Muutke küpsiste eelistusi

Business Analytics 4th edition [Kõva köide]

(University of Cincinnati), (University of Alabama), (Wake Forest University), (University of Iowa)
  • Formaat: Hardback, 816 pages, kõrgus x laius x paksus: 38x215x281 mm, kaal: 1973 g
  • Ilmumisaeg: 10-Mar-2020
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 0357131789
  • ISBN-13: 9780357131787
Teised raamatud teemal:
  • Formaat: Hardback, 816 pages, kõrgus x laius x paksus: 38x215x281 mm, kaal: 1973 g
  • Ilmumisaeg: 10-Mar-2020
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 0357131789
  • ISBN-13: 9780357131787
Teised raamatud teemal:
Develop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, 4E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals illustrate data and results for each topic. Step-by-step instructions guide you through using Microsoft® Excel, Tableau, R, and JMP Pro software to perform even advanced analytics concepts. Practical, relevant problems at all levels of difficulty further help you apply what you've learned. This edition assists you in becoming proficient in topics beyond the traditional quantitative concepts, such as data visualization and data mining, which are increasingly important in today's analytical problem solving. MindTap digital learning resources with an interactive eBook, algorithmic practice problems with solutions and Exploring Analytics visualizations strengthen your understanding of key concepts.
About The Authors xvii
Preface xix
Chapter 1 Introduction
1(18)
1.1 Decision Making
3(1)
1.2 Business Analytics Defined
4(1)
1.3 A Categorization of Analytical Methods and Models
5(1)
Descriptive Analytics
5(1)
Predictive Analytics
5(1)
Prescriptive Analytics
6(1)
1.4 Big Data
6(4)
Volume
8(1)
Velocity
8(1)
Variety
8(1)
Veracity
8(2)
1.5 Business Analytics in Practice
10(3)
Financial Analytics
10(1)
Human Resource (HR) Analytics
11(1)
Marketing Analytics
11(1)
Health Care Analytics
11(1)
Supply Chain Analytics
12(1)
Analytics for Government and Nonprofits
12(1)
Sports Analytics
12(1)
Web Analytics
13(1)
1.6 Legal and Ethical Issues in the Use of Data and Analytics
13(3)
Summary
16(1)
Glossary
16(3)
Available in the MindTap Reader
Appendix: Getting Started with R and RStudio
Appendix: Basic Data Manipulation with R
Chapter 2 Descriptive Statistics
19(66)
2.1 Overview of Using Data: Definitions and Goals
20(2)
2.2 Types of Data
22(3)
Population and Sample Data
22(1)
Quantitative and Categorical Data
22(1)
Cross-Sectional and Time Series Data
22(1)
Sources of Data
22(3)
2.3 Modifying Data in Excel
25(5)
Sorting and Filtering Data in Excel
25(3)
Conditional Formatting of Data in Excel
28(2)
2.4 Creating Distributions from Data
30(10)
Frequency Distributions for Categorical Data
30(1)
Relative Frequency and Percent Frequency Distributions
31(1)
Frequency Distributions for Quantitative Data
32(3)
Histograms
35(3)
Cumulative Distributions
38(2)
2.5 Measures of Location
40(5)
Mean (Arithmetic Mean)
40(1)
Median
41(1)
Mode
42(1)
Geometric Mean
42(3)
2.6 Measures of Variability
45(3)
Range
45(1)
Variance
46(1)
Standard Deviation
47(1)
Coefficient of Variation
48(1)
2.7 Analyzing Distributions
48(8)
Percentiles
49(1)
Quartiles
50(1)
z-Scores
50(1)
Empirical Rule
51(2)
Identifying Outliers
53(1)
Boxplots
53(3)
