Assignment Reading Chapter 2. Reasoning with Data Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was

Assignment Reading Chapter 2. Reasoning with Data
Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was

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Assignment Reading Chapter 2. Reasoning with Data
Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was the most important concept(s), method(s), term(s), and/or any other thing that you felt was worthy of your understanding in each assigned textbook chapter.Your initial post should be based upon the assigned reading for the week, so the textbook should be a source listed in your reference section and cited within the body of the text. Other sources are not required but feel free to use them if they aid in your discussion.
Also, provide a graduate-level response to each of the following questions:
Chapter 2 introduces deductive and inductive reasoning. Please explain both of these methods and give real life examples.
450+ words Predictive Analytics for
Business Strategy:

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Brue, McConnell, and Flynn
Essentials of Economics
Fourth Edition

Economics: The Basics
Third Edition

Essentials of Economics
Tenth Edition

Asarta and Butters
Principles of Economics,
Principles of Microeconomics,
Principles of Macroeconomics
Second Edition

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Issues in Economics Today
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Economics of Social Issues
Twenty-First Edition

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Basic Econometrics
Fifth Edition

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Essentials of Econometrics
Fourth Edition

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First Edition

Predictive Analytics for Business Strategy
First Edition

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Ninth Edition

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Managerial Economics and Organizational
Sixth Edition

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Twelfth Edition

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Second Edition

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Twelfth Edition

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International Economics
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Predictive Analytics for
Business Strategy:

Jeffrey T. Prince
Professor of Business Economics & Public Policy
Harold A. Poling Chair in Strategic Management
Kelley School of Business
Indiana University

pri91516_FM_i-xviii.indd 3 10/31/17 4:41 PM


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
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Some ancillaries, including electronic and print components, may not be available to customers outside the
United States.

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ISBN 978-1-259-19151-0
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The Internet addresses listed in the text were accurate at the time of publication. The inclusion of a website does not
indicate an endorsement by the authors or McGraw-Hill Education, and McGraw-Hill Education does not guarantee
the accuracy of the information presented at these sites.

pri91516_FM_i-xviii.indd 4 10/31/17 4:41 PM

To Mom and Dad

—Jeffrey T. Prince

pri91516_FM_i-xviii.indd 5 10/31/17 4:41 PM

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.


pri91516_FM_i-xviii.indd 6 10/31/17 4:41 PM


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.

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


pri91516_FM_i-xviii.indd 7 10/31/17 4:41 PM

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.


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.

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.

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.

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 One of our Technical Support
Analysts will be able to assist you in a timely fashion.

viii Preface

pri91516_FM_i-xviii.indd 8 10/31/17 4:41 PM

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

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,

Mark Dobeck
Cleveland State University

Tim Dorr
University of Bridgeport

Neal Duffy
State University of New York at

Jerry Dunn
Southwestern Oklahoma State

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,

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

Claudia Pragman
Minnesota State University,

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

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,

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

pri91516_FM_i-xviii.indd 9 10/31/17 4:41 PM

▪ 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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their reading material on smartphones and tablets. They
can study on the go and don’t need internet access to use
the eBook as a reference, with full functionality.

▪ Multimedia content such as videos, simulations, and games
drive student engagement and critical thinking skills.

©McGraw-Hill Education

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efficiently by delivering an interactive reading
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utilizes learning science and award-winning adaptive
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Over 7 billion questions have been
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Using Connect improves retention
rates by 19.8%, passing rates by
12.7%, and exam scores by 9.1%.

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More students earn
As and Bs when they

use Connect.

©Hero Images/Getty Images

▪ 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.

▪ Connect automatically grades assignments
and quizzes, providing easy-to-read reports
on individual and class performance.

▪ Connect integrates with your LMS to provide single sign-on and automatic syncing
of grades. Integration with Blackboard®, D2L®, and Canvas also provides automatic
syncing of the course calendar and assignment-level linking.

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of your implementation.

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tricks from super users, you can find tutorials as you work. Our Digital Faculty
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you want with Connect.

Trusted Service and Support

Robust Analytics and Reporting

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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

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.

This manual contains solutions to the end-of-chapter conceptual
questions and quantitative problems.

A comprehensive test bank offers hundreds of questions catego-
rized by learning objective, AACSB learning category, Bloom’s
taxonomy objectives, and level of difficulty.

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

Student supplements for Predictive Analytics for Business Strategy
are available online at These include
datasets for all Quantitative Problems and sample datasets for the
Course Project.

xii Acknowledgments


pri91516_FM_i-xviii.indd 12 10/31/17 4:41 PM


brief contents

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


pri91516_FM_i-xviii.indd 13 10/31/17 4:41 PM



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

Assumptions 46

● 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


pri91516_FM_i-xviii.indd 14 10/31/17 4:41 PM

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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