Muutke küpsiste eelistusi

Essentials of Modern Business Statistics with Microsoft® Excel® 8th edition [Kõva köide]

(Wake Forest University), (University of Cincinnati), (University of Iowa), (University of Cincinnati), (Rochester Institute of Technology), (University of Cincinnati), (University of Alabama)
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  • Ilmumisaeg: 01-Jan-2020
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 0357131622
  • ISBN-13: 9780357131626
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  • Formaat: Hardback, 816 pages, kõrgus x laius x paksus: 35x223x279 mm, kaal: 1905 g
  • Ilmumisaeg: 01-Jan-2020
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 0357131622
  • ISBN-13: 9780357131626
Develop a strong conceptual understanding of statistics as ESSENTIALS OF MODERN BUSINESS STATISTICS WITH MICROSOFT® EXCEL®, 8E balances real-world applications with an integrated focus on the latest version of Microsoft® Excel®. This best-selling, essentials edition clearly develops each statistical technique in an application setting. You learn to master statistical methodology with an easy-to-follow presentation of a statistical procedure followed by a discussion of how to use Excel® 2019 to perform the procedure. Step-by-step instructions and screen captures ensure understanding.

More than 140 new business examples, proven methods, and application exercises show how statistics provide insights into today's business decisions and problems. A unique problem-scenario approach and new case problems demonstrate how to apply statistical methods to practical business situations. MindTap digital resources provide tools to help you master Excel®, Excel® Online, and R as well as gain an understanding of business statistics.
Preface xix
About the Authors xxv
Chapter 1 Data and Statistics
1(34)
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 Statistical Analysis Using Microsoft Excel
16(4)
Data Sets and Excel Worksheets
17(1)
Using Excel for Statistical Analysis
18(2)
1.7 Analytics
20(1)
1.8 Big Data and Data Mining
21(1)
1.9 Ethical Guidelines for Statistical Practice
22(13)
Summary
24(1)
Glossary
24(1)
Supplementary Exercises
25(10)
Appendix 1.1 Getting Started with R and RStudio (MindTap Reader)
Appendix 1.2 Basic Data Manipulation in R (MindTap Reader)
Chapter 2 Descriptive Statistics: Tabular and Graphical Displays
35(68)
Statistics in Practice: Colgate-Palmolive Company
36(1)
2.1 Summarizing Data for a Categorical Variable
37(10)
Frequency Distribution
37(1)
Relative Frequency and Percent Frequency Distributions
38(1)
Using Excel to Construct a Frequency Distribution, a Relative Frequency Distribution, and a Percent Frequency Distribution
39(1)
Bar Charts and Pie Charts
40(2)
Using Excel to Construct a Bar Chart
42(5)
2.2 Summarizing Data for a Quantitative Variable
47(18)
Frequency Distribution
47(2)
Relative Frequency and Percent Frequency Distributions
49(1)
Using Excel to Construct a Frequency Distribution
50(1)
Dot Plot
51(1)
Histogram
52(2)
Using Excel's Recommended Charts Tool to Construct a Histogram
54(1)
Cumulative Distributions
55(1)
Stem-and-Leaf Display
56(9)
2.3 Summarizing Data for Two Variables Using Tables
65(10)
Crosstabulation
65(3)
Using Excel's PivotTable Tool to Construct a Crosstabulation
68(1)
Simpson's Paradox
69(6)
2.4 Summarizing Data for Two Variables Using Graphical Displays
75(10)
Scatter Diagram and Trendline
76(1)
Using Excel to Construct a Scatter Diagram and a Trendline
77(2)
Side-by-Side and Stacked Bar Charts
79(2)
Using Excel's Recommended Charts Tool to Construct Side-by-Side and Stacked Bar Charts
81(4)
2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays
85(13)
Creating Effective Graphical Displays
85(1)
Choosing the Type of Graphical Display
86(1)
Data Dashboards
86(2)
Data Visualization in Practice: Cincinnati Zoo and Botanical Garden
88(2)
Summary
90(1)
Glossary
91(1)
Key Formulas
92(1)
Supplementary Exercises
93(5)
Case Problem 1 Pelican Stores
98(1)
Case Problem 2 Movie Theater Releases
99(1)
Case Problem 3 Queen City
100(1)
Case Problem 4 Cut-Rate Machining, Inc.
