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Data Visualization Exploring and Explaining with Data Jeffrey D. Camm James J. Cochran Wake Forest University University of Alabama Michael J. Fry Jeffrey W. Ohlmann University of Cincinnati University of Iowa Australia ● Brazil ● Canada ● Mexico ● Singapore ● United Kingdom ● United States Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it. For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit www.cengage.com/highered to search by ISBN#, author, title, or keyword for materials in your areas of interest. Important Notice: Media content referenced within the product description or the product text may not be available in the eBook version. Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Data Visualization: Exploring and © 2022 Cengage Learning, Inc. Explaining with Data, WCN: 02-300 First Edition Jeffrey D. Camm, James J. Cochran, Unless otherwise noted, all content is © Cengage. Michael J. Fry, Jeffrey W. Ohlmann ALL RIGHTS RESERVED. No part of this work covered by the copyright herein may be reproduced or distributed in any form or by any means, SVP, Higher Education & Skills Product: except as permitted by U.S. copyright law, without the prior written Erin Joyner permission of the copyright owner. VP, Higher Education & Skills Product: Michael Schenk For product information and technology assistance, contact us at Product Director: Joe Sabatino Cengage Customer & Sales Support, 1-800-354-9706 or support.cengage.com. Senior Product Manager: Aaron Arnsparger Senior Learning Designer: Brandon Foltz For permission to use material from this text or product, submit all requests online at Senior Content Manager: Conor Allen www.cengage.com/permissions. Digital Delivery Lead: Mark Hopkinson Marketing Director: Danae April Library of Congress Control Number: 2021930729 Executive Marketing Manager: Nate Anderson ISBN: 978-0-357-63134-8 IP Analyst: Ashley Maynard Cengage IP Project Manager: Kelli Besse 200 Pier 4 Boulevard Production Service: MPS Limited Boston, MA 02210 USA Designer: Chris Doughman Cover Image Source: Cengage is a leading provider of customized learning solutions with iStockPhoto.com/mpilecky employees residing in nearly 40 different countries and sales in more than 125 countries around the world. Find your local representative at www.cengage.com. To learn more about Cengage platforms and services, register or access your online learning solution, or purchase materials for your course, visit www.cengage.com. Printed in the United States of America Print Number: 01 Print Year: 2021 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Brief Contents ABOUT THE AUTHORS xi PREFACE xiii Chapter 1 Introduction 2 Chapter 2 Selecting a Chart Type 26 Chapter 3 Data Visualization and Design 76 Chapter 4 Purposeful Use of Color 128 Chapter 5 Visualizing Variability 174 Chapter 6 Exploring Data Visually 226 Chapter 7 Explaining Visually to Influence with Data 284 Chapter 8 Data Dashboards 322 Chapter 9 Telling the Truth with Data Visualization 360 RefeRences 397 Index 399 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Contents ABOUT THE AUTHORS xi PREFACE xiii Chapter 1 Introduction 2 1.1 Analytics 3 1.2 Why Visualize Data? 4 Data Visualization for Exploration 4 Data Visualization for Explanation 7 1.3 Types of Data 8 Quantitative and Categorical Data 8 Cross-Sectional and Time Series Data 9 Big Data 10 1.4 Data Visualization in Practice 11 Accounting 11 Finance 12 Human Resource Management 13 Marketing 14 Operations 14 Engineering 16 Sciences 16 Sports 17 Summary 18 Glossary 19 Problems 20 Chapter 2 Selecting a Chart type 26 2.1 Defining the Goal of Your Data Visualization 28 Selecting an Appropriate Chart 28 2.2 Creating and Editing Charts in Excel 29 Creating a Chart in Excel 30 Editing a Chart in Excel 30 2.3 Scatter Charts and Bubble Charts 32 Scatter Charts 32 Bubble Charts 33 2.4 Line Charts, Column Charts, and Bar Charts 35 Line Charts 35 Column Charts 39 Bar Charts 41 2.5 Maps 42 Geographic Maps 42 Heat Maps 44 Treemaps 45 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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vi Contents 2.6 When to Use Tables 47 Tables versus Charts 47 2.7 Other Specialized Charts 49 Waterfall Charts 49 Stock Charts 51 Funnel Charts 52 2.8 A Summary Guide to Chart Selection 54 Guidelines for Selecting a Chart 54 Some Charts to Avoid 55 Excel’s Recommended Charts Tool 57 Summary 59 Glossary 60 Problems 61 Chapter 3 Data Visualization and Design 76 3.1 Preattentive Attributes 78 Color 81 Form 81 Length and Width 84 Spatial Positioning 87 Movement 87 3.2 Gestalt Principles 88 Similarity 88 Proximity 88 Enclosure 89 Connection 89 3.3 Data-Ink Ratio 91 3.4 Other Data Visualization Design Issues 98 Minimizing Eye Travel 98 Choosing a Font for Text 100 3.5 Common Mistakes in Data Visualization Design 102 Wrong Type of Visualization 102 Trying to Display Too Much Information 104 Using Excel Default Settings for Charts 106 Too Many Attributes 108 Unnecessary Use of 3D 109 Summary 111 Glossary 111 Problems 112 Chapter 4 purposeful Use of Color 128 4.1 Color and Perception 130 Attributes of Color: Hue, Saturation, and Luminance 130 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Contents vii Color Psychology and Color Symbolism 132 Perceived Color 132 4.2 Color Schemes and Types of Data 135 Categorical Color Schemes 135 Sequential Color Schemes 137 