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Essentials of Statistics for Business and Economics 10th edition [Minkštas viršelis]

(University of Cincinnati), (Wake Forest University), (University of Cincinnati), (University of Alabama), (Rochester Institute of Technology), (University of Iowa), (University of Cincinnati)
  • Formatas: Paperback / softback, 756 pages, aukštis x plotis x storis: 33x210x266 mm, weight: 1837 g
  • Išleidimo metai: 01-Jan-2023
  • Leidėjas: South-Western College Publishing
  • ISBN-10: 0357716019
  • ISBN-13: 9780357716014
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 756 pages, aukštis x plotis x storis: 33x210x266 mm, weight: 1837 g
  • Išleidimo metai: 01-Jan-2023
  • Leidėjas: South-Western College Publishing
  • ISBN-10: 0357716019
  • ISBN-13: 9780357716014
Kitos knygos pagal šią temą:
Learn how statistical information impacts decisions in todays business world as Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' leading ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 10E connects key concepts in each chapter to actual business practices. This edition combines clear statistical methods with a proven approach that presents a problem, then scenario. Updated applications reflect the latest developments in business and statistics. You work with more than 350 new and updated business examples, approximately 50 new and updated cases and hands-on exercises that highlight statistics in action. You also gain practice using leading professional statistical software with exercises and appendices that walk you through using Excel®, R and JMP® Student Edition. Digital resources in WebAssign are also available to help you strengthen your understanding of today's most important business statistics concepts.
About the Authors xvii
Preface xxi
Chapter 1 Data and Statistics
1(32)
Statistics in Practice: Bloomberg Businessweek
2(1)
1.1 Applications in Business and Economics
3(2)
Accounting
3(1)
Finance
3(1)
Marketing
4(1)
Production
4(1)
Economics
4(1)
Information Systems
4(1)
1.2 Data
5(5)
Elements, Variables, and Observations
5(1)
Scales of Measurement
5(2)
Categorical and Quantitative Data
7(1)
Cross-Sectional and Time Series Data
8(2)
1.3 Data Sources
10(3)
Existing Sources
10(1)
Observational Study
11(1)
Experiment
12(1)
Time and Cost Issues
13(1)
Data Acquisition Errors
13(1)
1.4 Descriptive Statistics
13(2)
1.5 Statistical Inference
15(1)
1.6 Analytics
16(1)
1.7 Big Data and Data Mining
17(2)
1.8 Computers and Statistical Analysis
19(1)
1.9 Ethical Guidelines for Statistical Practice
19(2)
Summary
21(1)
Glossary
21(1)
Supplementary Exercises
22(8)
Appendix 1.1 Opening and Saving DATA Files and Converting to Stacked form with JMP
30(3)
Available in the Cengage eBook
Appendix: Getting Started with R and RStudio
Appendix: Basic Data Manipulation with R
Chapter 2 Descriptive Statistics: Tabular and Graphical Displays
33(74)
Statistics in Practice: Colgate-Palmolive Company
34(1)
2.1 Summarizing Data for a Categorical Variable
35(7)
Frequency Distribution
35(1)
Relative Frequency and Percent Frequency Distributions
36(1)
Bar Charts and Pie Charts
37(5)
2.2 Summarizing Data for a Quantitative Variable
42(15)
Frequency Distribution
42(2)
Relative Frequency and Percent Frequency Distributions
44(1)
Dot Plot
45(1)
Histogram
45(2)
Cumulative Distributions
47(1)
Stem-and-Leaf Display
47(10)
2.3 Summarizing Data for Two Variables Using Tables
57(8)
Crosstabulation
57(2)
Simpson's Paradox
59(6)
2.4 Summarizing Data for Two Variables Using Graphical Displays
65(6)
Scatter Diagram and Trendline
65(1)
Side-by-Side and Stacked Bar Charts
66(5)
2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays
71(6)
Creating Effective Graphical Displays
71(1)
Choosing the Type of Graphical Display
72(1)
Data Dashboards
73(2)
Data Visualization in Practice: Cincinnati Zoo and Botanical Garden
75(2)
Summary
77(1)
Glossary
78(1)
Key Formulas
79(1)
Supplementary Exercises
80(5)
Case Problem 1 Pelican Stores
85(1)
Case Problem 2 Movie Theater Releases
86(1)
Case Problem 3 Queen City
87(1)
Case Problem 4 Cut-Rate Machining, Inc.
