These objectives, along with AACSB and Bloom’s taxonomy learning categories, are connected to the end-of- chapter material and test bank questions to offer a comprehensive and thorough teaching and learning experience.
ASSURANCE OF LEARNING READY Many educational institutions today are focused on the notion of assurance of learning, an important element of some accredi- tation standards. Predictive Analytics for Business Strategy is designed specifically to support your assurance of learning initia- tives with a simple, yet powerful solution.
Instructors can use Connect to easily query for learning out- comes/objectives that directly relate to the learning objectives of the course. You can then use the reporting features of Connect to aggregate student results in similar fashion, making the col- lection and presentation of assurance of learning data simple and easy.
AACSB STATEMENT McGraw-Hill Global Education is a proud corporate member of AACSB International. Understanding the importance and value of AACSB accreditation, Predictive Analytics for Business Strategy has sought to recognize the curricula guidelines detailed in the AACSB standards for business accreditation by connecting ques- tions in the test bank and end-of-chapter material to the general knowledge and skill guidelines found in the AACSB standards.
It is important to note that the statements contained in Predictive Analytics for Business Strategy are provided only as a guide for the users of this text. The AACSB leaves content cover- age and assessment within the purview of individual schools, the mission of the school, and the faculty. While Predictive Analytics for Business Strategy and the teaching package make no claim of any specific AACSB qualification or evaluation, we have labeled questions according to the general knowledge and skill areas.
MCGRAW-HILL CUSTOMER CARE CONTACT INFORMATION At McGraw-Hill, we understand that getting the most from new technology can be challenging. That’s why our services don’t stop after you purchase our products. You can e-mail our Product Specialists 24 hours a day to get product training online. Or you can search our knowledge bank of Frequently Asked Questions on our support website. For Customer Support, call 800-331-5094, or visit www.mhhe.com/support. One of our Technical Support Analysts will be able to assist you in a timely fashion.
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I would like to thank the following reviewers, as well as hundreds of students at Indiana University’s Kelley School of Business and
colleagues who unselfishly gave up their own time to provide com- ments and suggestions to improve this book.
Imam Alam University of Northern Iowa
Ahmad Bajwa University of Arkansas at Little Rock
Steven Bednar Elon University
Hooshang M. Beheshti Radford University
Anton Bekkerman Montana State University
Khurrum S. Bhutta Ohio University
Gary Black University of Southern Indiana
Andre Boik University of California, Davis
Ambarish Chandra University of Toronto
Richard Cox Arizona State University
Steven Cuellar Sonoma State University
Craig Depken University of North Carolina, Charlotte
Mark Dobeck Cleveland State University
Tim Dorr University of Bridgeport
Neal Duffy State University of New York at Plattsburgh
Jerry Dunn Southwestern Oklahoma State University
Kathryn Ernstberger Indiana University Southeast
Ana L. Rosado Feger Ohio University
Frederick Floss Buffalo State University
Chris Forman Georgia Institute of Technology
Avi Goldfarb University of Toronto
Michael Gordinier Washington University, St. Louis
Gauri Guha Arkansas State University
Kuang-Chung Hsu University of Central Oklahoma
Kyle Huff Georgia Gwinnett College
Jongsung Kim Bryant University
Ching-Chung Kuo University of North Texas
Lirong Liu Texas A&M University, Commerce
Stanislav Manonov Montclair State University
John Mansuy Wheeling Jesuit University
Ryan McDevitt Duke University
Alex Meisami Indiana University South Bend
Ignacio Molina Arizona State University
Georgette Nicolaides Syracuse University
Jie Peng St. Ambrose University
Jeremy Petranka Duke University
Kameliia Petrova State University of New York at Plattsburgh
Claudia Pragman Minnesota State University, Mankato
Reza Ramazani Saint Michael's College
Doug Redington Elon University
Sunil Sapra California State University, Los Angeles
Robert Seamans New York University
Mary Ann Shifflet University of Southern Indiana
Timothy Simcoe Boston University
Shweta Singh Kean University
John Louis Sparco Wilmington University
Arun Srinivasan Indiana University Southeast
Purnima Srinivasan Kean University
Leonie Stone State University of New York at Geneseo
Richard Szal Northern Arizona University
Vicar Valencia Indiana University South Bend
Timothy S. Vaughan University of Wisconsin, Eau Claire
Bindiganavale Vijayaraman The University of Akron
Padmal Vitharana Syracuse University
Razvan Vlaicu University of Maryland
Rubina Vohra New Jersey City University
Emily Wang University of Massachusetts, Amherst
Miao Wang Marquette University
Matthew Weinberg Drexel University
Andy Welki John Carroll University
John Whitehead Appalachian State University
Peter Wui University of Arkansas, Pine Bluff
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▪ Connect content is authored by the world’s best subject matter experts, and is available to your class through a simple and intuitive interface.
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▪ Multimedia content such as videos, simulations, and games drive student engagement and critical thinking skills.
▪ Connect’s assignments help students contextualize what they’ve learned through application, so they can better understand the material and think critically.
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▪ SmartBook helps students study more efficiently by delivering an interactive reading experience through adaptive highlighting and review.
McGraw-Hill Connect® is a highly reliable, easy-to-use homework and learning management solution that utilizes learning science and award-winning adaptive tools to improve student results.
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▪ Connect Insight® generates easy-to-read reports on individual students, the class as a whole, and on specific assignments.
▪ The Connect Insight dashboard delivers data on performance, study behavior, and effort. Instructors can quickly identify students who struggle and focus on material that the class has yet to master.
