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Business Analytics 3rd edition [Hardback]

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(Wake Forest University), (University of Cincinnati), (Rochester Institute of Technology), (University of Cincinnati), (University of Iowa), (University of Cincinnati), (University of Alabama)
  • Formāts: Hardback, 800 pages, height x width x depth: 35x220x279 mm, weight: 1950 g
  • Izdošanas datums: 27-Mar-2018
  • Izdevniecība: South-Western College Publishing
  • ISBN-10: 1337406422
  • ISBN-13: 9781337406420
Citas grāmatas par šo tēmu:
  • Formāts: Hardback, 800 pages, height x width x depth: 35x220x279 mm, weight: 1950 g
  • Izdošanas datums: 27-Mar-2018
  • Izdevniecība: South-Western College Publishing
  • ISBN-10: 1337406422
  • ISBN-13: 9781337406420
Citas grāmatas par šo tēmu:
Cover the full range of analytics --from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' market-leading BUSINESS ANALYTICS, 3E. Clear, step-by-step instructions help students learn how to use Excel as well as powerful, but easy-to-use software, such as JMP Pro and the Excel Add-in Analytic Solver. The authors introduce students to more advanced analytics concepts while giving you, the instructor, the freedom to choose your preferred software for teaching the concepts. Extensive solutions to problems and cases save you time in grading and helping students master the material. In addition, MindTap customizable digital course solution offers an interactive eBook, auto graded exercises from the book, algorithmic practice problems with solutions and Exploring Analytics visualizations to strengthen understanding of course concepts.
About The Authors xix
Preface xxiii
Chapter 1 Introduction 2(16)
1.1 Decision Making
4(1)
1.2 Business Analytics Defined
5(1)
1.3 A Categorization of Analytical Methods and Models
6(1)
Descriptive Analytics
6(1)
Predictive Analytics
6(1)
Prescriptive Analytics
7(1)
1.4 Big Data
7(4)
Volume
9(1)
Velocity
9(1)
Variety
9(1)
Veracity
9(2)
1.5 Business Analytics in Practice
11(3)
Financial Analytics
11(1)
Human Resource (HR) Analytics
12(1)
Marketing Analytics
12(1)
Health Care Analytics
12(1)
Supply-Chain Analytics
13(1)
Analytics for Government and Nonprofits
13(1)
Sports Analytics
13(1)
Web Analytics
14(1)
Summary
14(1)
Glossary
15(3)
Chapter 2 Descriptive Statistics 18(64)
Analytics in Action: U.S. Census Bureau
19(1)
2.1 Overview of Using Data: Definitions and Goals
19(2)
2.2 Types of Data
21(3)
Population and Sample Data
21(1)
Quantitative and Categorical Data
21(1)
Cross-Sectional and Time Series Data
21(1)
Sources of Data
21(3)
2.3 Modifying Data in Excel
24(5)
Sorting and Filtering Data in Excel
24(3)
Conditional Formatting of Data in Excel
27(2)
2.4 Creating Distributions from Data
29(10)
Frequency Distributions for Categorical Data
29(1)
Relative Frequency and Percent Frequency Distributions
30(1)
Frequency Distributions for Quantitative Data
31(3)
Histograms
34(3)
Cumulative Distributions
37(2)
2.5 Measures of Location
39(5)
Mean (Arithmetic Mean)
39(1)
