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Essentials of Statistics for Business & Economics 9th edition [Kõva köide]

(University of Iowa), (University of Cincinnati), (Wake Forest University), (Rochester Institute of Technology), (University of Cincinnati), (University of Cincinnati), (University of Alabama)
  • Formaat: Hardback, 880 pages, kõrgus x laius x paksus: 38x223x281 mm, kaal: 1995 g
  • Ilmumisaeg: 01-Feb-2019
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 0357045432
  • ISBN-13: 9780357045435
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  • Formaat: Hardback, 880 pages, kõrgus x laius x paksus: 38x223x281 mm, kaal: 1995 g
  • Ilmumisaeg: 01-Feb-2019
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 0357045432
  • ISBN-13: 9780357045435
Discover how statistical information impacts decisions in todays business world as Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann's leading ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 9E connects concepts in each chapter to real-world practice. This edition delivers sound statistical methodology, a proven problem-scenario approach and meaningful applications that reflect the latest developments in business and statistics today. More than 350 new and proven real business examples, a wealth of practical cases and meaningful hands-on exercises highlight statistics in action. You gain practice using leading professional statistical software with exercises and appendices that walk you through using JMP® Student Edition 14 and Excel® 2016. WebAssign's online course management systems is available separately to further strengthen this business statistics approach and helps you maximize your course success.
About The Authors xix
Preface xxiii
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)
Appendix 1.2 Getting Started with R and RStudio (MindTap Reader)
Appendix 1.3 Basic Data Manipulation in R (MindTap Reader)
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)
Appendix 2.3 Creating Tabular and Graphical Presentations with R (MindTap Reader)
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)
Appendix 3.3 Descriptive Statistics with R (MindTap Reader)
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(5)
Tabular Approach
210(2)
Summary
212(1)
Glossary
213(1)
Key Formulas
214(1)
Supplementary Exercises
214(5)
Case Problem 1: Hamilton County Judges
219(2)
Case Problem 2: Rob's Market
221(2)
Chapter 5 Discrete Probability Distributions 223(59)
Statistics in Practice: Voter Waiting Times in Elections
224(1)
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(9)
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
272(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(4)
Appendix 5.3 Discrete Probability Distributions with R (MindTap Reader)
Chapter 6 Continuous Probability Distributions 281 Statistics in Practice: Procter & Gamble 282(37)
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 Probabilities for 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
302(1)
Relationship Between the Poisson and Exponential Distributions
303(2)
Summary
305(1)
Glossary
305(1)
Key Formulas
306(1)
Supplementary Exercises
306(3)
Case Problem 1: Specialty Toys
309(2)
Case Problem 2: Gebhardt Electronics
311(1)
Appendix 6.1 Continuous Probability Distributions with JMP
312(5)
Appendix 6.2 Continuous Probability Distributions with Excel
317(2)
Appendix 6.3 Continuous Probability Distribution with R (MindTap Reader)
Chapter 7 Sampling and Sampling Distributions 319(54)
Statistics in Practice: Meadwestvaco Corporation
320(1)
7.1 The Electronics Associates Sampling Problem
321(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: Marion Dairies
366(1)
Appendix 7.1 The Expected Value and Standard Deviation of x
367(1)
Appendix 7.2 Random Sampling with JMP
368(3)
Appendix 7.3 Random Sampling with Excel
371(2)
Appendix 7.4 Random Sampling with R (MindTap Reader)
Chapter 8 Interval Estimation 373(44)
Statistics in Practice: Food Lion
374(1)
8.1 Population Mean: σ Known
375(6)
Margin of Error and the Interval Estimate
375(4)
Practical Advice
379(2)
8.2 Population Mean: σ Unknown
381(9)
Margin of Error and the Interval Estimate
382(3)
Practical Advice
385(1)
Using a Small Sample
385(1)
Summary of Interval Estimation Procedures
386(4)
8.3 Determining the Sample Size
390(3)
8.4 Population Proportion
393(5)
Determining the Sample Size
394(4)
8.5 Big Data and Confidence Intervals
398(3)
Big Data and the Precision of Confidence Intervals
398(1)
Implications of Big Data for Confidence Intervals
399(2)
Summary
401(1)
Glossary
402(1)
Key Formulas
402(1)
Supplementary Exercises
403(3)
Case Problem 1: Young Professional Magazine
406(1)
Case Problem 2: Gulf Real Estate Properties
407(2)
Case Problem 3: Metropolitan Research, Inc.
