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Essentials of Statistics for Business & Economics 9th Edition

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David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran

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978-0357045435

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9th edition | 2019

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ISBN: 978-0357045435 SKU: BS1376 Categories: Economics, Mathematics Tags: Economics, Essentials of Statistics, Essentials of Statistics for Business, Essentials of Statistics for Business & Economics, Essentials of Statistics for Business & Economics 9th Edition, Statistics
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Description

Essentials of Statistics for Business & Economics 9th Edition

Essentials of Statistics for Business & Economics Ninth Edition:

Discover how statistical information impacts decisions in today’s 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.

Additional ISBNs:

∗ eText ISBN: 0357118197, 978-0357118191, 9780357118191

  • See additional information on the Amazon.

More Details

Contents

Essentials of Statistics for Business & Economics 9th Edition:

Brief Contents
Contents
About the Authors
Preface
Chapter 1: Data and Statistics
1.1 Applications in Business and Economics
1.2 Data
1.3 Data Sources
1.4 Descriptive Statistics
1.5 Statistical Inference
1.6 Analytics
1.7 Big Data and Data Mining
1.8 Computers and Statistical Analysis
1.9 Ethical Guidelines for Statistical Practice
Summary
Glossary
Supplementary Exercises
Chapter 1 Appendix
Chapter 2: Descriptive Statistics: Tabular and Graphical Displays
2.1 Summarizing Data for a Categorical Variable
2.2 Summarizing Data for a Quantitative Variable
2.3 Summarizing Data for Two Variables Using Tables
2.4 Summarizing Data for Two Variables Using Graphical Displays
2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Movie Theater Releases
Case Problem 3: Queen City
Case Problem 4: Cut-Rate Machining, Inc.
Chapter 2 Appendix
Chapter 3: Descriptive Statistics: Numerical Measures
3.1 Measures of Location
3.2 Measures of Variability
3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
3.4 Five-Number Summaries and Boxplots
3.5 Measures of Association between Two Variables
3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Movie Theater Releases
Case Problem 3: Business Schools of Asia-Pacific
Case Problem 4: Heavenly Chocolates Website Transactions
Case Problem 5: African Elephant Populations
Chapter 3 Appendix
Chapter 4: Introduction to Probability
4.1 Random Experiments, Counting Rules, and Assigning Probabilities
4.2 Events and Their Probabilities
4.3 Some Basic Relationships of Probability
4.4 Conditional Probability
4.5 Bayes’ Theorem
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Hamilton County Judges
Case Problem 2: Rob’s Market
Chapter 5: Discrete Probability Distributions
5.1 Random Variables
5.2 Developing Discrete Probability Distributions
5.3 Expected Value and Variance
5.4 Bivariate Distributions, Covariance, and Financial Portfolios
5.6 Poisson Probability Distribution
5.7 Hypergeometric Probability Distribution
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Go Bananas! Breakfast Cereal
Case Problem 2: McNeil’s Auto Mall
Case Problem 3: Grievance Committee at Tuglar Corporation
Chapter 5 Appendix
Chapter 6: Continuous Probability Distributions
6.1 Uniform Probability Distribution
6.2 Normal Probability Distribution
6.3 Normal Approximation of Binomial Probabilities
6.4 Exponential Probability Distribution
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Specialty Toys
Case Problem 2: Gebhardt Electronics
Chapter 6 Appendix
Chapter 7: Sampling and Sampling Distributions
7.1 The Electronics Associates Sampling Problem
7.2 Selecting a Sample
7.3 Point Estimation
7.4 Introduction to Sampling Distributions
7.5 Sampling Distribution of x
7.6 Sampling Distribution of p
7.7 Properties of Point Estimators
7.8 Other Sampling Methods
7.9 Big Data and Standard Errors of Sampling Distributions
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Marion Dairies
Chapter 7 Appendix
Chapter 8: Interval Estimation
8.1 Population Mean: o Known
8.2 Population Mean: o Unknown
8.3 Determining the Sample Size
8.5 Big Data and Confidence Intervals
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Young Professional Magazine
Case Problem 2: Gulf Real Estate Properties
Case Problem 3: Metropolitan Research, Inc.
Chapter 8 Appendix
Chapter 9: Hypothesis Tests
9.1 Developing Null and Alternative Hypotheses
9.2 Type I and Type II Errors
9.3 Population Mean: o Known
9.4 Population Mean: o Unknown
9.5 Population Proportion
9.6 Hypothesis Testing and Decision Making
9.8 Determining the Sample Size for a Hypothesis Test about a Population Mean
9.9 Big Data and Hypothesis Testing
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Quality Associates, Inc.
Case Problem 2: Ethical Behavior of Business Students at Bayview University