Median
40(1)
Mode
41(1)
Geometric Mean
41(3)
2.6 Measures of Variability
44(3)
Range
44(1)
Variance
45(1)
Standard Deviation
46(1)
Coefficient of Variation
47(1)
2.7 Analyzing Distributions
47(8)
Percentiles
48(1)
Quartiles
49(1)
z-Scores
49(1)
Empirical Rule
50(2)
Identifying Outliers
52(1)
Box Plots
52(3)
2.8 Measures of Association Between Two Variables
55(6)
Scatter Charts
55(2)
Covariance
57(3)
Correlation Coefficient
60(1)
2.9 Data Cleansing
61(7)
Missing Data
61(2)
Blakely Tires
63(2)
Identification of Erroneous Outliers and Other Erroneous Values
65(2)
Variable Representation
67(1)
Summary
68(1)
Glossary
69(2)
Problems
71(8)
Case Problem: Heavenly Chocolates Web Site Transactions
79(3)
Chapter 3 Data Visualization 82(56)
Analytics in Action: Cincinnati Zoo & Botanical Garden
83(2)
3.1 Overview of Data Visualization
85(3)
Effective Design Techniques
85(3)
3.2 Tables
88(11)
Table Design Principles
89(1)
Crosstabulation
90(3)
PivotTables in Excel
93(4)
Recommended PivotTables in Excel
97(2)
3.3 Charts
99(18)
Scatter Charts
99(2)
Recommended Charts in Excel
101(1)
Line Charts
102(4)
Bar Charts and Column Charts
106(1)
A Note on Pie Charts and Three-Dimensional Charts
107(2)
Bubble Charts
109(1)
Heat Maps
110(2)
Additional Charts for Multiple Variables
112(3)
PivotCharts in Excel
115(2)
3.4 Advanced Data Visualization
117(5)
Advanced Charts
117(3)
Geographic Information Systems Charts
120(2)
3.5 Data Dashboards
122(3)
Principles of Effective Data Dashboards
123(1)
Applications of Data Dashboards
123(2)
Summary
125(1)
Glossary
125(1)
Problems
126(10)
Case Problem: All-Time Movie Box-Office Data
136(2)
Chapter 4 Descriptive Data Mining 138(28)
Analytics in Action: Advice from a Machine
139(1)
4.1 Cluster Analysis
140(8)
Measuring Similarity Between Observations
140(3)
Hierarchical Clustering
143(3)
k-Means Clustering
146(1)
Hierarchical Clustering versus k-Means Clustering
147(1)
4.2 Association Rules
148(3)
Evaluating Association Rules
150(1)
4.3 Text Mining
151(4)
Voice of the Customer at Triad Airline
151(2)
Preprocessing Text Data for Analysis
153(1)
Movie Reviews
154(1)
Summary
155(1)
Glossary
155(1)
Problems
156(8)
Case Problem: Know Thy Customer
164(2)
Chapter 5 Probability: An Introduction to Modeling Uncertainty 166(54)
Analytics in Action: National Aeronautics and Space Administration
167(1)
5.1 Events and Probabilities
168(1)
5.2 Some Basic Relationships of Probability
169(3)
Complement of an Event
169(1)
Addition Law
170(2)
5.3 Conditional Probability
172(8)
Independent Events
177(1)
Multiplication Law
177(1)
Bayes' Theorem
178(2)
5.4 Random Variables
180(2)
Discrete Random Variables
180(1)
Continuous Random Variables
181(1)
5.5 Discrete Probability Distributions
182(12)
Custom Discrete Probability Distribution
182(2)
Expected Value and Variance
184(3)
Discrete Uniform Probability Distribution
187(1)
Binomial Probability Distribution
188(3)
Poisson Probability Distribution
191(3)
5.6 Continuous Probability Distributions
194(13)
Uniform Probability Distribution
194(2)
Triangular Probability Distribution