100(3)
Appendix 2.1 Creating Tabular and Graphical Presentations with R (MindTap Reader)
Chapter 3 Descriptive Statistics: Numerical Measures
103(68)
Statistics in Practice: Small Fry Design
104(1)
3.1 Measures of Location
105(16)
Mean
105(2)
Median
107(1)
Mode
108(1)
Using Excel to Compute the Mean, Median, and Mode
109(1)
Weighted Mean
109(2)
Geometric Mean
111(1)
Using Excel to Compute the Geometric Mean
112(1)
Percentiles
113(1)
Quartiles
114(1)
Using Excel to Compute Percentiles and Quartiles
115(6)
3.2 Measures of Variability
121(9)
Range
122(1)
Interquartile Range
122(1)
Variance
122(2)
Standard Deviation
124(1)
Using Excel to Compute the Sample Variance and Sample Standard Deviation
125(1)
Coefficient of Variation
126(1)
Using Excel's Descriptive Statistics Tool
126(4)
3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
130(8)
Distribution Shape
130(1)
z-Scores
131(1)
Chebyshev's Theorem
132(1)
Empirical Rule
133(1)
Detecting Outliers
134(4)
3.4 Five-Number Summaries and Boxplots
138(6)
Five-Number Summary
138(1)
Boxplot
138(1)
Using Excel to Construct a Boxplot
139(1)
Comparative Analysis Using Boxplots
139(1)
Using Excel to Construct a Comparative Analysis Using Boxplots
140(4)
3.5 Measures of Association Between Two Variables
144(9)
Covariance
144(2)
Interpretation of the Covariance
146(2)
Correlation Coefficient
148(1)
Interpretation of the Correlation Coefficient
149(2)
Using Excel to Compute the Sample Covariance and Sample Correlation Coefficient
151(2)
3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness
153(12)
Summary
156(1)
Glossary
157(1)
Key Formulas
158(1)
Supplementary Exercises
159(6)
Case Problem 1 Pelican Stores
165(1)
Case Problem 2 Movie Theater Releases
166(1)
Case Problem 3 Business Schools of Asia-Pacific
167(1)
Case Problem 4 Heavenly Chocolates Website Transactions
167(2)
Case Problem 5 African Elephant Populations
169(2)
Appendix 3.1 Descriptive Statistics with R (MindTap Reader)
Chapter 4 Introduction to Probability
171(46)
Statistics in Practice: National Aeronautics and Space Administration
172(1)
4.1 Experiments, Counting Rules, and Assigning Probabilities
173(10)
Counting Rules, Combinations, and Permutations
174(4)
Assigning Probabilities
178(1)
Probabilities for the KP&L Project
179(4)
4.2 Events and Their Probabilities
183(4)
4.3 Some Basic Relationships of Probability
187(6)
Complement of an Event
187(1)
Addition Law
188(5)
4.4 Conditional Probability
193(8)
Independent Events
196(1)
Multiplication Law
196(5)
4.5 Bayes' Theorem
201(12)
Tabular Approach
204(2)
Summary
206(1)
Glossary
207(1)
Key Formulas
208(1)
Supplementary Exercises
208(5)
Case Problem 1 Hamilton County Judges
213(2)
Case Problem 2 Rob's Market
215(2)
Chapter 5 Discrete Probability Distributions
217(56)
Statistics in Practice: Voter Waiting Times in Elections
218(1)
5.1 Random Variables
218(3)
Discrete Random Variables
219(1)
Continuous Random Variables
220(1)
5.2 Developing Discrete Probability Distributions
221(5)
5.3 Expected Value and Variance
226(7)
Expected Value
226(1)
Variance
227(1)
Using Excel to Compute the Expected Value, Variance, and Standard Deviation
228(5)
5.4 Bivariate Distributions, Covariance, and Financial Portfolios
233(9)
A Bivariate Empirical Discrete Probability Distribution
233(3)
Financial Applications
236(3)
Summary
239(3)
5.5 Binomial Probability Distribution
242(10)
A Binomial Experiment
242(2)
Martin Clothing Store Problem
244(4)
Using Excel to Compute Binomial Probabilities
248(1)
Expected Value and Variance for the Binomial Distribution
249(3)
5.6 Poisson Probability Distribution
252(5)
An Example Involving Time Intervals
253(1)
An Example Involving Length or Distance Intervals
254(1)
Using Excel to Compute Poisson Probabilities
254(3)
5.7 Hypergeometric Probability Distribution