Chapter 9 Appendix
Chapter 10: Inference about Means and Proportions with Two Populations
10.1 Inferences about the Difference between Two Population Means: o1 and o2 Known
10.2 Inferences about the Difference between Two Population Means: o1 and o2 Unknown
10.3 Inferences about the Difference between Two Population Means: Matched Samples
10.4 Inferences about the Difference between Two Population Proportions
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Par, Inc.
Chapter 10 Appendix
Chapter 11: Inferences about Population Variances
11.1 Inferences about a Population Variance
11.2 Inferences about Two Population Variances
Summary
Key Formulas
Supplementary Exercises
Case Problem 1: Air Force Training Program
Case Problem 2: Meticulous Drill & Reamer
Chapter 11 Appendix
Chapter 12: Comparing Multiple Proportions, Test of Independence and Goodness of Fit
12.1 Testing the Equality of Population Proportions for Three or More Populations
12.2 Test of Independence
12.3 Goodness of Fit Test
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: A Bipartisan Agenda for Change
Case Problem 2: Fuentes Salty Snacks, Inc.
Case Problem 3: Fresno Board Games
Chapter 12 Appendix
Chapter 13: Experimental Design and Analysis of Variance
13.1 An Introduction to Experimental Design and Analysis of Variance
13.2 Analysis of Variance and the Completely Randomized Design
13.3 Multiple Comparison Procedures
13.4 Randomized Block Design
13.5 Factorial Experiment
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Wentworth Medical Center
Case Problem 2: Compensation for Sales Professionals
Case Problem 3: TourisTopia Travel
Chapter 13 Appendix
Chapter 14: Simple Linear Regression
14.1 Simple Linear Regression Model
14.2 Least Squares Method
14.3 Coefficient of Determination
14.4 Model Assumptions
14.5 Testing for Significance
14.6 Using the Estimated Regression Equation for Estimation and Prediction
14.7 Computer Solution
14.8 Residual Analysis: Validating Model Assumptions
14.9 Residual Analysis: Outliers and Influential Observations
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Measuring Stock Market Risk
Case Problem 2: U.S. Department of Transportation
Case Problem 3: Selecting a Point-and-Shoot Digital Camera
Case Problem 4: Finding the Best Car Value
Case Problem 5: Buckeye Creek Amusement Park
Chapter 14 Appendix
Chapter 15: Multiple Regression
15.1 Multiple Regression Model
15.2 Least Squares Method
15.3 Multiple Coefficient of Determination
15.4 Model Assumptions
15.5 Testing for Significance
15.6 Using the Estimated Regression Equation for Estimation and Prediction
15.7 Categorical Independent Variables
15.8 Residual Analysis
15.9 Logistic Regression
15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Consumer Research, Inc.
Case Problem 2: Predicting Winnings for NASCAR Drivers
Case Problem 3: Finding the Best Car Value
Chapter 15 Appendix
Appendix A – References and Bibliography
Appendix B – Tables
Appendix C – Summation Notation
Appendix E – Microsoft Excel 2016 and Tools for Statistical Analysis
Appendix F – Computing p-Values with JMP and Excel
Index

About the Author

David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran

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 college’s 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 ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

Jeffrey D. Camm is the Inmar Presidential Chair and senior associate dean of business analytics programs 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, he 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 many professional journals, including Science, Management Science, Operations Research and the INFORMS Journal on Applied Analytics. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati, and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, Dr. Camm has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016 Dr. Camm received the George E. Kimball Medal for service to the operations research profession, and in 2017 he was named an INFORMS fellow.

James J. Cochran is associate dean for research, a professor of applied statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has served as 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 in numerous journals, including 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. Dr. Cochran received the 2008 INFORMS prize for the Teaching of Operations Research Practice, the 2010 Mu Sigma Rho Statistical Education Award and the 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005 and was named a fellow of the American Statistical Association in 2011 and a fellow of INFORMS in 2017. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In addition, he received the INFORMS President’s Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has also served as an operations research or statistics consultant to numerous companies and not-for-profit organizations.

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