88(2)
Appendix 2.1 Creating Tabular and Graphical Presentations with JMP
90(3)
Appendix 2.2 Creating Tabular and Graphical Presentations with Excel
93(14)
Available in the Cengage eBook
Appendix: Creating Tabular and Graphical Presentations with R
Chapter 3 Descriptive Statistics: Numerical Measures
107(70)
Statistics in Practice: Small Fry Design
108(1)
3.1 Measures of Location
109(13)
Mean
109(2)
Weighted Mean
111(1)
Median
112(1)
Geometric Mean
113(2)
Mode
115(1)
Percentiles
115(1)
Quartiles
116(6)
3.2 Measures of Variability
122(7)
Range
123(1)
Interquartile Range
123(1)
Variance
123(2)
Standard Deviation
125(1)
Coefficient of Variation
126(3)
3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
129(8)
Distribution Shape
129(1)
Z-Scores
130(1)
Chebyshev's Theorem
131(1)
Empirical Rule
132(2)
Detecting Outliers
134(3)
3.4 Five-Number Summaries and Boxplots
137(5)
Five-Number Summary
138(1)
Boxplot
138(1)
Comparative Analysis Using Boxplots
139(3)
3.5 Measures of Association Between Two Variables
142(8)
Covariance
142(2)
Interpretation of the Covariance
144(2)
Correlation Coefficient
146(1)
Interpretation of the Correlation Coefficient
147(3)
3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness
150(3)
Summary
153(1)
Glossary
154(1)
Key Formulas
155(1)
Supplementary Exercises
156(6)
Case Problem 1 Pelican Stores
162(1)
Case Problem 2 Movie Theater Releases
163(1)
Case Problem 3 Business Schools of Asia-Pacific
164(1)
Case Problem 4 Heavenly Chocolates Website Transactions
164(2)
Case Problem 5 African Elephant Populations
166(2)
Appendix 3.1 Descriptive Statistics with JMP
168(3)
Appendix 3.2 Descriptive Statistics with Excel
171(6)
Available in the Cengage eBook
Appendix: Descriptive Statistics with R
Chapter 4 Introduction to Probability
177(46)
Statistics in Practice: National Aeronautics and Space Administration
178(1)
4.1 Random Experiments, Counting Rules, and Assigning Probabilities
179(10)
Counting Rules, Combinations, and Permutations
180(4)
Assigning Probabilities
184(1)
Probabilities for the KP&L Project
185(4)
4.2 Events and Their Probabilities
189(4)
4.3 Some Basic Relationships of Probability
193(6)
Complement of an Event
193(1)
Addition Law
194(5)
4.4 Conditional Probability
199(8)
Independent Events
202(1)
Multiplication Law
202(5)
4.5 Bayes `Theorem'
207(6)
Tabular Approach
210(3)
Summary
213(1)
Glossary
213(1)
Key Formulas
214(1)
Supplementary Exercises
215(5)
Case Problem 1 Hamilton County Judges
220(1)
Case Problem 2 Rob's Market
220(3)
Chapter 5 Discrete Probability Distributions
223(58)
Statistics in Practice: 5.1 Random Variables
225(3)
Discrete Random Variables
225(1)
Continuous Random Variables
225(3)
5.2 Developing Discrete Probability Distributions
228(5)
5.3 Expected Value and Variance
233(5)
Expected Value
233(1)
Variance
233(5)
5.4 Bivariate Distributions, Covariance, and Financial Portfolios
238(6)
A Bivariate Empirical Discrete Probability Distribution
238(3)
Financial Applications
241(3)
Summary
244(3)
5.5 Binomial Probability Distribution
247(11)
A Binomial Experiment
248(1)
Martin Clothing Store Problem
249(4)
Using Tables of Binomial Probabilities
253(1)
Expected Value and Variance for the Binomial Distribution
254(4)
5.6 Poisson Probability Distribution
258(4)
An Example Involving Time Intervals
259(1)
An Example Involving Length or Distance Intervals
260(2)
5.7 Hypergeometric Probability Distribution
262(3)
Summary
265(1)
Glossary
266(1)
Key Formulas
266(2)
Supplementary Exercises
268(4)
Case Problem 1 Go Bananas. Breakfast Cereal
272(1)
Case Problem 2 McNeil's Auto Mall
273(1)
Case Problem 3 Grievance Committee at Tuglar Corporation
273(2)
Appendix 5.1 Discrete Probability Distributions with JMP
275(3)
Appendix 5.2 Discrete Probability Distributions with Excel
278(3)