257(11)
Using Excel to Compute Hypergeometric Probabilities
259(2)
Summary
261(1)
Glossary
262(1)
Key Formulas
263(1)
Supplementary Exercises
264(4)
Case Problem 1 Go Bananas! Breakfast Cereal
268(1)
Case Problem 2 McNeil's Auto Mall
269(1)
Case Problem 3 Grievance Committee at Tuglar Corporation
270(1)
Case Problem 4 Sagittarius Casino
270(3)
Appendix 5.1 Discrete Probability Distributions with R (MindTap Reader)
Chapter 6 Continuous Probability Distributions
273(32)
Statistics in Practice: Procter & Gamble
274(1)
6.1 Uniform Probability Distribution
275(4)
Area as a Measure of Probability
276(3)
6.2 Normal Probability Distribution
279(14)
Normal Curve
279(2)
Standard Normal Probability Distribution
281(4)
Computing Probabilities for Any Normal Probability Distribution
285(1)
Grear Tire Company Problem
286(2)
Using Excel to Compute Normal Probabilities
288(5)
6.3 Exponential Probability Distribution
293(8)
Computing Probabilities for the Exponential Distribution
294(1)
Relationship Between the Poisson and Exponential Distributions
295(1)
Using Excel to Compute Exponential Probabilities
295(3)
Summary
298(1)
Glossary
298(1)
Key Formulas
298(1)
Supplementary Exercises
299(2)
Case Problem 1 Specialty Toys
301(1)
Case Problem 2 Gebhardt Electronics
302(3)
Appendix 6.1 Continuous Probability Distributions with R (MindTap Reader)
Chapter 7 Sampling and Sampling Distributions
305(50)
Statistics in Practice: The Food and Agriculture Organization
306(1)
7.1 The Electronics Associates Sampling Problem
307(1)
7.2 Selecting a Sample
308(8)
Sampling from a Finite Population
308(4)
Sampling from an Infinite Population
312(4)
7.3 Point Estimation
316(3)
Practical Advice
317(2)
7.4 Introduction to Sampling Distributions
319(3)
7.5 Sampling Distribution of x
322(9)
Expected Value of x
322(1)
Standard Deviation of x
322(2)
Form of the Sampling Distribution of x
324(1)
Sampling Distribution of x for the EAI Problem
324(1)
Practical Value of the Sampling Distribution of x
325(2)
Relationship Between the Sample Size and the Sampling Distribution of x
327(4)
7.6 Sampling Distribution of p
331(6)
Expected Value of p
332(1)
Standard Deviation of p
332(1)
Form of the Sampling Distribution of p
333(1)
Practical Value of the Sampling Distribution of p
333(4)
7.7 Other Sampling Methods
337(2)
Stratified Random Sampling
337(1)
Cluster Sampling
337(1)
Systematic Sampling
338(1)
Convenience Sampling
338(1)
Judgment Sampling
339(1)
7.8 Practical Advice: Big Data and Errors in Sampling
339(16)
Sampling Error
339(1)
Nonsampling Error
340(1)
Big Data
341(1)
Understanding What Big Data Is
342(1)
Implications of Big Data for Sampling Error
343(5)
Summary
348(1)
Glossary
348(1)
Key Formulas
349(1)
Supplementary Exercises
350(3)
Case Problem: Marion Dairies
353(2)
Appendix 7.1 Random Sampling with R (MindTap Reader)
Chapter 8 Interval Estimation
355(42)
Statistics in Practice: Food Lion
356(1)
8.1 Population Mean: σ Known
357(7)
Margin of Error and the Interval Estimate
357(4)
Using Excel
361(1)
Practical Advice
362(2)
8.2 Population Mean: σ Unknown
364(10)
Margin of Error and the Interval Estimate
365(3)
Using Excel
368(1)
Practical Advice
369(1)
Using a Small Sample
369(2)
Summary of Interval Estimation Procedures
371(3)
8.3 Determining the Sample Size
374(3)
8.4 Population Proportion
377(7)
Using Excel
378(2)
Determining the Sample Size
380(4)
8.5 Practical Advice: Big Data and Interval Estimation
384(8)
Big Data and the Precision of Confidence Intervals
384(1)
Implications of Big Data for Confidence Intervals
385(2)
Summary
387(1)
Glossary
388(1)
Key Formulas
388(1)
Supplementary Exercises
389(3)
Case Problem 1 Young Professional Magazine
392(1)
Case Problem 2 GULF Real Estate Properties
393(2)
Case Problem 3 Metropolitan Research, Inc.