409(1)
Appendix 8.1 Interval Estimation with JMP
410(3)
Appendix 8.2 Interval Estimation Using Excel
413(4)
Appendix 8.3 Interval Estimation with R (MindTap Reader)
Chapter 9 Hypothesis Tests 417(64)
Statistics in Practice: John Morrell & Company
418(1)
9.1 Developing Null and Alternative Hypotheses
419(3)
The Alternative Hypothesis as a Research Hypothesis
419(1)
The Null Hypothesis as an Assumption to Be Challenged
420(1)
Summary of Forms for Null and Alternative Hypotheses
421(1)
9.2 Type I and Type II Errors
422(3)
9.3 Population Mean: σ Known
425(14)
One-Tailed Test
425(5)
Two-Tailed Test
430(3)
Summary and Practical Advice
433(1)
Relationship Between Interval Estimation and Hypothesis Testing
434(5)
9.4 Population Mean: σ Unknown
439(6)
One-Tailed Test
439(1)
Two-Tailed Test
440(1)
Summary and Practical Advice
441(4)
9.5 Population Proportion
445(5)
Summary
447(3)
9.6 Hypothesis Testing and Decision Making
450(1)
9.7 Calculating the Probability of Type II Errors
450(5)
9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean
455(4)
9.9 Big Data and Hypothesis Testing
459(3)
Big Data, Hypothesis Testing, and p Values
459(1)
Implications of Big Data in Hypothesis Testing
460(2)
Summary
462(1)
Glossary
462(1)
Key Formulas
463(1)
Supplementary Exercises
463(4)
Case Problem 1: Quality Associates, Inc.
467(2)
Case Problem 2: Ethical Behavior of Business Students at Bayview University
469(2)
Appendix 9.1 Hypothesis Testing with JMP
471(4)
Appendix 9.2 Hypothesis Testing with Excel
475(6)
Appendix 9.3 Hypothesis Testing with R (MindTap Reader)
Chapter 10 Inference About Means and Proportions with Two Populations 481(44)
Statistics in Practice: U.S. Food and Drug Administration
482(1)
10.1 Inferences About the Difference Between Two Population Means: σ1 and σ2 Known
483(6)
Interval Estimation of μ1 - μ2
483(2)
Hypothesis Tests About μ1 - μ2
485(2)
Practical Advice
487(2)
10.2 Inferences About the Difference Between Two Population Means: σ1 and σ2 Unknown
489(8)
Interval Estimation of - μ1 - μ2
489(2)
Hypothesis Tests About μ1 - μ2
491(2)
Practical Advice
493(4)
10.3 Inferences About the Difference Between Two Population Means: Matched Samples
497(6)
10.4 Inferences About the Difference Between Two Population Proportions
503(6)
Interval Estimation of p1 - p2
503(2)
Hypothesis Tests About p1 - p2
505(4)
Summary
509(1)
Glossary
509(1)
Key Formulas
509(2)
Supplementary Exercises
511(3)
Case Problem: Par, Inc.