196(2)
Normal Probability Distribution
198(5)
Exponential Probability Distribution
203(4)
Summary
207(1)
Glossary
207(2)
Problems
209(9)
Case Problem: Hamilton County Judges
218(2)
Chapter 6 Statistical Inference 220(74)
Analytics in Action: John Morrell & Company
221(2)
6.1 Selecting a Sample
223(4)
Sampling from a Finite Population
223(1)
Sampling from an Infinite Population
224(3)
6.2 Point Estimation
227(2)
Practical Advice
229(1)
6.3 Sampling Distributions
229(11)
Sampling Distribution of x
232(5)
Sampling Distribution of To
237(3)
6.4 Interval Estimation
240(10)
Interval Estimation of the Population Mean
240(7)
Interval Estimation of the Population Proportion
247(3)
6.5 Hypothesis Tests
250(18)
Developing Null and Alternative Hypotheses
250(3)
Type I and Type II Errors
253(1)
Hypothesis Test of the Population Mean
254(11)
Hypothesis Test of the Population Proportion
265(3)
6.6 Big Data, Statistical Inference, and Practical Significance
268(10)
Sampling Error
268(1)
Nonsampling Error
269(1)
Big Data
270(1)
Understanding What Big Data Is
271(1)
Big Data and Sampling Error
272(1)
Big Data and the Precision of Confidence Intervals
273(1)
Implications of Big Data for Confidence Intervals
274(1)
Big Data, Hypothesis Testing, and p Values
275(2)
Implications of Big Data in Hypothesis Testing
277(1)
Summary
278(1)
Glossary
279(2)
Problems
281(10)
Case Problem 1: Young Professional Magazine
291(1)
Case Problem 2: Quality Associates, Inc
292(2)
Chapter 7 Linear Regression 294(78)
Analytics in Action: Alliance Data Systems
295(1)
7.1 Simple Linear Regression Model
296(2)
Regression Model
296(1)
Estimated Regression Equation
296(2)
7.2 Least Squares Method
298(6)
Least Squares Estimates of the Regression Parameters
300(2)
Using Excel's Chart Tools to Compute the Estimated Regression Equation
302(2)
7.3 Assessing the Fit of the Simple Linear Regression Model
304(4)
The Sums of Squares
304(2)
The Coefficient of Determination
306(1)
Using Excel's Chart Tools to Compute the Coefficient of Determination
307(1)
7.4 The Multiple Regression Model
308(5)
Regression Model
308(1)
Estimated Multiple Regression Equation
308(1)
Least Squares Method and Multiple Regression
309(1)
Butler Trucking Company and Multiple Regression
310(1)
Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation
310(3)
7.5 Inference and Regression
313(12)
Conditions Necessary for Valid Inference in the Least Squares Regression Model
314(4)
Testing Individual Regression Parameters
318(3)
Addressing Nonsignificant Independent Variables
321(1)
Multicollinearity
322(3)
7.6 Categorical Independent Variables
325(5)
Butler Trucking Company and Rush Hour
325(2)
Interpreting the Parameters
327(1)
More Complex Categorical Variables
328(2)
7.7 Modeling Nonlinear Relationships
330(12)
Quadratic Regression Models
331(4)
Piecewise Linear Regression Models
335(2)
Interaction Between Independent Variables
337(5)
7.8 Model Fitting
342(2)
Variable Selection Procedures
342(1)
Overfitting
343(1)
7.9 Big Data and Regression
344(5)
Inference and Very Large Samples
344(4)
Model Selection