395(2)
Appendix 8.1 Interval Estimation with R (MindTap Reader)
Chapter 9 Hypothesis Tests
397(48)
Statistics in Practice: John Morrell & Company
398(1)
9.1 Developing Null and Alternative Hypotheses
399(3)
The Alternative Hypothesis as a Research Hypothesis
399(1)
The Null Hypothesis as an Assumption to Be Challenged
400(1)
Summary of Forms for Null and Alternative Hypotheses
401(1)
9.2 Type I and Type II Errors
402(3)
9.3 Population Mean: σ Known
405(15)
One-Tailed Test
405(5)
Two-Tailed Test
410(3)
Using Excel
413(1)
Summary and Practical Advice
414(1)
Relationship Between Interval Estimation and Hypothesis Testing
415(5)
9.4 Population Mean: σ Unknown
420(8)
One-Tailed Test
421(1)
Two-Tailed Test
422(1)
Using Excel
423(2)
Summary and Practical Advice
425(3)
9.5 Population Proportion
428(6)
Using Excel
430(1)
Summary
431(3)
9.6 Practical Advice: Big Data and Hypothesis Testing
434(8)
Big Data, Hypothesis Testing, and p-Values
434(2)
Implications of Big Data in Hypothesis Testing
436(1)
Summary
437(1)
Glossary
438(1)
Key Formulas
438(1)
Supplementary Exercises
439(3)
Case Problem 1 Quality Associates, Inc.
442(1)
Case Problem 2 Ethical Behavior of Business Students at Bayview University
443(2)
Appendix 9.1 Hypothesis Testing with R (MindTap Reader)
Chapter 10 Inference About Means and Proportions with Two Populations
445(44)
Statistics in Practice: U.S. Food and Drug Administration
446(1)
10.1 Inferences About the Difference Between Two Population Means: σ1 and σ2 Known
447(9)
Interval Estimation of μ1 -- μ2
447(2)
Using Excel to Construct a Confidence Interval
449(2)
Hypothesis Tests About μ1 -- μ2
451(1)
Using Excel to Conduct a Hypothesis Test
452(2)
Practical Advice
454(2)
10.2 Inferences About the Difference Between Two Population Means: σ1 and σ2 Unknown
456(11)
Interval Estimation of μ1 -- μ2
457(1)
Using Excel to Construct a Confidence Interval
458(2)
Hypothesis Tests About μ1 -- μ2
460(2)
Using Excel to Conduct a Hypothesis Test
462(1)
Practical Advice
463(4)
10.3 Inferences About the Difference Between Two Population Means: Matched Samples
467(7)
Using Excel to Conduct a Hypothesis Test
469(5)
10.4 Inferences About the Difference Between Two Population Proportions
474(15)
Interval Estimation of p1 -- p2
474(2)
Using Excel to Construct a Confidence Interval
476(1)
Hypothesis Tests About p1 -- p2
477(2)
Using Excel to Conduct a Hypothesis Test
479(4)
Summary
483(1)
Glossary
483(1)
Key Formulas
483(2)
Supplementary Exercises
485(3)
Case Problem: Par, Inc.