2.8 Measures of Association Between Two Variables
56(6)
Scatter Charts
56(2)
Covariance
58(3)
Correlation Coefficient
61(1)
2.9 Data Cleansing
62(7)
Missing Data
62(2)
Blakely Tires
64(2)
Identification of Erroneous Outliers and Other Erroneous Values
66(2)
Variable Representation
68(1)
Summary
69(1)
Glossary
70(1)
Problems
71(10)
Case Problem 1 Heavenly Chocolates Web Site Transactions
81(1)
Case Problem 2 African Elephant Populations
82(3)
Available in the MindTap Reader
Appendix: Descriptive Statistics with R
Chapter 3 Data Visualization
85(72)
3.1 Overview of Data Visualization
88(3)
Effective Design Techniques
88(3)
3.2 Tables
91(11)
Table Design Principles
92(1)
Crosstabulation
93(3)
PivotTables in Excel
96(4)
Recommended PivotTables in Excel
100(2)
3.3 Charts
102(18)
Scatter Charts
102(2)
Recommended Charts in Excel
104(1)
Line Charts
105(4)
Bar Charts and Column Charts
109(1)
A Note on Pie Charts and Three-Dimensional Charts
110(2)
Bubble Charts
112(1)
Heat Maps
113(2)
Additional Charts for Multiple Variables
115(3)
PivotCharts in Excel
118(2)
3.4 Advanced Data Visualization
120(5)
Advanced Charts
120(3)
Geographic Information Systems Charts
123(2)
3.5 Data Dashboards
125(3)
Principles of Effective Data Dashboards
125(1)
Applications of Data Dashboards
126(2)
Summary
128(1)
Glossary
128(1)
Problems
129(10)
Case Problem 1 Pelican stores
139(1)
Case Problem 2 Movie Theater Releases
140(1)
Appendix: Data Visualization in Tableau
141(16)
Available in the MindTap Reader
Appendix: Creating Tabular and Graphical Presentations with R
Chapter 4 Probability: An Introduction to Modeling Uncertainty
157(56)
4.1 Events and Probabilities
159(1)
4.2 Some Basic Relationships of Probability
160(3)
Complement of an Event
160(1)
Addition Law
161(2)
4.3 Conditional Probability
163(8)
Independent Events
168(1)
Multiplication Law
168(1)
Bayes' Theorem
169(2)
4.4 Random Variables
171(2)
Discrete Random Variables
171(1)
Continuous Random Variables
172(1)
4.5 Discrete Probability Distributions
173(12)
Custom Discrete Probability Distribution
173(2)
Expected Value and Variance
175(3)
Discrete Uniform Probability Distribution
178(1)
Binomial Probability Distribution
179(3)
Poisson Probability Distribution
182(3)
4.6 Continuous Probability Distributions
185(13)
Uniform Probability Distribution
185(2)
Triangular Probability Distribution
187(2)
Normal Probability Distribution
189(5)
Exponential Probability Distribution
194(4)
Summary
198(1)
Glossary
198(2)
Problems
200(9)
Case Problem 1 Hamilton County Judges
209(1)
Case Problem 2 McNeil's Auto Mall
210(1)
Case Problem 3 Gebhardt Electronics
211(2)
Available in the MindTap Reader
Appendix: Discrete Probability Distributions with R
Appendix: Continuous Probability Distributions with R
Chapter 5 Descriptive Data Mining
213(40)
5.1 Cluster Analysis
215(11)
Measuring Distance Between Observations
215(3)
k-Means Clustering
218(3)
Hierarchical Clustering and Measuring Dissimilarity Between Clusters
221(4)
Hierarchical Clustering Versus k-Means Clustering
225(1)
5.2 Association Rules
226(3)
Evaluating Association Rules
228(1)
5.3 Text Mining
229(6)
Voice of the Customer at Triad Airline
229(2)
Preprocessing Text Data for Analysis
231(1)
Movie Reviews
232(2)
Computing Dissimilarity Between Documents
234(1)
Word Clouds
234(1)