Available in the Cengage eBook
Appendix: Discrete Probability Distributions with R
Chapter 6 Continuous Probability Distributions
281(38)
Statistics in Practice: Procter & Gamble
282(1)
6.1 Uniform Probability Distribution
283(4)
Area as a Measure of Probability
284(3)
6.2 Normal Probability Distribution
287(12)
Normal Curve
287(2)
Standard Normal Probability Distribution
289(5)
Computing Probabilitiesfor Any Normal Probability Distribution
294(1)
Grear Tire Company Problem
294(5)
6.3 Normal Approximation of Binomial Probabilities
299(3)
6.4 Exponential Probability Distribution
302(3)
Computing Probabilities for the Exponential Distribution
303(1)
Relationship Between the Poisson and Exponential Distributions
303(2)
Summary
305(1)
Glossary
306(1)
Key Formulas
306(1)
Supplementary Exercises
306(4)
Case Problem 1 Specialty Toys
310(1)
Case Problem 2 Gebhardt Electronics
311(1)
Appendix 6.1 Continuous Probability Distributions with JMP
312(6)
Appendix 6.2 Continuous Probability Distributions with Excel
318(1)
Available in the Cengage eBook
Appendix: Continuous Probability Distributions with R
Chapter 7 Sampling and Sampling Distributions
319(56)
Statistics in Practice: The Food and Agriculture Organization
320(2)
7.1 The Electronics Associates Sampling Problem
322(1)
7.2 Selecting a Sample
322(5)
Sampling from a Finite Population
322(2)
Sampling from an Infinite Population
324(3)
7.3 Point Estimation
327(4)
Practical Advice
329(2)
7.4 Introduction to Sampling Distributions
331(2)
7.5 Sampling Distribution of x
333(10)
Expected Value of x
334(1)
Standard Deviation of x
334(1)
Form of the Sampling Distribution of x
335(2)
Sampling Distribution of x for the EAI Problem
337(1)
Practical Value of the Sampling Distribution of x
338(1)
Relationship Between the Sample Size and the Sampling Distribution of x
339(4)
7.6 Sampling Distribution of p
343(6)
Expected Value of p
344(1)
Standard Deviation of p
344(1)
Form of the Sampling Distribution of p
345(1)
Practical Value of the Sampling Distribution of p
345(4)
7.7 Properties of Point Estimators
349(2)
Unbiased
349(1)
Efficiency
350(1)
Consistency
351(1)
7.8 Other Sampling Methods
351(3)
Stratified Random Sampling
352(1)
Cluster Sampling
352(1)
Systematic Sampling
353(1)
Convenience Sampling
353(1)
Judgment Sampling
354(1)
7.9 Big Data and Standard Errors of Sampling Distributions
354(6)
Sampling Error
354(1)
Nonsampling Error
355(1)
Big Data
356(1)
Understanding What Big Data Is
356(1)
Implications of Big Data for Sampling Error
357(3)
Summary
360(1)
Glossary
361(1)
Key Formulas
362(1)
Supplementary Exercises
363(3)
Case Problem 1 Marion Dairies
366(1)
Case Problem 2 Profitability of Small Restaurants
367(1)
Appendix 7.1 The Expected Value and Standard Deviation of x
368(1)
Appendix 7.2 Random Sampling with JMP
369(3)
Appendix 7.3 Random Sampling with Excel
372(3)
Available in the Cengage eBook
Appendix: Random Sampling with R
Chapter 8 Interval Estimation
375(46)
Statistics in Practice: Food Lion
376(1)
8.1 Population Mean: cr Known
377(6)
Margin of Error and the Interval Estimate
377(4)
Practical Advice
381(2)
8.2 Population Mean: a Unknown
383(5)
Margin of Error and the Interval Estimate
384(3)
Practical Advice
387(1)
Using a Small Sample
387(1)
Summary of Interval Estimation Procedures
388(4)
8.3 Determining the Sample Size
392(3)
8.4 Population Proportion
395(5)
Determining the Sample Size
396(4)
8.5 Big Data and Confidence Intervals
400(3)
Big Data and the Precision of Confidence Intervals
400(1)
Implications of Big Data for Confidence Intervals
401(2)
Summary
403(1)
Glossary
404(1)
Key Formulas
404(1)
Supplementary Exercises
405(4)
Supplementary Case Problem 1: Young Professional Magazine
408(1)
Case Problem 2 Gulf Real Estate Properties
409(2)
Case Problem 3 Garza Research, Inc.
