Business Analytics Descriptive Predictive Prescriptive Jeffrey D. Cochran Wake Forest University University of Alabama Michael J. Ohlmann University of Cincinnati University of Iowa Australia ● Brazil ● Mexico ● Singapore ● United Kingdom ● United States Copyright 2021 Cengage Learning. All Rights Reserved.
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Business Analytics, Fourth Edition © 2021, 2019 Cengage Learning, Inc. Cochran, WCN: 02-300 Michael J. Ohlmann Unless otherwise noted, all content is © Cengage. Senior Vice President, Higher Education & Skills ALL RIGHTS RESERVED.
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Printed in the United States of America Print Number: 01 Print Year: 2020 Copyright 2021 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. Brief Contents ABOUT THE AUTHORS xvii PREFACE xix Chapter 1 Introduction 1 Chapter 2 Descriptive Statistics 19 Chapter 3 Data Visualization 85 Chapter 4 Probability: An Introduction to Modeling Uncertainty 157 Chapter 5 Descriptive Data Mining 213 Chapter 6 Statistical Inference 253 Chapter 7 Linear Regression 327 Chapter 8 Time Series Analysis and Forecasting 407 Chapter 9 Predictive Data Mining 459 Chapter 10 Spreadsheet Models 509 Chapter 11 Monte Carlo Simulation 547 Chapter 12 Linear Optimization Models 609 Chapter 13 Integer Linear Optimization Models 663 Chapter 14 Nonlinear Optimization Models 703 Chapter 15 Decision Analysis 737 ulti-Chapter Case Problems M Capital State University Game-Day Magazines 783 Hanover Inc. 785 Appendix A Basics of Excel 787 Appendix B Database Basics with Microsoft Access 799 Appendix C Solutions to Even-Numbered Problems (MindTap Reader) References 837 Index 839 Copyright 2021 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. Copyright 2021 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. Contents ABOUT THE AUTHORS xvii PREFACE xix Chapter 1 Introduction 1 1.2 Business Analytics Defined 4 1.3 A Categorization of Analytical Methods and Models 5 Descriptive Analytics 5 Predictive Analytics 5 Prescriptive Analytics 6 1.4 Big Data 6 Volume 8 Velocity 8 Variety 8 Veracity 8 1.5 Business Analytics in Practice 10 Financial Analytics 10 Human Resource (HR) Analytics 11 Marketing Analytics 11 Health Care Analytics 11 Supply Chain Analytics 12 Analytics for Government and Nonprofits 12 Sports Analytics 12 Web Analytics 13 1.6 Legal and Ethical Issues in the Use of Data and Analytics 13 Summary 16 Glossary 16 Available in the MindTap Reader: Appendix: Getting Started with R and RStudio Appendix: Basic Data Manipulation with R Chapter 2 Descriptive Statistics 19 2.1 Overview of Using Data: Definitions and Goals 20 2.2 Types of Data 22 Population and Sample Data 22 Quantitative and Categorical Data 22 Cross-Sectional and Time Series Data 22 Sources of Data 22 2.3 Modifying Data in Excel 25 Sorting and Filtering Data in Excel 25 Conditional Formatting of Data in Excel 28 Copyright 2021 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.4 Creating Distributions from Data 30 Frequency Distributions for Categorical Data 30 Relative Frequency and Percent Frequency Distributions 31 Frequency Distributions for Quantitative Data 32 Histograms 35 Cumulative Distributions 38 2.5 Measures of Location 40 Mean (Arithmetic Mean) 40 Median 41 Mode 42 Geometric Mean 42 2.6 Measures of Variability 45 Range 45 Variance 46 Standard Deviation 47 Coefficient of Variation 48 2.7 Analyzing Distributions 48 Percentiles 49 Quartiles 50 z-Scores 50 Empirical Rule 51 Identifying Outliers 53 Boxplots 53 2.8 Measures of Association Between Two Variables 56 Scatter Charts 56 Covariance 58 Correlation Coefficient 61 2.9 Data Cleansing 62 Missing Data 62 Blakely Tires 64 Identification of Erroneous Outliers and Other Erroneous Values 66 Variable Representation 68 