Summary
235(1)
Glossary
235(2)
Problems
237(14)
Case Problem 1 Big Ten Expansion
251(1)
Case Problem 2 Know Thy Customer
251(2)
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
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(74)
6.1 Selecting a Sample
256(4)
Sampling from a Finite Population
256(1)
Sampling from an Infinite Population
257(3)
6.2 Point Estimation
260(2)
Practical Advice
262(1)
6.3 Sampling Distributions
262(11)
Sampling Distribution of x
265(5)
Sampling Distribution of p
270(3)
6.4 Interval Estimation
273(10)
Interval Estimation of the Population Mean
273(7)
Interval Estimation of the Population Proportion
280(3)
6.5 Hypothesis Tests
283(18)
Developing Null and Alternative Hypotheses
283(3)
Type I and Type II Errors
286(1)
Hypothesis Test of the Population Mean
287(11)
Hypothesis Test of the Population Proportion
298(3)
6.6 Big Data, Statistical Inference, and Practical Significance
301(9)
Sampling Error
301(1)
Nonsampling Error
302(1)
Big Data
303(1)
Understanding What Big Data Is
304(1)
Big Data and Sampling Error
305(1)
Big Data and the Precision of Confidence Intervals
306(1)
Implications of Big Data for Confidence Intervals
307(1)
Big Data, Hypothesis Testing, and p Values
308(2)
Implications of Big Data in Hypothesis Testing
310(1)
Summary
310(1)
Glossary
311(3)
Problems
314(10)
Case Problem 1 Young Professional Magazine
324(1)
Case Problem 2 Quality Associates, Inc.
325(2)
Available in the MindTap Reader
Appendix: Random Sampling with R
Appendix: Interval Estimation with R
Appendix: Hypothesis Testing with R
Chapter 7 Linear Regression
327(80)
7.1 Simple Linear Regression Model
329(2)
Regression Model
329(1)
Estimated Regression Equation
329(2)
7.2 Least Squares Method
331(6)
Least Squares Estimates of the Regression Parameters
333(2)
Using Excel's Chart Tools to Compute the Estimated Regression Equation
335(2)
7.3 Assessing the Fit of the Simple Linear Regression Model
337(4)
The Sums of Squares
337(2)
The Coefficient of Determination
339(1)
Using Excel's Chart Tools to Compute the Coefficient of Determination
340(1)
7.4 The Multiple Regression Model
341(5)
Regression Model
341(1)
Estimated Multiple Regression Equation
341(1)
Least Squares Method and Multiple Regression
342(1)
Butler Trucking Company and Multiple Regression
342(1)
Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation
343(3)
7.5 Inference and Regression
346(12)
Conditions Necessary for Valid Inference in the Least Squares Regression Model
347(4)
Testing Individual Regression Parameters
351(3)
Addressing Nonsignificant Independent Variables
354(1)
Multicollinearity
355(3)
7.6 Categorical Independent Variables
358(5)
Butler Trucking Company and Rush Hour
358(2)
Interpreting the Parameters
360(1)
More Complex Categorical Variables
361(2)
7.7 Modeling Nonlinear Relationships
363(12)
Quadratic Regression Models
364(4)
Piecewise Linear Regression Models
368(2)
Interaction Between Independent Variables
370(5)
7.8 Model Fitting
375(2)
Variable Selection Procedures
375(1)
Overfitting
376(1)
7.9 Big Data and Regression
377(5)
Inference and Very Large Samples
377(3)
Model Selection
380(2)
7.10 Prediction with Regression
382(2)
Summary
384(1)
Glossary
384(2)
Problems
386(16)
Case Problem 1 Alumni Giving
402(2)
Case Problem 2 Consumer Research, Inc.