411(1)
Case Problem 4 Go-Fer Meal Delivery Service
412(1)
Appendix 8.1 Interval Estimation with JMP
413(4)
Appendix 8.2 Interval Estimation with Excel
417(4)
Available in the Cengage eBook
Appendix: Interval Estimation of a Population Mean, Known Standard Deviation with R
Appendix: Interval Estimation of a Population Mean, Unknown Standard Deviation with R
Appendix: Interval Estimation of a Population Proportion with R
Chapter 9 Hypothesis Tests
421(66)
Statistics in Practice: John Morrell & Company
422(1)
9.1 Developing Null and Alternative Hypotheses
423(2)
The Alternative Hypothesis as a Research Hypothesis
423(1)
The Null Hypothesis as an Assumption to Be Challenged
424(1)
Summary of Forms for Null and Alternative Hypotheses
425(1)
9.2 Type I and Type II Errors
426(3)
9.3 Population Mean: a Known
429(5)
One-Tailed Test
429(5)
Summary
434(3)
Two-Tailed Test
434(3)
Summary and Practical Advice
437(6)
Relationship Between Interval Estimation and Hypothesis Testing
438(5)
9.4 Population Mean: cr Unknown
443(2)
One-Tailed Test
443(1)
Two-Tailed Test
444(1)
Summary and Practical Advice
445(4)
9.5 Population Proportion
449(2)
Summary
451(3)
9.6 Hypothesis Testing and Decision Making
454(1)
9.7 Calculating the Probability of Type II Errors
454(5)
9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean
459(4)
9.9 Big Data and Hypothesis Testing
463(3)
Big Data, Hypothesis Testing, and p Values
463(1)
Implications of Big Data in Hypothesis Testing
464(2)
Summary
466(1)
Glossary
466(1)
Key Formulas
467(1)
Supplementary Exercises
467(4)
Case Problem 1 Quality Associates, Inc.
471(2)
Case Problem 2 Ethical Behavior of Students at Bayview University
473(2)
Appendix 9.1 Hypothesis Testing with JMP
475(6)
Appendix 9.2 Hypothesis Testing with Excel
481(6)
Available in the Cengage eBook
Appendix: Hypothesis Testing of a Population Mean, Known Standard Deviation with R
Appendix: Hypothesis Testing of a Population Mean, Unknown Standard Deviation with R
Appendix: Hypothesis Testing of a Population Proportion with R
Chapter 10 Inference About Means and Proportions with Two Populations
487(44)
Statistics in Practice: U.S. Food and Drug Administration
488(1)
10.1 Inferences About the Difference Between Two Population Means: σ1 and σ2 Known
489(6)
Interval Estimation of μ1 - μ2
489(2)
Hypothesis Tests About μ1 - μ2
491(2)
Practical Advice
493(2)
10.2 Inferences About the Difference Between Two Population Means: σ1, and σ2 Unknown
495(8)
Interval Estimation of μ1 - μ2
496(1)
Hypothesis Tests About μ1 - μ2
497(2)
Practical Advice
499(4)
10.3 Inferences About the Difference Between Two Population Means: Matched Samples
503(6)
10.4 Inferences About the Difference Between Two Population Proportions
509(6)
Interval Estimation of p1 -- p2
509(2)
Hypothesis Tests About p1 -- p2
511(4)
Summary
515(1)
Glossary
515(1)
Key Formulas
515(2)
Supplementary Exercises
517(3)
Case Problem: Par, Inc.