Diverging Color Schemes 139 4.3 Custom Color Using the HSL Color System 141 4.4 Common Mistakes in the Use of Color in Data Visualization 146 Unnecessary Color 146 Excessive Color 148 Insufficient Contrast 151 Inconsistency Across Related Charts 153 Neglecting Colorblindness 153 Not Considering the Mode of Delivery 156 Summary 156 Glossary 157 Problems 157 Chapter 5 Visualizing Variability 174 5.1 Creating Distributions from Data 176 Frequency Distributions for Categorical Data 176 Relative Frequency and Percent Frequency 179 Visualizing Distributions of Quantitative Data 181 5.2 Statistical Analysis of Distributions of Quantitative Variables 193 Measures of Location 193 Measures of Variability 194 Box and Whisker Charts 197 5.3 Uncertainty in Sample Statistics 200 Displaying a Confidence Interval on a Mean 201 Displaying a Confidence Interval on a Proportion 203 5.4 Uncertainty in Predictive Models 205 Illustrating Prediction Intervals for a Simple Linear Regression Model 205 Illustrating Prediction Intervals for a Time Series Model 208 Summary 211 Glossary 211 Problems 213 Chapter 6 exploring Data Visually 226 6.1 Introduction to Exploratory Data Analysis 228 Espléndido Jugo y Batido, Inc. Example 229 Organizing Data to Facilitate Exploration 230 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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viii Contents 6.2 Analyzing Variables One at a Time 234 Exploring a Categorical Variable 234 Exploring a Quantitative Variable 237 6.3 Relationships between Variables 242 Crosstabulation 242 Association between Two Quantitative Variables 247 6.4 Analysis of Missing Data 256 Types of Missing Data 256 Exploring Patterns Associated with Missing Data 258 6.5 Visualizing Time-Series Data 260 Viewing Data at Different Temporal Frequencies 260 Highlighting Patterns in Time Series Data 262 Rearranging Data for Visualization 266 6.6 Visualizing Geospatial Data 269 Choropleth Maps 269 Cartograms 272 Summary 273 Glossary 274 Problems 275 Chapter 7 explaining Visually to Influence with Data 284 7.1 Know Your Audience 287 Audience Member Needs 287 Audience Member Analytical Comfort Levels 289 7.2 Know Your Message 292 What Helps the Decision Maker? 293 Empathizing with Data 294 7.3 Storytelling with Charts 300 Choosing the Correct Chart to Tell Your Story 300 Using Preattentive Attributes to Tell Your Story 304 7.4 Bringing It All Together: Storytelling and Presentation Design 306 Aristotle’s Rhetorical Triangle 307 Freytag’s Pyramid 308 Storyboarding 311 Summary 313 Glossary 313 Problems 314 Chapter 8 Data Dashboards 322 8.1 What Is a Data Dashboard? 324 Principles of Effective Data Dashboards 325 Applications of Data Dashboards 325 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Contents ix 8.2 Data Dashboards Taxonomies 327 Data Updates 327 User Interaction 327 Organizational Function 328 8.3 Data Dashboard Design 328 Understanding the Purpose of the Data Dashboard 329 Considering the Needs of the Data Dashboard’s Users 329 Data Dashboard Engineering 330 8.4 Using Excel Tools to Build a Data Dashboard 331 Espléndido Jugo y Batido, Inc. 331 Using PivotTables, PivotCharts, and Slicers to Build a Data Dashboard 332 Linking Slicers to Multiple PivotTables 343 Protecting a Data Dashboard 346 Final Review of a Data Dashboard 347 8.5 Common Mistakes in Data Dashboard Design 348 Summary 349 Glossary 349 Problems 350 Chapter 9 telling the truth with Data Visualization 360 9.1 Missing Data and Data Errors 363 Identifying Missing Data 363 Identifying Data Errors 366 9.2 Biased Data 369 Selection Bias 369 Survivor Bias 372 9.3 Adjusting for Inflation 374 9.4 Deceptive Design 377 Design of Chart Axes 377 Dual-Axis Charts 381 Data Selection and Temporal Frequency 382 Issues Related to Geographic Maps 386 Summary 388 Glossary 389 Problems 389 referenCeS 397 InDex 399 Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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About the Authors Jeffrey D. Camm is Inmar Presidential Chair and Senior Associate Dean of Business Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he was on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, INFORMS Journal on Applied Analytics, and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati, and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in prac- ticing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016, Professor Camm received the George E. Kimball Medal for service to the operations research profession, and in 2017 he was named an INFORMS Fellow. James J. Cochran is Associate Dean for Research, Professor of Applied Statistics, and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. from Wright State University and his Ph.D. from the Uni- versity of Cincinnati. He has been at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa, and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 50 papers in the development and application of operations research and statistical methods. He has published in several journals, including Management Science, The American Statistician, Communications in Statistics—Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Jour- nal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Opera- tions Research Practice, 2010 Mu Sigma Rho Statistical Education Award, and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005, named a Fellow of the American Statistical Association in 2011, and named a Fellow of INFORMS in 2017. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statis- tical Association, and he received the INFORMS President’s Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has served as an operations research consul- tant to numerous companies and not-for-profit organizations. He served as editor-in-chief of INFORMS Transactions on Education and is on the editorial board of INFORMS Journal on Applied Analytics, International Transactions in Operational Research, and Significance. Michael J. Fry is Professor of Operations, Business Analytics, and Information Systems (OBAIS) and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he served as Department Head from 2014 to 2018 and has been named a Lindner Research Fellow. He has also been a visit- ing professor at Cornell University and at the University of British Columbia. Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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xii About the Authors Professor Fry has published more than 25 research papers in journals such as Opera- tions Research, Manufacturing and Service Operations Management, Transportation Sci- ence, Naval Research Logistics, IIE Transactions, Critical Care Medicine, and Interfaces. He serves on editorial boards for journals such as Production and Operations Management, INFORMS Journal on Applied Analytics (formerly Interfaces), and Journal of Quantitative Analysis in Sports. His research interests are in applying analytics to the areas of supply chain management, sports, and public-policy operations. He has worked with many different orga- nizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals, and the Cincinnati Zoo and Botanical Gardens. In 2008, he was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati. In 2019, he led the team that was awarded the INFORMS UPS George D. Smith Prize on behalf of the OBAIS Department at the University of Cincinnati. Jeffrey W. Ohlmann is Associate Professor of Business Analytics and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska and M.S. and Ph.D. degrees from the University of Michigan. He has been at the University of Iowa since 2003. Professor Ohlmann’s research on the modeling and solution of decision-making prob- lems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Trans- portation Science, and European Journal of Operational Research. He has collaborated with organizations such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elec- tions, and three National Football League franchises. Because of the relevance of his work to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice. Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Preface Data Visualization: Exploring and Explaining with Data is designed to introduce best practices in data visualization to undergraduate and graduate students. This is one of the first books on data visualization designed for college courses. The book contains material on effective design, choice of chart type, effective use of color, how to explore data visually, how to build data dashboards, and how to explain concepts and results visually in a compelling way with data. In an increasingly data-driven economy, these concepts are becoming more important for analysts, natural scientists, social scientists, engineers, medical professionals, business professionals, and virtually everyone who needs to interact with data. Indeed, the skills developed in this book will be helpful to all who want to influence with data or be accurately informed by data. The book is designed for a semester-long course at either the undergraduate or graduate level. The examples used in this book are drawn from a variety of functional areas in the business world including accounting, finance, operations, and human resources as well as from sports, politics, science, medicine, and economics. The intention is that this book will be relevant to students at either the undergraduate or graduate level in a business school as well as to students studying in other academic areas. Data Visualization: Exploring and Explaining with Data is written in a style that does not require advanced knowledge of mathematics or statistics. The first five chapters cover foundational issues important to constructing good charts. Chapter 1 introduces data visual- ization and how it fits into the broader area of analytics. A brief history of data visualization is provided as well as a discussion of the different types of data and examples of a variety of charts. Chapter 2 provides guidance on selecting an appropriate type of chart based on the goals of the visualization and the type of data to be visualized. Best practices in chart design, including discussions of preattentive attributes, Gestalt principles, and the data-ink ratio, are covered in Chapter 3. Chapter 4 discusses the attributes of color, how to use color effectively, and some common mistakes in the use of color in data visualization. Chapter 5 covers the im- portant topic of visualizing and describing variability that occurs in observed values. Chapter 5 introduces the visualization of frequency distributions for categorical and quantitative vari- ables, measures of location and variability, and confidence intervals and prediction intervals. Chapters 6 and 7 cover how to explore and explain with data visualization in detail with examples. Chapter 6 discusses the use of visualization in exploratory data