404(1)
Case Problem 3 Predicting Winnings for NASCAR Drivers
405(2)
Available in the MindTap Reader
Appendix: Simple Linear Regression with R
Appendix: Multiple Linear Regression with R
Appendix: Regression Variable Selection Procedures with R
Chapter 8 Time Series Analysis and Forecasting
407(52)
8.1 Time Series Patterns
410(7)
Horizontal Pattern
410(2)
Trend Pattern
412(1)
Seasonal Pattern
413(1)
Trend and Seasonal Pattern
414(3)
Cyclical Pattern
417(1)
Identifying Time Series Patterns
417(1)
8.2 Forecast Accuracy
417(4)
8.3 Moving Averages and Exponential Smoothing
421(9)
Moving Averages
422(4)
Exponential Smoothing
426(4)
8.4 Using Regression Analysis for Forecasting
430(10)
Linear Trend Projection
430(2)
Seasonality Without Trend
432(1)
Seasonality with Trend
433(3)
Using Regression Analysis as a Causal Forecasting Method
436(3)
Combining Causal Variables with Trend and Seasonality Effects
439(1)
Considerations in Using Regression in Forecasting
440(1)
8.5 Determining the Best Forecasting Model to Use
440(1)
Summary
441(1)
Glossary
441(1)
Problems
442(8)
Case Problem 1 Forecasting Food and Beverage Sales
450(1)
Case Problem 2 Forecasting Lost Sales
450(2)
Appendix: Using the Excel Forecast Sheet
452(7)
Available in the MindTap Reader
Appendix: Forecasting with R
Chapter 9 Predictive Data Mining
459(50)
9.1 Data Sampling, Preparation, and Partitioning
461(3)
Static Holdout Method
461(1)
k-Fold Cross-Validation
462(1)
Class Imbalanced Data
463(1)
9.2 Performance Measures
464(7)
Evaluating the Classification of Categorical Outcomes
464(6)
Evaluating the Estimation of Continuous Outcomes
470(1)
9.3 Logistic Regression
471(4)
9.4 k-Nearest Neighbors
475(3)
Classifying Categorical Outcomes with k-Nearest Neighbors
475(2)
Estimating Continuous Outcomes with k-Nearest Neighbors
477(1)
9.5 Classification and Regression Trees
478(11)
Classifying Categorical Outcomes with a Classification Tree
478(5)
Estimating Continuous Outcomes with a Regression Tree
483(2)
Ensemble Methods
485(4)
Summary
489(2)
Glossary
491(1)
Problems
492(13)
Case Problem: Grey Code Corporation
505(4)
Available in the MindTap Reader
Appendix: Classification via Logistic Regression with R
Appendix: k-Nearest Neighbor Classification with R
Appendix: k-Nearest Neighbor Regression with R
Appendix: Individual Classification Trees with R
Appendix: Individual Regression Trees with R
Appendix: Random Forests of Classification Trees with R
Appendix: Random Forests of Regression Trees with R
Appendix: R/Rattle Settings to Solve
Chapter 9 Problems
Appendix: Data Partitioning with JMP Pro
Appendix: Classification via Logistic Regression with JMP Pro
Appendix: k-Nearest Neighbors Classification and Regression with JMP Pro
Appendix: Individual Classification and Regression Trees with JMP Pro
Appendix: Random Forests of Classification or Regression Trees with JMP Pro
Appendix: JMP Pro Settings to Solve
Chapter 9 Problems
Chapter 10 Spreadsheet Models
509(38)
10.1 Building Good Spreadsheet Models
511(5)
Influence Diagrams
511(1)
Building a Mathematical Model
511(2)
Spreadsheet Design and Implementing the Model in a Spreadsheet
513(3)
10.2 What-If Analysis
516(9)
Data Tables
516(2)
Goal Seek
518(2)
Scenario Manager
520(5)
10.3 Some Useful Excel Functions for Modeling
525(7)
Sum And Sumproduct
526(2)
IF and COUNTIF
528(2)
VLOOKUP
530(2)
10.4 Auditing Spreadsheet Models
532(4)
Trace Precedents and Dependents
532(1)
Show Formulas
532(2)
Evaluate Formulas
534(1)
Error Checking
534(1)
Watch Window
535(1)
10.5 Predictive and Prescriptive Spreadsheet Models
536(1)
Summary
537(1)
Glossary
537(1)
Problems
538(6)
Case Problem: Retirement Plan
544(3)
Chapter 11 Monte Carlo Simulation
547(62)
11.1 Risk Analysis for Sanotronics LLC
549(12)
Base-Case Scenario
549(1)
Worst-Case Scenario
550(1)
Best-Case Scenario
550(1)
Sanotronics Spreadsheet Model
550(1)
Use of Probability Distributions to Represent Random Variables
551(2)
Generating Values for Random Variables with Excel
553(4)
Executing Simulation Trials with Excel
557(1)
Measuring and Analyzing Simulation Output
557(4)
11.2 Inventory Policy Analysis for Promus Corp
561(7)
Spreadsheet Model for Promus
562(1)
Generating Values for Promus Corp's Demand
563(2)
Executing Simulation Trials and Analyzing Output
565(3)