520(1)
Appendix 10.1 Inferences About Two Populations with JMP
521(5)
Appendix 10.2 Inferences About Two Populations with Excel
526(5)
Available in the Cengage eBook
Appendix: Inferences About Two Populations with R
Chapter 11 Inferences About Population Variances
531(28)
Statistics in Practice: U.S. Government Accountability Office
532(1)
11.1 Inferences About a Population Variance
533(10)
Interval Estimation
533(4)
Hypothesis Testing
537(6)
11.2 Inferences About Two Population Variances
543(7)
Summary
550(1)
Key Formulas
550(1)
Supplementary Exercises
550(2)
Case Problem 1 Air Force Training Program
552(1)
Case Problem 2 Meticulous Drill & Reamer
553(2)
Appendix 11.1 Population Variances with JMP
555(2)
Appendix 11.2 Population Variances with Excel
557(2)
Available in the Cengage eBook
Appendix: Population Variances with R
Chapter 12 Comparing Multiple Proportions, Test of Independence, and Goodness of Fit
559(44)
Statistics in Practice: United Way
560(1)
12.1 Testing the Equality of Population Proportions for Three or More Populations
561(10)
A Multiple Comparison Procedure
566(5)
12.2 Test of Independence
571(8)
12.3 Goodness of Fit Test
579(9)
Multinomial Probability Distribution
579(3)
Normal Probability Distribution
582(6)
Summary
588(1)
Glossary
588(1)
Key Formulas
589(1)
Supplementary Exercises
589(4)
Case Problem 1 A Bipartisan Agenda for Change
593(1)
Case Problem 2 Fuentes Salty Snacks, Inc.
594(1)
Case Problem 3 Fresno Board Games
594(2)
Appendix 12.1 Chi-Square Tests with Jmp
596(4)
Appendix 12.2 Chi-Square Tests with Excel
600(3)
Available in the Cengage eBook
Appendix: Chi-Square Tests with R
Chapter 13 Experimental Design and Analysis of Variance
603(56)
Statistics in Practice: Burke, Inc.
604(1)
13.1 An Introduction to Experimental Design and Analysis of Variance
605(5)
Data Collection
606(1)
Assumptions for Analysis of Variance
607(1)
Analysis of Variance: A Conceptual Overview
607(3)
13.2 Analysis of Variance and the Completely Randomized Design
610(11)
Between-Treatments Estimate of Population Variance
611(1)
Within-Treatments Estimate of Population Variance
612(1)
Comparing the Variance Estimates: The FTest
612(2)
ANOVA Table
614(1)
Computer Results for Analysis of Variance
615(1)
Testing for the Equality of k Population Means: An Observational Study
616(5)
13.3 Multiple Comparison Procedures
621(6)
Fisher's LSD
621(2)
Type I Error Rates
623(4)
13.4 Randomized Block Design
627(6)
Air Traffic Controller Stress Test
627(2)
ANOVA Procedure
629(1)
Computations and Conclusions
629(4)
13.5 Factorial Experiment
633(8)
ANOVA Procedure
635(1)
Computations and Conclusions
635(6)
Summary
641(1)
Glossary
641(1)
Key Formulas
642(2)
Supplementary Exercises
644(4)
Case Problem 1 Wentworth Medical Center
648(1)
Case Problem 2 Compensation for Sales Professionals
649(1)
Case Problem 3 Touristopia Travel
650(2)
Appendix 13.1 Analysis of Variance with JMP
652(3)
Appendix 13.2 Analysis of Variance with Excel
655(4)
Available in the Cengage eBook
Appendix: Analysis of Variance with R
Chapter 14 Simple Linear Regression
659(82)
Statistics in Practice: Alliance Data Systems
660(1)
14.1 Simple Linear Regression Model
661(3)
Regression Model and Regression Equation
661(1)
Estimated Regression Equation
662(2)
14.2 Least Squares Method
664(10)
14.3 Coefficient of Determination
674(7)
Correlation Coefficient
677(4)
14.4 Model Assumptions
681(1)
14.5 Testing for Significance
682(8)
Estimate of σ2
682(1)
T Test
683(2)
Confidence Interval for σ3
685(1)
F Test
685(2)
Some Cautions About the Interpretation of Significance Tests
687(3)
14.6 Using the Estimated Regression Equation for Estimation and Prediction
690(7)
Interval Estimation
691(1)
Confidence Interval for the Mean Value of y
691(1)
Prediction Interval for an Individual Value of y
692(5)
14.7 Computer Solution
697(3)
14.8 Residual Analysis: Validating Model Assumptions
700(9)
Residual Plot Against x
701(2)
Residual Plot Against y
703(1)
Standardized Residuals
704(1)
Normal Probability Plot
705(4)
14.9 Residual Analysis: Outliers and Influential Observations
709(7)
Detecting Outliers
709(1)
Detecting Influential Observations
710(6)
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression
716(1)
Summary