analysis. The ex- ploration of individual variables as well as the relationship between pairs of variables is con- sidered. The organization of data to facilitate exploration is discussed as well as the effect of missing data. The special considerations of visualizing time series data and geospatial data are also presented. Chapter 7 provides important coverage of how to explain and influence with data visualization, including knowing your message, understanding the needs of your audience, and using preattentive attributes to better convey your message. Chapter 8 is a discussion of how to design and construct data dashboards, collections of data visualizations used for decision making. Finally, Chapter 9 covers the responsible use of data visualization to avoid confusing or misleading your audience. Chapter 9 addresses the importance of understanding your data in order to best convey insights accurately and also discusses how design choices in a data visualization affect the insights conveyed to the audience. This textbook can be used by students who have previously taken a basic statistics course as well as by students who have not had a prior course in statistics. The two most techni- cal chapters, Chapters 5 (Visualizing Variability) and 6 (Exploring Data Visually), do not assume a previous course in statistics. All technical concepts are gently introduced. For students who have had a previous statistics class, the statistical coverage in these chapters provides a good review within a treatment where the focus is on visualization. The book of- fers complete coverage for a full course in data visualization, but it can also support a basic statistics or analytics course. The following table gives our recommendations for chapters to use to support a variety of courses. Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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xiv Preface Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter 1 2 3 4 5 6 7 8 9 Intro Chart Type Design Color Variability Exploring Explaining Dashboards Truth Full Data Visualiza- • • • • • • • • • tion Course Data Visualization Course Focused on • • • • • • Presentation Part of a Basic Statis- • • • • • • tics Course Part of an Analytics • • • • • • Course Features and Pedagogy The style and format of this textbook are similar to our other textbooks. Some of the specific features that we use in this textbook are listed here. ●● Data Visualization Makeover: With the exception of Chapter 1, each chapter contains a Data Visualization Makeover. Each of these vignettes presents a real visualization that can be improved using the principles discussed in the chapter. We present the original data visualization and then discuss how it can be improved. The examples are drawn from many different organizations in a variety of areas including government, retail, sports, science, politics, and entertainment. ●● Learning Objectives: Each chapter has a list of learning objectives of that chapter. The list provides details of what students should be able to do and understand once they have completed the chapter. ●● Software: Because of its widespread use and ease of availability, we have chosen Microsoft Excel as the software to illustrate the best practices and principles contained herein. Excel has been thoroughly integrated throughout this textbook. Whenever we introduce a new type of chart or table, we provide detailed step-by-step instructions for how to create the chart or table in Excel. Step-by-step instructions for creating many of the charts and tables from the textbook using Tableau and Power BI are also available in MindTap. ●● Notes and Comments: At the end of many sections, we provide Notes and Comments to give the student additional insights about the material presented in that section. Additionally, margin notes are used throughout the textbook to provide insights and tips related to the specific material being discussed. ●● End-of-Chapter Problems: Each chapter contains at least 15 problems to help the stu- dent master the material presented in that chapter. The problems are separated into Conceptual and Applications problems. Conceptual problems test the student’s under- standing of concepts presented in the chapter. Applications problems are hands-on and require the student to construct or edit charts or tables. ●● DATAfiles and CHARTfiles: All data sets used as examples and in end-of-chapter problems are Excel files designated as DATAfiles and are available for download by the student. The names of the DATAfiles are called out in margin notes throughout the textbook. Similarly, some Excel files with completed charts are available for download and are designated as CHARTfiles. Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Preface xv MindTap MindTap is a customizable digital course solution that includes an interactive eBook, auto-graded exercises and problems from the textbook with solutions feedback, interactive visualization applets with quizzes, chapter overview and problem walk-through videos, and more! MindTap also includes step-by-step instructions for creating charts and tables from the textbook in Tableau and Power BI. Contact your Cengage account executive for more information about MindTap. Instructor and Student Resources Additional instructor and student resources for this product are available online. Instructor assets include an Instructor’s Manual, Educator’s Guide, PowerPoint® slides, a Solutions and Answers Guide, and a test bank powered by Cognero®. Student assets include data sets. Sign up or sign in at www.cengage.com to search for and access this product and its online resources. ACKNOWLEDGMENTS We would like to acknowledge the work of reviewers who have provided comments and suggestions for improvement of this first edition of this text. Thanks to: Xiaohui Chang Oregon State University Wei Chen York College of Pennsylvania Anjee Gorkhali Susquehanna University Rita Kumar Cal Poly Pomona Barin Nag Towson University Andy Olstad Oregon State University Vivek Patil Gonzaga University Nolan Taylor Indiana University We are also indebted to the entire team at Cengage who worked on this title: Senior Prod- uct Manager, Aaron Arnsparger; Senior Content Manager, Conor Allen; Senior Learning Designer, Brandon Foltz; Digital Delivery Lead, Mark Hopkinson; Associate Subject-Matter Expert, Nancy Marchant; Content Program Manager, Jessica Galloway; Content Quality Assurance Engineer, Douglas Marks; and our Senior Project Manager at MPS Limited, Anubhav Kaushal, for their editorial counsel and support during the preparation of this text. The following Technical Content Developers worked on the MindTap content for this text: Anthony Bacon, Philip Bozarth, Sam Gallagher, Anna Geyer, Matthew Holmes, and Christopher Kurt. Our thanks to them as well. Jeffrey D. Camm James J. Cochran Michael J. Fry Jeffrey W. Ohlmann Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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Chapter 1 Introduction C o n t e n t s 1-1 ANALYTICS 1-4 DATA VISUALIZATION IN PRACTICE Accounting 1-2 WHY VISUALIZE DATA? Finance Data Visualization for Exploration Human Resource Management Data Visualization for Explanation Marketing Operations Engineering 1-3 TYPES OF DATA Sciences Quantitative and Categorical Data Sports Cross-Sectional and Time Series Data Big Data SUMMARY GLOSSARY PROBLEMS L e A R n I n G o B J e C t I V e s After completing this chapter, you will be able to Lo 1 Define analytics and describe the different types Lo 3 Describe various examples of data visualization of analytics used in practice Lo 2 Describe the different types of data and give Lo 4 Identify the various charts defined in this chapter an example of each Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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1-1 Analytics 3 You need a ride to a concert, so you select the Uber app on your phone. You enter the loca- tion of the concert. Your phone automatically knows your location and the app presents several options with prices. You select an option and confirm with your driver. You receive the driver’s name, license plate number, make and model of vehicle, and a photograph of the driver and the car. A map showing the location of the driver and the time remaining until arrival is updated in real time. Without even thinking about it, we continually use data to make decisions in our lives. How the data are displayed to us has a direct impact on how much effort we must expend to utilize the data. In the case of Uber, we enter data (our destination) and we are presented with data (prices) that allow us to make an informed decision. We see the result of our decision with an indication of the driver’s name, make and model of vehicle, and license plate number that makes us feel more secure. Rather than simply displaying the time until arrival, seeing the progress of the car on a map gives us some indication of the driver’s route. Watching the driver’s progress on the app removes some uncertainty and to some extent can divert our attention from how long we have been waiting. What data are pre- sented and how they are presented has an impact on our ability to understand the situation and make more-informed decisions. A weather map, an airplane seating chart, the dashboard of your car, a chart of the per- formance of the Dow Jones Industrial Average, your fitness tracker—all of these involve the visual display of data. Data visualization is the graphical representation of data and information using displays such as charts, graphs, and maps. Our ability to process infor- mation visually is strong. For example, numerical data that have been displayed in a chart, graph, or map allow us to more easily see relationships between variables in our data set. Trends, patterns, and the distributions of data are more easily comprehended when data are displayed visually. This book is about how to effectively display data to both discover and describe the information it contains data. We provide best practices in the design of visual displays of data, the effective use of color, and chart type selection. The goal of this book is to instruct you how to create effective data visualizations. Through the use of examples (using real data when possible), this book presents visualization principles and guidelines for gaining insight from data and conveying an impactful message to the audience. With the increased use of analytics in business, industry, science, engineering, and government, data visualization has increased dramatically in importance. We begin with a discussion of analytics and data visualization’s role in this rapidly growing field. 