11.3 Simulation Modeling for Land Shark Inc.
568(12)
Spreadsheet Model for Land Shark
569(1)
Generating Values for Land Shark's Random Variables
570(2)
Executing Simulation Trials and Analyzing Output
572(3)
Generating Bid Amounts with Fitted Distributions
575(5)
11.4 Simulation with Dependent Random Variables
580(5)
Spreadsheet Model for Press Teag Worldwide
580(5)
11.5 Simulation Considerations
585(1)
Verification and Validation
585(1)
Advantages and Disadvantages of Using Simulation
585(1)
Summary
586(1)
Summary of Steps for Conducting a Simulation Analysis
586(1)
Glossary
587(1)
Problems
587(13)
Case Problem: Four Corners
600(2)
Appendix: Common Probability Distributions for Simulation
602(7)
Chapter 12 Linear Optimization Models
609(54)
12.1 A Simple Maximization Problem
611(3)
Problem Formulation
612(2)
Mathematical Model for the Par, Inc. Problem
614(1)
12.2 Solving the Par, Inc. Problem
614(7)
The Geometry of the Par, Inc. Problem
615(2)
Solving Linear Programs with Excel Solver
617(4)
12.3 A Simple Minimization Problem
621(2)
Problem Formulation
621(1)
Solution for the M&D Chemicals Problem
621(2)
12.4 Special Cases of Linear Program Outcomes
623(5)
Alternative Optimal Solutions
624(1)
Infeasibility
625(1)
Unbounded
626(2)
12.5 Sensitivity Analysis
628(2)
Interpreting Excel Solver Sensitivity Report
628(2)
12.6 General Linear Programming Notation and More Examples
630(12)
Investment Portfolio Selection
631(2)
Transportation Planning
633(4)
Maximizing Banner Ad Revenue
637(5)
12.7 Generating an Alternative Optimal Solution for a Linear Program
642(2)
Summary
644(1)
Glossary
645(1)
Problems
646(14)
Case Problem: Investment Strategy
660(3)
Chapter 13 Integer Linear Optimization Models
663(40)
13.1 Types of Integer Linear Optimization Models
664(1)
13.2 Eastborne Realty, an Example of Integer Optimization
665(3)
The Geometry of Linear All-Integer Optimization
666(2)
13.3 Solving Integer Optimization Problems with Excel Solver
668(5)
A Cautionary Note About Sensitivity Analysis
671(2)
13.4 Applications Involving Binary Variables
673(10)
Capital Budgeting
673(2)
Fixed Cost
675(3)
Bank Location
678(2)
Product Design and Market Share Optimization
680(3)
13.5 Modeling Flexibility Provided by Binary Variables
683(2)
Multiple-Choice and Mutually Exclusive Constraints
683(1)
K Out of n Alternatives Constraint
684(1)
Conditional and Corequisite Constraints
684(1)
13.6 Generating Alternatives in Binary Optimization
685(2)
Summary
687(1)
Glossary
688(1)
Problems
689(12)
Case Problem: Applecore Children's Clothing
701(2)
Chapter 14 Nonlinear Optimization Models
703(34)
14.1 A Production Application: Par, Inc. Revisited
704(5)
An Unconstrained Problem
704(1)
A Constrained Problem
705(2)
Solving Nonlinear Optimization Models Using Excel Solver
707(1)
Sensitivity Analysis and Shadow Prices in Nonlinear Models
708(1)
14.2 Local and Global Optima
709(5)
Overcoming Local Optima with Excel Solver
712(2)
14.3 A Location Problem
714(1)
14.4 Markowitz Portfolio Model
715(5)
14.5 Adoption of a New Product: The Bass Forecasting Model
720(3)
Summary
723(1)
Glossary
724(1)
Problems
724(8)
Case Problem: Portfolio Optimization with Transaction Costs
732(5)
Chapter 15 Decision Analysis
737(46)
15.1 Problem Formulation
739(2)
Payoff Tables
740(1)
Decision Trees
740(1)
15.2 Decision Analysis Without Probabilities
741(3)
Optimistic Approach
741(1)
Conservative Approach
742(1)