717(1)
Glossary
717(1)
Key Formulas
718(2)
Supplementary Exercises
720(10)
Case Problem 1 Measuring Stock Market Risk
730(1)
Case Problem 2 U.S. Department of Transportation
731(1)
Case Problem 3 Selecting a Point-and-Shoot Digital Camera
731(1)
Case Problem 4 Finding the Best Car Value
732(1)
Case Problem 5 Buckeye Creek Amusement Park
733(2)
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
735(1)
Appendix 14.2 A Test for Significance Using Correlation
736(1)
Appendix 14.3 Simple Linear Regression with JMP
736(2)
Appendix 14.4 Regression Analysis with Excel
738(3)
Available in the Cengage eBook
Appendix: Simple Linear Regression with R
Chapter 15 Multiple Regression
741(71)
Statistics in Practice: 84.51°
742(51)
15.1 Multiple Regression Model Regression Model and Regression Equation Estimated Multiple Regression Equation
15.2 Least Squares Method
An Example: Butler Trucking Company Note on Interpretation of Coefficients
15.3 Multiple Coefficient of Determination
15.4 Model Assumptions
15.5 Testing for Significance
F Test
T Test
Multicollinearity
15.6 Using the Estimated Regression Equation for Estimation and Prediction
15.7 Categorical Independent Variables
An Example: Johnson Filtration, Inc.
Interpreting the Parameters
More Complex Categorical Variables
15.8 Residual Analysis
Detecting Outliers
Studentized Deleted Residuals and Outliers Influential Observations Using Cook's Distance Measure to Identify Influential Observations
15.9 Logistic Regression
Logistic Regression Equation
Estimating the Logistic Regression Equation
Testing for Significance
Managerial Use
Interpreting the Logistic Regression Equation Logit Transformation
15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression
Summary
793(1)
Glossary
793(1)
Key Formulas
794(2)
Supplementary Exercises
796(5)
Case Problem 1 Consumer Research, Inc.
801(1)
Case Problem 2 Predicting Winnings for Nascar Drivers
802(2)
Case Problem 3 Finding the Best Car Value
804(2)
Appendix 15.1 Multiple Linear Regression with Jmp
806(2)
Appendix 15.2 Logistic Regression with Jmp
808(1)
Appendix 15.3 Multiple Regression with Excel
809(3)
Available in the Cengage eBook
Appendix: Multiple Linear Regression with R
Appendix: Logistic Regression with R
Appendix A References and Bibliography 812(2)
Appendix B Tables 814(27)
Appendix C Summation Notation 841(2)
Appendix D Microsoft Excel and Tools for Statistical Analysis 843(8)
Appendix E Computing p-Values with JMP and Excel 851(4)
Appendix F Microsoft Excel Online and Tools for Statistical Analysis 855(8)
Appendix G Solutions to Even-Numbered Exercises (Cengage eBook) Index 863
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. David R. Anderson is a leading author and professor emeritus of quantitative analysis in the College of Business Administration at the University of Cincinnati. Dr. Anderson has served as head of the Department of Quantitative Analysis and Operations Management and as associate dean of the College of Business Administration. He was also coordinator of the colleges first executive program. In addition to introductory statistics for business students, Dr. Anderson taught graduate-level courses in regression analysis, multivariate analysis and management science. He also taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences and he actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, Dr. Anderson earned his B.S., M.S. and Ph.D. degrees from Purdue University. Dennis J. Sweeney is professor emeritus of quantitative analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a B.S.B.A. degree from Drake University and his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA fellow. Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. He also served as head of the Department of Quantitative Analysis and served four years as associate dean of the College of Business Administration at the University of Cincinnati. Dr. Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in journals such as Management Science, Operations Research, Mathematical Programming and Decision Sciences. Dr. Sweeney has co-authored 10 textbooks in the areas of statistics, management science, linear programming and production and operations management.