514(1)
Appendix 10.1 Inferences About Two Populations with JMP
515(4)
Appendix 10.2 Inferences About Two Populations with Excel
519(6)
Appendix 10.3 Inferences about Two Populations with R (MindTap Reader)
Chapter 11 Inferences About Population Variances 525(28)
Statistics in Practice: U.S. Government Accountability Office
526(1)
11.1 Inferences About a Population Variance
527(10)
Interval Estimation
527(4)
Hypothesis Testing
531(6)
11.2 Inferences About Two Population Variances
537(7)
Summary
544(1)
Key Formulas
544(1)
Supplementary Exercises
544(2)
Case Problem 1: Air Force Training Program
546(1)
Case Problem 2: Meticulous Drill & Reamer
547(2)
Appendix 11.1 Population Variances with JMP
549(2)
Appendix 11.2 Population Variances with Excel
551(2)
Appendix 11.3 Population Variances with R (MindTap Reader)
Chapter 12 Comparing Multiple Proportions, Test of Independence and Goodness of Fit 553(44)
Statistics in Practice: United Way
554(1)
12.1 Testing the Equality of Population Proportions for Three or More Populations
555(10)
A Multiple Comparison Procedure
560(5)
12.2 Test of Independence
565(8)
12.3 Goodness of Fit Test
573(9)
Multinomial Probability Distribution
573(3)
Normal Probability Distribution
576(6)
Summary
582(1)
Glossary
582(1)
Key Formulas
583(1)
Supplementary Exercises
583(4)
Case Problem 1: A Bipartisan Agenda for Change
587(1)
Case Problem 2: Fuentes Salty Snacks, Inc.
588(1)
Case Problem 3: Fresno Board Games
588(2)
Appendix 12.1 Chi-Square Tests with JMP
590(3)
Appendix 12.2 Chi-Square Tests with Excel
593(4)
Appendix 12.3 Chi-Squared Tests with R (MindTap Reader)
Chapter 13 Experimental Design and Analysis of Variance 597(56)
Statistics in Practice: Burke Marketing Services, Inc.
598(1)
13.1 An Introduction to Experimental Design and Analysis of Variance
599(5)
Data Collection
600(1)
Assumptions for Analysis of Variance
601(1)
Analysis of Variance: A Conceptual Overview
601(3)
13.2 Analysis of Variance and the Completely Randomized Design
604(11)
Between-Treatments Estimate of Population Variance
605(1)
Within-Treatments Estimate of Population Variance
606(1)
Comparing the Variance Estimates: The F Test
606(2)
ANOVA Table
608(1)
Computer Results for Analysis of Variance
609(1)
Testing for the Equality of k Population Means: An Observational Study
610(5)
13.3 Multiple Comparison Procedures
615(6)
Fisher's LSD
615(2)
Type I Error Rates
617(4)
13.4 Randomized Block Design
621(6)
Air Traffic Controller Stress Test
621(2)
ANOVA Procedure
623(1)
Computations and Conclusions
623(4)
13.5 Factorial Experiment
627(8)
ANOVA Procedure
629(1)
Computations and Conclusions
629(6)
Summary
635(1)
Glossary
635(1)
Key Formulas
636(2)
Supplementary Exercises
638(5)
Case Problem 1: Wentworth Medical Center
643(1)
Case Problem 2: Compensation for Sales Professionals
644(1)
Case Problem 3: Touristopia Travel
644(2)
Appendix 13.1 Analysis of Variance with JMP
646(3)
Appendix 13.2 Analysis of Variance with Excel
649(4)
Appendix 13.3 Analysis Variance with R (MindTap Reader)
Chapter 14 Simple Linear Regression 653(78)
Statistics in Practice: Alliance Data Systems
654(1)
14.1 Simple Linear Regression Model
655(3)
Regression Model and Regression Equation
655(1)
Estimated Regression Equation
656(2)
14.2 Least Squares Method
658(10)
14.3 Coefficient of Determination
668(7)
Correlation Coefficient
671(4)
14.4 Model Assumptions
675(1)
14.5 Testing for Significance
676(8)
Estimate of σ2
676(1)
t Test
677(2)
Confidence Interval for β1
679(1)
F Test
679(2)
Some Cautions About the Interpretation of Significance Tests