Minimax Regret Approach
742(2)
15.3 Decision Analysis with Probabilities
744(4)
Expected Value Approach
744(2)
Risk Analysis
746(1)
Sensitivity Analysis
747(1)
15.4 Decision Analysis with Sample Information
748(6)
Expected Value of Sample Information
753(1)
Expected Value of Perfect Information
753(1)
15.5 Computing Branch Probabilities with Bayes' Theorem
754(3)
15.6 Utility Theory
757(10)
Utility and Decision Analysis
758(4)
Utility Functions
762(3)
Exponential Utility Function
765(2)
Summary
767(1)
Glossary
767(2)
Problems
769(11)
Case Problem: Property Purchase Strategy
780(3)
Multi-Chapter Case Problems Capital State University Game-Day Magazines 783(2)
Hanover Inc. 785(2)
Appendix A Basics of Excel 787(12)
Appendix B Database Basics with Microsoft Access 799(38)
Appendix C Solutions to Even-Numbered Problems (MindTap Reader)
References 837(2)
Index 839
Jeffrey D. Camm is the Inmar Presidential Chair of Analytics and Senior Associate Dean for Faculty 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, Dr. Camm served 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, The 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 recipient of the 2006 INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as a consultant to numerous companies and government agencies. Dr. Camm served as editor-in-chief of INFORMS Journal on Applied Analytics and is an INFORMS fellow. James J. Cochran is Professor of Applied Statistics, the Mike and Cathy Mouron Research Chair and Associate Dean for Faculty and Research at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. degrees from Wright State University and his Ph.D. from the University of Cincinnati. Dr. Cochran has served 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 his research in Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017 he received the American Statistical Associations Waller Distinguished Teaching Career Award and in 2018 he received the INFORMS Presidents Award. Dr. Cochran is an elected member of the International Statistics Institute, a fellow of the American Statistical Association and a fellow of INFORMS. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Dr. Cochran has organized and chaired teaching workshops throughout the world. Michael J. Fry is Professor of Operations, Business Analytics and Information Systems, Lindner Research Fellow and Managing 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 his M.S.E. and Ph.D. from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. He has also been a visiting professor at Cornell University and the University of British Columbia. Dr. Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IISE Transactions, Critical Care Medicine and INFORMS Journal on Applied Analytics. His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics and public-policy operations. He has worked with many organizations 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 Garden. Dr. Fry 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. 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 his M.S. and Ph.D. from the University of Michigan. He has been at the University of Iowa since 2003. Dr. Ohlmanns research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research and INFORMS Journal on Applied Analytics (formerly Interfaces). He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections as well as 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.