488(1)
Appendix 10.1 Inferences About Two Populations with R (MindTap Reader)
Chapter 11 Inferences About Population Variances
489(28)
Statistics in Practice: U.S. Government Accountability Office
490(1)
11.1 Inferences About a Population Variance
491(12)
Interval Estimation
491(4)
Using Excel to Construct a Confidence Interval
495(1)
Hypothesis Testing
496(2)
Using Excel to Conduct a Hypothesis Test
498(5)
11.2 Inferences About Two Population Variances
503(10)
Using Excel to Conduct a Hypothesis Test
507(4)
Summary
511(1)
Key Formulas
511(1)
Supplementary Exercises
511(2)
Case Problem 1 Air Force Training Program
513(1)
Case Problem 2 Meticulous Drill & Reamer
514(3)
Appendix 11.1 Population Variances with R (MindTap Reader)
Chapter 12 Tests of Goodness of Fit, Independence, and Multiple Proportions
517(34)
Statistics in Practice: United Way
518(1)
12.1 Goodness of Fit Test
519(6)
Multinomial Probability Distribution
519(4)
Using Excel to Conduct a Goodness of Fit Test
523(2)
12.2 Test of Independence
525(9)
Using Excel to Conduct a Test of Independence
529(5)
12.3 Testing for Equality of Three or More Population Proportions
534(13)
A Multiple Comparison Procedure
537(2)
Using Excel to Conduct a Test of Multiple Proportions
539(4)
Summary
543(1)
Glossary
544(1)
Key Formulas
544(1)
Supplementary Exercises
544(3)
Case Problem 1 A Bipartisan Agenda for Change
547(1)
Case Problem 2 Fuentes Salty Snacks, Inc.
548(1)
Case Problem 3 Fresno Board Games
549(2)
Appendix 12.1 Chi-Square Tests with R (MindTap Reader)
Chapter 13 Experimental Design and Analysis of Variance
551(54)
Statistics in Practice: Burke, Inc.
552(1)
13.1 An Introduction to Experimental Design and Analysis of Variance
553(5)
Data Collection
554(2)
Assumptions for Analysis of Variance
556(1)
Analysis of Variance: A Conceptual Overview
556(2)
13.2 Analysis of Variance and the Completely Randomized Design
558(12)
Between-Treatments Estimate of Population Variance
559(1)
Within-Treatments Estimate of Population Variance
560(1)
Comparing the Variance Estimates: The F Test
561(1)
ANOVA Table
562(1)
Using Excel
563(1)
Testing for the Equality of k Population Means: An Observational Study
564(6)
13.3 Multiple Comparison Procedures
570(5)
Fisher's LSD
570(2)
Type I Error Rates
572(3)
13.4 Randomized Block Design
575(9)
Air Traffic Controller Stress Test
576(1)
ANOVA Procedure
577(1)
Computations and Conclusions
578(1)
Using Excel
579(5)
13.5 Factorial Experiment
584(17)
ANOVA Procedure
585(1)
Computations and Conclusions
586(3)
Using Excel
589(4)
Summary
593(1)
Glossary
594(1)
Key Formulas
595(1)
Completely Randomized Design
595(1)
Multiple Comparison Procedures
596(1)
Randomized Block Design
596(1)
Factorial Experiment
596(1)
Supplementary Exercises
596(5)
Case Problem 1 Wentworth Medical Center
601(1)
Case Problem 2 Compensation for Sales Professionals
602(1)
Case Problem 3 TourisTopia Travel
603(2)
Appendix 13.1 Analysis of Variance with R (MindTap Reader)
Chapter 14 Simple Linear Regression
605(80)
Statistics in Practice: walmart.com
606(1)
14.1 Simple Linear Regression Model
607(3)
Regression Model and Regression Equation
607(2)
Estimated Regression Equation
609(1)
14.2 Least Squares Method
610(11)
Using Excel to Construct a Scatter Diagram, Display the Estimated Regression Line, and Display the Estimated Regression Equation
614(7)
14.3 Coefficient of Determination
621(8)
