3P-EBK: BUSINESS ANALYTICS DATA ANALYSIS/DEC MAKING
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BUSINESS
ANALYTICS
Data Analysis and Decision Making
SEVENTH EDITION
S. Christian Al
ight
Kelly School of Business,
Indiana University, Emeritus
Wayne L. Winston
Kelly School of Business,
Indiana University, Emeritus
Australia • Brazil • Mexico • Singapore • United Kingdom • United States
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Business Analytics: Data Analysis and
Decision Making, 7e
S. Christian Al
ight and Wayne L. Winston
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To my wonderful wife Mary—my best friend and companion; and to Sam, Lindsay,
Teddy, and Archie S.C.A
To my wonderful family W.L.W.
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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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S. Christian Al
ight got his B.S. degree in Mathematics from Stanford in 1968 and his
PhD in Operations Research from Stanford in 1972. He taught in the Operations & Decision
Technologies Department in the Kelley School of Business at Indiana University (IU) for close
to 40 years, before retiring from teaching in 2011. While at IU, he taught courses in management
science, computer simulation, statistics, and computer programming to all levels of business
students, including undergraduates, MBAs, and doctoral students. In addition, he taught simula-
tion modeling at General Motors and Whirlpool, and he taught database analysis for the Army. He
published over 20 articles in leading operations research journals in the area of applied probabil-
ity, and he has authored the books Statistics for Business and Economics, Practical Management
Science, Spreadsheet Modeling and Applications, Data Analysis for Managers, and VBA for Mod-
elers. He worked for several years after “retirement” with the Palisade Corporation developing
training materials for its software products, he has developed a commercial version of his Excel®
tutorial, called ExcelNow!, and he continues to revise his textbooks.
On the personal side, Chris has been ma
ied for 47 years to his wonderful wife, Mary, who
etired several years ago after teaching 7th grade English for 30 years. They have one son, Sam,
who lives in Philadelphia with his wife Lindsay and their two sons, Teddy and Archie. Chris has
many interests outside the academic area. They include activities with his family, traveling with
Mary, going to cultural events, power walking while listening to books on his iPod, and reading.
And although he earns his livelihood from quantitative methods, his real passion is for playing
classical piano music.
Wayne L. Winston taught in the Operations & Decision Technologies Department in the Kelley
School of Business at Indiana University for close to 40 before retiring a few years ago. Wayne
eceived his B.S. degree in Mathematics from MIT and his PhD in Operations Research from
Yale. He has written the successful textbooks Operations Research: Applications and Algorithms,
Mathematical Programming: Applications and Algorithms, Simulation Modeling Using @RISK,
Practical Management Science, Data Analysis and Decision Making, Financial Models Using
Simulation and Optimization, and Mathletics. Wayne has published more than 20 articles in lead-
ing journals and has won many teaching awards, including the school-wide MBA award four
times. He has taught classes at Microsoft, GM, Ford, Eli Lilly, Bristol-Myers Squi
, Arthur
Andersen, Roche, PricewaterhouseCoopers, and NCR, and in “retirement,” he is cu
ently teach-
ing several courses at the University of Houston. His cu
ent interest is showing how spread-
sheet models can be used to solve business problems in all disciplines, particularly in finance and
marketing.
Wayne enjoys swimming and basketball, and his passion for trivia won him an appearance
several years ago on the television game show Jeopardy!, where he won two games. He is ma
ied
to the lovely and talented Vivian. They have two children, Gregory and Jennifer.
ABOUT THE AUTHORS
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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
Preface xvi
1 Introduction to Business Analytics 1
PART 1 Data Analysis 37
2 Describing the Distribution of a Variable 38
3 Finding Relationships among Variables 84
4 Business Intelligence (BI) Tools for Data Analysis 132
PART 2 Probability and Decision Making under Uncertainty 183
5 Probability and Probability Distributions 184
6 Decision Making under Uncertainty 242
PART 3 Statistical Inference 293
7 Sampling and Sampling Distributions 294
8 Confidence Interval Estimation 323
9 Hypothesis Testing 368
PART 4 Regression Analysis and Time Series Forecasting 411
10 Regression Analysis: Estimating Relationships 412
11 Regression Analysis: Statistical Inference 472
12 Time Series Analysis and Forecasting 523
PART 5 Optimization and Simulation Modeling 575
13 Introduction to Optimization Modeling 576
14 Optimization Models 630
15 Introduction to Simulation Modeling 717
16 Simulation Models 779
PART 6 Advanced Data Analysis 837
17 Data Mining 838
18 Analysis of Variance and Experimental Design (MindTap Reader only)
19 Statistical Process Control (MindTap Reader only)
APPENDIX A: Quantitative Reporting (MindTap Reader only)
References 873
Index 875
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CONTENTS
Preface xvi
1 Introduction to Business Analytics 1
1-1 Introduction 3
1-2 Overview of the Book 4
1-2a The Methods 4
1-2b The Software 6
1-3 Introduction to Spreadsheet Modeling 8
1-3a Basic Spreadsheet Modeling: Concepts and Best Practices 9
1-3b Cost Projections 12
1-3c Breakeven Analysis 15
1-3d Ordering with Quantity Discounts and Demand Uncertainty 20
1-3e Estimating the Relationship between Price and Demand 24
1-3f Decisions Involving the Time Value of Money 29
1-4 Conclusion 33
PART 1 Data Analysis 37
2 Describing the Distribution of a Variable 38
2-1 Introduction 39
2-2 Basic Concepts 41
2-2a Populations and Samples 41
2-2b Data Sets, Variables, and Observations 41
2-2c Data Types 42
2-3 Summarizing Categorical Variables 45
2-4 Summarizing Numeric Variables 49
2-4a Numeric Summary Measures 49
2-4b Charts for Numeric Variables 57
2-5 Time Series Data 62
2-6 Outliers and Missing Values 69
2-7 Excel Tables for Filtering, Sorting, and Summarizing 71
2-8 Conclusion 77
Appendix: Introduction to StatTools 83
3 Finding Relationships among Variables 84
3-1 Introduction 85
3-2 Relationships among Categorical Variables 86
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C O N T E N T S v i i
3-3 Relationships among Categorical Variables
and a Numeric Variable 89
3-4 Relationships among Numeric Variables 96
3-4a Scatterplots 96
3-4b Co
elation and Covariance 101
3-5 Pivot Tables 106
3-6 Conclusion 126
Appendix: Using StatTools to Find Relationships 131
4 Business Intelligence (BI) Tools for Data Analysis 132
4-1 Introduction 133
4-2 Importing Data into Excel with Power Query 134
4-2a Introduction to Relational Databases 134
4-2b Excel’s Data Model 139
4-2c Creating and Editing Queries 146
4-3 Data Analysis with Power Pivot 152
4-3a Basing Pivot Tables on a Data Model 154
4-3b Calculated Columns, Measures, and the DAX Language 154
4-4 Data Visualization with Tableau Public 162
4-5 Data Cleansing 172
4-6 Conclusion 178
PART 2 Probability and Decision Making under Uncertainty 183