Summary 69 Glossary 70 Problems 71 Case Problem 1: Heavenly Chocolates Web Site Transactions 81 Case Problem 2: African Elephant Populations 82 Available in the MindTap Reader: Appendix: Descriptive Statistics with R Chapter 3 Data Visualization 85 3.1 Overview of Data Visualization 88 Effective Design Techniques 88 3.2 Tables 91 Table Design Principles 92 Crosstabulation 93 Copyright 2021 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. Contents vii PivotTables in Excel 96 Recommended PivotTables in Excel 100 3.3 Charts 102 Scatter Charts 102 Recommended Charts in Excel 104 Line Charts 105 Bar Charts and Column Charts 109 A Note on Pie Charts and Three-Dimensional Charts 110 Bubble Charts 112 Heat Maps 113 Additional Charts for Multiple Variables 115 PivotCharts in Excel 118 3.4 Advanced Data Visualization 120 Advanced Charts 120 Geographic Information Systems Charts 123 3.5 Data Dashboards 125 Principles of Effective Data Dashboards 125 Applications of Data Dashboards 126 Summary 128 Glossary 128 Problems 129 Case Problem 1: Pelican stores 139 Case Problem 2: Movie Theater Releases 140 Appendix: Data Visualization in Tableau 141 Available in the MindTap Reader: Appendix: Creating Tabular and Graphical Presentations with R Chapter 4 Probability: An Introduction to Modeling Uncertainty 157 4.1 Events and Probabilities 159 4.2 Some Basic Relationships of Probability 160 Complement of an Event 160 Addition Law 161 4.3 Conditional Probability 163 Independent Events 168 Multiplication Law 168 Bayes’ Theorem 169 4.4 Random Variables 171 Discrete Random Variables 171 Continuous Random Variables 172 4.5 Discrete Probability Distributions 173 Custom Discrete Probability Distribution 173 Expected Value and Variance 175 Discrete Uniform Probability Distribution 178 Binomial Probability Distribution 179 Poisson Probability Distribution 182 Copyright 2021 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.6 Continuous Probability Distributions 185 Uniform Probability Distribution 185 Triangular Probability Distribution 187 Normal Probability Distribution 189 Exponential Probability Distribution 194 Summary 198 Glossary 198 Problems 200 Case Problem 1: Hamilton County Judges 209 Case Problem 2: McNeil’s Auto Mall 210 Case Problem 3: Gebhardt Electronics 211 Available in the MindTap Reader: Appendix: Discrete Probability Distributions with R Appendix: Continuous Probability Distributions with R Chapter 5 Descriptive Data Mining 213 5.1 Cluster Analysis 215 Measuring Distance Between Observations 215 k-Means Clustering 218 Hierarchical Clustering and Measuring Dissimilarity Between Clusters 221 Hierarchical Clustering Versus k-Means Clustering 225 5.2 Association Rules 226 Evaluating Association Rules 228 5.3 Text Mining 229 Voice of the Customer at Triad Airline 229 Preprocessing Text Data for Analysis 231 Movie Reviews 232 Computing Dissimilarity Between Documents 234 Word Clouds 234 Summary 235 Glossary 235 Problems 237 Case Problem 1: Big Ten Expansion 251 Case Problem 2: Know Thy Customer 251 Available in the MindTap Reader: Appendix: Getting Started with Rattle in R Appendix: k-Means Clustering with R Appendix: Hierarchical Clustering with R Appendix: Association Rules with R Appendix: Text Mining with R Appendix: R/Rattle Settings to Solve Chapter 5 Problems Appendix: Opening and Saving Excel Files in JMP Pro Appendix: Hierarchical Clustering with JMP Pro Copyright 2021 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. Contents ix Appendix: k-Means Clustering with JMP Pro Appendix: Association Rules with JMP Pro Appendix: Text Mining with JMP Pro Appendix: JMP Pro Settings to Solve Chapter 5 Problems Chapter 6 Statistical Inference 253 6.1 Selecting a Sample 256 Sampling from a Finite Population 256 Sampling from an Infinite Population 257 6.