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I understand that the reliability and accuracy of the book and the accompanying supplements are of the utmost importance. To that end, I have been personally involved in crafting and accuracy checking each of the supplements. The following ancillaries are available for quick download and convenient access via the instructor resource material available through Connect.
POWERPOINT PRESENTATION Presentation slides incorporate both the fundamental concepts of each chapter and the graphs and figures essential to each topic. These slides can be edited, printed, or rearranged to fit the needs of your course.
SOLUTIONS MANUAL This manual contains solutions to the end-of-chapter conceptual questions and quantitative problems.
TEST BANK A comprehensive test bank offers hundreds of questions catego- rized by learning objective, AACSB learning category, Bloom’s taxonomy objectives, and level of difficulty.
COMPUTERIZED TEST BANK TestGen is a complete, state-of-the-art test generator and edit- ing application software that allows instructors to quickly and easily select test items from McGraw Hill’s test bank content. The instructors can then organize, edit and customize ques- tions and answers to rapidly generate tests for paper or online administration. Questions can include stylized text, symbols, graphics, and equations that are inserted directly into ques- tions using built-in mathematical templates. TestGen’s random generator provides the option to display different text or calcu- lated number values each time questions are used. With both quick and simple test creation and flexible and robust editing tools, TestGen is a complete test generator system for today’s educators.
ONLINE RESOURCES Student supplements for Predictive Analytics for Business Strategy are available online at www.mhhe.com/prince1e. These include datasets for all Quantitative Problems and sample datasets for the Course Project.
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chapter 1 The Roles of Data and Predictive Analytics in Business 1
chapter 2 Reasoning with Data 32
chapter 3 Reasoning from Sample to Population 55
chapter 4 The Scientific Method: The Gold Standard for Establishing Causality 83
chapter 5 Linear Regression as a Fundamental Descriptive Tool 113
chapter 6 Correlation vs. Causality in Regression Analysis 151
chapter 7 Basic Methods for Establishing Causal Inference 187
chapter 8 Advanced Methods for Establishing Causal Inference 224
chapter 9 Prediction for a Dichotomous Variable 258
chapter 10 Identification and Data Assessment 292
APPLICATIONS Data Analysis Critiques, Write-ups, and Projects 322
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The Roles of Data and Predictive Analytics in Business 1 Data Challenge: Navigating a Data Dump 1 Introduction 2 Defining Data and Data Uses in Business 2
Data 3 Predictive Analytics within Business Analytics 3 Business Strategy 4 Predictive Analytics for Business Strategy 4
Data Features 5 Structured vs. Unstructured Data 5 The Unit of Observation 6 Data-generating Process 9
Basic Uses of Data Analysis for Business 12 Queries 12 Pattern Discovery 15 Causal Inference 17
Data Analysis for the Past, Present, and Future 19 Lag and Lead Information 19 Predictive Analytics 22
Active Prediction for Business Strategy Formation 25 Rising to the Data Challenge 26 Summary / Key Terms and Concepts / Conceptual Questions / Quantitative Problems
● COMMUNICATING DATA 1.1: Is/Are Data Singular or Plural? 4
● COMMUNICATING DATA 1.2: Elaborating on Data Types 10
● COMMUNICATING DATA 1.3: Situational Batting Averages 14
● COMMUNICATING DATA 1.4: Indirect Causal Relationships in Purse Knockoffs 18
● COMMUNICATING DATA 1.5: Passive and Active Prediction in Politics and Retail 25
Reasoning with Data 32 Data Challenge: Testing for Sex Imbalance 32 Introduction 33 What is Reasoning? 33 Deductive Reasoning 35
Definition and Examples 35 Empirically Testable Conclusions 41
Inductive Reasoning 43 Definition and Examples 43 Evaluating Assumptions 45 Selection Bias 49
Rising to the Data Challenge 51 Summary / Key Terms and Concepts / Conceptual Questions / Quantitative Problems
● COMMUNICATING DATA 2.1: Deducing Guilt and Innocence 39
● COMMUNICATING DATA 2.2: Inductive Reasoning via Customer Testimonies 45
● COMMUNICATING DATA 2.3: Selection Bias in News Network Polls 51
● REASONING BOX 2.1: Direct Proof and Transposition 38 ● REASONING BOX 2.2: Inductive Reasoning for Evaluating
● REASONING BOX 2.3: Selection Bias in Inductive Reasoning 50
Reasoning from Sample to Population 55 Data Challenge: Knowing All Your Customers by Observing a Few 55 Introduction 56 Distributions and Sample Statistics 57
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Distributions of Random Variables 57 Data Samples and Sample Statistics 61
The Interplay Between Deductive and Inductive Reasoning in Active Predictions 77 Rising to the Data Challenge 79 Summary / Key Terms and Concepts / Conceptual Questions / Quantitative Problems
● COMMUNICATING DATA 3.1: What Can Political Polls Tell Us about the General Population? 70
● COMMUNICATING DATA 3.2: Does Working at Work Make a Difference? 77
● REASONING BOX 3.1: The Distribution of the Sample Mean 66
● REASONING BOX 3.2: Confidence Intervals 68 ● REASONING BOX 3.3: The Distribution of the Sample
Mean for Hypothesized Population Mean 71
● REASONING BOX 3.4: Hypothesis Testing 76 ● REASONING BOX 3.5: Reasoning in Active Predictions 78
The Scientific Method: The Gold Standard for Establishing Causality 83 Data Challenge: Does Dancing Yield Dollars? 83 Introduction 84 The Scientific …
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