Using Excel to Compute the Coefficient of Determination
625(1)
Correlation Coefficient
626(3)
14.4 Model Assumptions
629(2)
14.5 Testing for Significance
631(8)
Estimate of σ2
631(1)
T Test
632(1)
Confidence Interval for β1
633(1)
F Test
634(2)
Some Cautions About the Interpretation of Significance Tests
636(3)
14.6 Using the Estimated Regression Equation for Estimation and Prediction
639(7)
Interval Estimation
640(1)
Confidence Interval for the Mean Value of y
640(1)
Prediction Interval for an Individual Value of y
641(5)
14.7 Excel's Regression Tool
646(5)
Using Excel's Regression Tool for the Armand's Pizza Parlors Example
646(1)
Interpretation of Estimated Regression Equation Output
647(1)
Interpretation of ANOVA Output
648(1)
Interpretation of Regression Statistics Output
649(2)
14.8 Residual Analysis: Validating Model Assumptions
651(12)
Residual Plot Against x
652(1)
Residual Plot Against y
653(2)
Standardized Residuals
655(2)
Using Excel to Construct a Residual Plot
657(3)
Normal Probability Plot
660(3)
14.9 Outliers and Influential Observations
663(7)
Detecting Outliers
663(2)
Detecting Influential Observations
665(5)
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression
670(8)
Summary
671(1)
Glossary
671(1)
Key Formulas
672(2)
Supplementary Exercises
674(4)
Case Problem 1 Measuring Stock Market Risk
678(1)
Case Problem 2 U.S. Department of Transportation
679(1)
Case Problem 3 Selecting a Point-and-Shoot Digital Camera
680(1)
Case Problem 4 Finding the Best Car Value
681(1)
Case Problem 5 Buckeye Creek Amusement Park
682(1)
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
683(1)
Appendix 14.2 A Test for Significance Using Correlation
684(1)
Appendix 14.3 Simple Linear Regression with R (MindTap Reader)
Chapter 15 Multiple Regression
685(49)
Statistics in Practice: International Paper
686(1)
15.1 Multiple Regression Model
687(1)
Regression Model and Regression Equation
687(1)
Estimated Multiple Regression Equation
687(1)
15.2 Least Squares Method
688(10)
An Example: Butler Trucking Company
689(2)
Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation
691(2)
Note on Interpretation of Coefficients
693(5)
15.3 Multiple Coefficient of Determination
698(2)
15.4 Model Assumptions
700(2)
15.5 Testing for Significance
702(6)
F Test
702(2)
T Test
704(1)
Multicollinearity
705(3)
15.6 Using the Estimated Regression Equation for Estimation and Prediction
708(2)
15.7 Categorical Independent Variables
710(8)
An Example: Johnson Filtration, Inc.
710(2)
Interpreting the Parameters
712(1)
More Complex Categorical Variables
713(5)
15.8 Residual Analysis
718(4)
Residual Plot Against y
718(1)
Standardized Residual Plot Against y
719(3)
15.9 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression
722(7)
Summary
723(1)
Glossary
723(1)
Key Formulas
724(1)
Supplementary Exercises
725(4)
Case Problem 1 Consumer Research, Inc.
729(1)
Case Problem 2 Predicting Winnings for NASCAR Drivers
730(2)
Case Problem 3 Finding the Best Car Value
732(2)
Appendix 15.1 Multiple Linear Regression with R (MindTap Reader)
Appendix A References and Bibliography 734(2)
Appendix B Tables 736(11)
Appendix C Summation Notation 747(2)
Appendix D Answers to Even-Numbered Exercises (MindTap Reader)
Appendix E Microsoft Excel and Tools for Statistical Analysis 749(8)
Appendix F Microsoft Excel Online and Tools for Statistical Analysis 757(8)
Index 765
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.