1-1 Analytics Analytics is the scientific process of transforming data into insights for making better decisions.1 Three developments have spurred the explosive growth in the use of analytics for improving decision making in all facets of our lives, including business, sports, science, medicine, and government: ●● Incredible amounts of data are produced by technological advances such as point- of-sale scanner technology; e-commerce and social networks; sensors on all kinds of mechanical devices such as aircraft engines, automobiles, thermometers, and farm machinery enabled by the so-called Internet of Things; and personal electronic devices such as cell phones. Businesses naturally want to use these data to improve the efficiency and profitability of their operations, better understand their customers, and price their products more effectively and competitively. Scientists and engineers use these data to invent new products, improve existing products, and make new basic discoveries about nature and human behavior. 1We adopt the definition of analytics developed by the Institute for Operations Research and the Management Sciences (INFORMS). Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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4 Chapter 1 Introduction ●● Ongoing research has resulted in numerous methodological developments, including advances in computational approaches to effectively handle and explore massive amounts of data as well as faster algorithms for data visualization, machine learning, optimization, and simulation. ●● The explosion in computing power and storage capability through better computing hardware, parallel computing, and cloud computing (the remote use of hardware and software over the internet) enable us to solve larger decision problems more quickly and more accurately than ever before. In summary, the availability of massive amounts of data, improvements in analytical meth- ods, and substantial increases in computing power and storage have enabled the explosive growth in analytics, data science, and artificial intelligence. Analytics can involve techniques as simple as reports or as complex as large-scale opti- mizations and simulations. Analytics is generally grouped into three broad categories of methods: descriptive, predictive, and prescriptive analytics. Descriptive analytics is the set of analytical tools that describe what has happened. This includes techniques such as data queries (requests for information with certain charac- teristics from a database), reports, descriptive or summary statistics, and data visualization. Descriptive data mining techniques such as cluster analysis (grouping data points with similar characteristics) also fall into this category. In general, these techniques summarize existing data or the output from predictive or prescriptive analyses. Predictive analytics consists of techniques that use mathematical models constructed from past data to predict future events or better understand the relationships between vari- ables. Techniques in this category include regression analysis, time series forecasting, computer simulation, and predictive data mining. As an example of a predictive model, past weather data are used to build mathematical models that forecast future weather. Likewise, past sales data can be used to predict future sales for seasonal products such as snowblow- ers, winter coats, and bathing suits. Prescriptive analytics are mathematical or logical models that suggest a decision or course of action. This category includes mathematical optimization models, decision analysis, and heuristic or rule-based systems. For example, solutions to supply network optimization models provide insights into the quantities of a company’s various products that should be manufactured at each plant, how much should be shipped to each of the company’s distribution centers, and which distribution center should serve each customer to minimize cost and meet service constraints. Data visualization is mission-critical to the success of all three types of analytics. We discuss this in more detail with examples in the next section. 1-2 Why Visualize Data? We create data visualizations for two reasons: exploring data and communicating/explaining a message. Let us discuss these uses of data visualization in more detail, examine the differences in the two uses, and consider how they relate to the types of analytics previously described. Data Visualization for exploration Data visualization is a powerful tool for exploring data to more easily identify patterns, recognize anomalies or irregularities in the data, and better understand the relationships between variables. Our ability to spot these types of characteristics of data is much stronger and quicker when we look at a visual display of the data rather than a simple listing. As an example of data visualization for exploration, let us consider the zoo attendance In chapter 2, we introduce a data shown in Table 1.1 and Figure 1.1. These data on monthly attendance to a zoo can be variety of different chart types found in the file Zoo. Comparing Table 1.1 and Figure 1.1, observe that the pattern in the data and how to construct charts is more detectable in the column chart of Figure 1.1 than in a table of numbers. A column in Excel. chart shows numerical data by the height of the column for a variety of categories or time periods. In the case of Figure 1.1, the time periods are the different months of the year. Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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