348(1)
7.10 Prediction with Regression
349(2)
Summary
351(1)
Glossary
352(2)
Problems
354(15)
Case Problem: Alumni Giving
369(3)
Chapter 8 Time Series Analysis and Forecasting 372(50)
Analytics in Action: ACCO Brands
373(2)
8.1 Time Series Patterns
375(7)
Horizontal Pattern
375(2)
Trend Pattern
377(1)
Seasonal Pattern
378(1)
Trend and Seasonal Pattern
379(3)
Cyclical Pattern
382(1)
Identifying Time Series Patterns
382(1)
8.2 Forecast Accuracy
382(4)
8.3 Moving Averages and Exponential Smoothing
386(9)
Moving Averages
387(4)
Exponential Smoothing
391(4)
8.4 Using Regression Analysis for Forecasting
395(10)
Linear Trend Projection
395(2)
Seasonality Without Trend
397(1)
Seasonality with Trend
398(3)
Using Regression Analysis as a Causal Forecasting Method
401(3)
Combining Causal Variables with Trend and Seasonality Effects
404(1)
Considerations in Using Regression in Forecasting
405(1)
8.5 Determining the Best Forecasting Model to Use
405(1)
Summary
406(1)
Glossary
406(1)
Problems
407(8)
Case Problem: Forecasting Food and Beverage Sales
415(7)
Chapter 9 Predictive Data Mining 422(42)
Analytics in Action: Orbitz
423(1)
9.1 Data Sampling, Preparation, and Partitioning
424(1)
9.2 Performance Measures
425(7)
Evaluating the Classification of Categorical Outcomes
425(6)
Evaluating the Estimation of Continuous Outcomes
431(1)
9.3 Logistic Regression
432(4)
9.4 k-Nearest Neighbors
436(3)
Classifying Categorical Outcomes with k-Nearest Neighbors
436(2)
Estimating Continuous Outcomes with k-Nearest Neighbors
438(1)
9.5 Classification and Regression Trees
439(10)
Classifying Categorical Outcomes with a Classification Tree
439(6)
Estimating Continuous Outcomes with a Regression Tree
445(1)
Ensemble Methods
446(3)
Summary
449(1)
Glossary
450(2)
Problems
452(10)
Case Problem: Grey Code Corporation
462(2)
Chapter 10 Spreadsheet Models 464(36)
Analytics in Action: Procter & Gamble
465(1)
10.1 Building Good Spreadsheet Models
466(5)
Influence Diagrams
466(1)
Building a Mathematical Model
466(2)
Spreadsheet Design and Implementing the Model in a Spreadsheet
468(3)
10.2 What-If Analysis
471(9)
Data Tables
471(2)
Goal Seek
473(2)
Scenario Manager
475(5)
10.3 Some Useful Excel Functions for Modeling
480(7)
SUM and SUMPRODUCT
481(2)
IF and COUNTIF
483(2)
VLOOKUP
485(2)
10.4 Auditing Spreadsheet Models
487(4)
Trace Precedents and Dependents
487(1)
Show Formulas
487(2)
Evaluate Formulas
489(1)
Error Checking
489(1)
Watch Window
490(1)
10.5 Predictive and Prescriptive Spreadsheet Models
491(1)
Summary
492(1)
Glossary
492(1)
Problems
493(6)
Case Problem: Retirement Plan
499(1)
Chapter 11 Monte Carlo Simulation 500(56)
Analytics in Action: Polio Eradication
501(1)
11.1 Risk Analysis for Sanotronics LLC
502(12)
Base-Case Scenario
502(1)
Worst-Case Scenario
503(1)
Best-Case Scenario
503(1)
Sanotronics Spreadsheet Model
503(1)
Use of Probability Distributions to Represent Random Variables
504(2)
Generating Values for Random Variables with Excel
506(4)
Executing Simulation Trials with Excel
510(1)
Measuring and Analyzing Simulation Output
510(4)