681(3)
14.6 Using the Estimated Regression Equation
for Estimation and Prediction
684(1)
Interval Estimation
685(1)
Confidence Interval for the Mean Value of y
685(1)
Prediction Interval for an Individual Value of y
686(5)
14.7 Computer Solution
691(3)
14.8 Residual Analysis: Validating Model Assumptions
694(9)
Residual Plot Against x
695(2)
Residual Plot Against y
697(1)
Standardized Residuals
698(1)
Normal Probability Plot
699(4)
14.9 Residual Analysis: Outliers and Influential Observations
703(7)
Detecting Outliers
703(1)
Detecting Influential Observations
704(6)
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression
710(1)
Summary
711(1)
Glossary
711(1)
Key Formulas
712(2)
Supplementary Exercises
714(7)
Case Problem 1: Measuring Stock Market Risk
721(1)
Case Problem 2: U.S. Department of Transportation
721(1)
Case Problem 3: Selecting a Point-and-Shoot Digital Camera
722(1)
Case Problem 4: Finding the Best Car Value
723(1)
Case Problem 5: Buckeye Creek Amusement Park
724(2)
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
726(1)
Appendix 14.2 A Test for Significance Using Correlation
727(1)
Appendix 14.3 Simple Linear Regression with JMP
727(1)
Appendix 14.4 Regression Analysis with Excel
728(3)
Appendix 14.5 Simple Linear Regression with R (MindTap Reader)
Chapter 15 Multiple Regression 731(69)
Statistics in Practice: 84.51°
732(1)
15.1 Multiple Regression Model
733(1)
Regression Model and Regression Equation
733(1)
Estimated Multiple Regression Equation
733(1)
15.2 Least Squares Method
734(9)
An Example: Butler Trucking Company
735(2)
Note on Interpretation of Coefficients
737(6)
15.3 Multiple Coefficient of Determination
743(3)
15.4 Model Assumptions
746(1)
15.5 Testing for Significance
747(6)
F Test
747(3)
t Test
750(1)
Multicollinearity
750(3)
15.6 Using the Estimated Regression Equation for Estimation and Prediction
753(2)
15.7 Categorical Independent Variables
755(9)
An Example: Johnson Filtration, Inc.
756(2)
Interpreting the Parameters
758(2)
More Complex Categorical Variables
760(4)
15.8 Residual Analysis
764(7)
Detecting Outliers
766(1)
Studentized Deleted Residuals and Outliers
766(1)
Influential Observations
767(1)
Using Cook's Distance Measure to Identify Influential Observations
767(4)
15.9 Logistic Regression
771(11)
Logistic Regression Equation
772(1)
Estimating the Logistic Regression Equation
773(1)
Testing for Significance
774(1)
Managerial Use
775(1)
Interpreting the Logistic Regression Equation
776(2)
Logit Transformation
778(4)
15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression
782(1)
Summary
783(1)
Glossary
783(1)
Key Formulas
784(2)
Supplementary Exercises
786(4)
Case Problem 1: Consumer Research, Inc.
790(1)
Case Problem 2: Predicting Winnings for NASCAR Drivers
791(1)
Case Problem 3: Finding the Best Car Value
792(2)
Appendix 15.1 Multiple Linear Regression with JMP
794(2)
Appendix 15.2 Logistic Regression with JMP
796(1)
Appendix 15.3 Multiple Regression with Excel
797(3)
Appendix 15.4 Multiple Linear Regression with R (MindTap Reader)
Appendix 15.5 Logistics Regression with R (MindTap Reader)
Appendix A References And Bibliography 800(2)
Appendix B Tables 802(27)
Appendix C Summation Notation 829(2)
Appendix D Answers To Even-Numbered Exercises (Mindtap Reader)
Appendix E Microsoft Excel 2016 And Tools For Statistical Analysis 831(8)
Appendix F Computing P-Values With JMP And Excel 839(4)
Index 843
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. 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.