11.2 Simulation Modeling for Land Shark Inc.
514(13)
Spreadsheet Model for Land Shark
515(2)
Generating Values for Land Shark's Random Variables
517(2)
Executing Simulation Trials and Analyzing Output
519(3)
Generating Bid Amounts with Fitted Distributions
522(5)
11.3 Simulation with Dependent Random Variables
527(5)
Spreadsheet Model for Press Teag Worldwide
527(5)
11.4 Simulation Considerations
532(1)
Verification and Validation
532(1)
Advantages and Disadvantages of Using Simulation
532(1)
Summary
533(1)
Glossary
534(1)
Problems
534(13)
Case Problem: Four Corners
547(2)
Appendix 11.1: Common Probability Distributions for Simulation
549(7)
Chapter 12 Linear Optimization Models 556(50)
Analytics in Action: General Electric
557(1)
12.1 A Simple Maximization Problem
558(3)
Problem Formulation
559(2)
Mathematical Model for the Par, Inc. Problem
561(1)
12.2 Solving the Par, Inc. Problem
561(7)
The Geometry of the Par, Inc. Problem
562(2)
Solving Linear Programs with Excel Solver
564(4)
12.3 A Simple Minimization Problem
568(2)
Problem Formulation
568(1)
Solution for the M&D Chemicals Problem
568(2)
12.4 Special Cases of Linear Program Outcomes
570(5)
Alternative Optimal Solutions
571(1)
Infeasibility
572(1)
Unbounded
573(2)
12.5 Sensitivity Analysis
575(2)
Interpreting Excel Solver Sensitivity Report
575(2)
12.6 General Linear Programming Notation and More Examples
577(12)
Investment Portfolio Selection
578(2)
Transportation Planning
580(4)
Advertising Campaign Planning
584(5)
12.7 Generating an Alternative Optimal Solution for a Linear Program
589(2)
Summary
591(1)
Glossary
592(1)
Problems
593(11)
Case Problem: Investment Strategy
604(2)
Chapter 13 Integer Linear Optimization Models 606(40)
Analytics in Action: Petrobras
607(1)
13.1 Types of Integer Linear Optimization Models
607(1)
13.2 Eastborne Realty, an Example of Integer Optimization
608(3)
The Geometry of Linear All-Integer Optimization
609(2)
13.3 Solving Integer Optimization Problems with Excel Solver
611(5)
A Cautionary Note About Sensitivity Analysis
614(2)
13.4 Applications Involving Binary Variables
616(10)
Capital Budgeting
616(2)
Fixed Cost
618(3)
Bank Location
621(2)
Product Design and Market Share Optimization
623(3)
13.5 Modeling Flexibility Provided by Binary Variables
626(2)
Multiple-Choice and Mutually Exclusive Constraints
626(1)
k Out of n Alternatives Constraint
627(1)
Conditional and Corequisite Constraints
627(1)
13.6 Generating Alternatives in Binary Optimization
628(2)
Summary
630(1)
Glossary
631(1)
Problems
632(11)
Case Problem: Applecore Children's Clothing
643(3)
Chapter 14 Nonlinear Optimization Models 646(32)
Analytics in Action: InterContinental Hotels
647(1)
14.1 A Production Application: Par, Inc. Revisited
647(5)
An Unconstrained Problem
647(1)
A Constrained Problem
648(2)
Solving Nonlinear Optimization Models Using Excel Solver
650(1)
Sensitivity Analysis and Shadow Prices in Nonlinear Models
651(1)
14.2 Local and Global Optima
652(5)
Overcoming Local Optima with Excel Solver
655(2)
14.3 A Location Problem
657(1)
14.4 Markowitz Portfolio Model
658(5)
14.5 Forecasting Adoption of a New Product
663(3)
Summary
666(1)
Glossary
667(1)
Problems
667(8)
Case Problem: Portfolio Optimization with Transaction Costs
675(3)
Chapter 15 Decision Analysis 678(46)
Analytics in Action: Phytopharm
679(1)
15.1 Problem Formulation
680(2)
Payoff Tables
681(1)
Decision Trees
681(1)
15.2 Decision Analysis without Probabilities
682(3)
Optimistic Approach
682(1)
Conservative Approach
683(1)
Minimax Regret Approach
683(2)
15.3 Decision Analysis with Probabilities
685(4)
Expected Value Approach
685(2)
Risk Analysis
687(1)
Sensitivity Analysis
688(1)
15.4 Decision Analysis with Sample Information
689(6)
Expected Value of Sample Information
694(1)
Expected Value of Perfect Information
694(1)
15.5 Computing Branch Probabilities with Bayes' Theorem
695(3)
15.6 Utility Theory
698(26)
Utility and Decision Analysis
699(4)
Utility Functions
703(3)
Exponential Utility Function
706(2)
Summary
708(1)
Glossary
708(2)
Problems
710(11)
Case Problem: Property Purchase Strategy
721(3)
Appendix A: Basics Of Excel 724(12)
Appendix B: Database Basics With Microsoft Access 736(38)
References 774(2)
Index 776
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.