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A direct approach to business statistics, ordered in a signature step-by-step framework.
Business Statistics uses a direct approach that consistently presents concepts and techniques in way that benefits readers of all mathematical backgrounds. This text also contains engaging business examples to show the relevance of business statistics in action.
The eighth edition provides even more learning aids to help readers understand the material.
David F. Groebner is Professor Emeritus of Production Management in the College of Business and Economics at Boise State University. He has bachelor’s and master’s degrees in engineering and a Ph.D. in business administration. After working as an engineer, he has taught statistics and related subjects for 27 years. In addition to writing textbooks and academic papers, he has worked extensively with both small and large organizations, including Hewlett-Packard, Boise Cascade, Albertson’s, and Ore-Ida. He has worked with numerous government agencies, including Boise City and the U.S. Air Force.
Patrick W. Shannon, Ph.D. is Dean and Professor of Supply Chain Operations Management in the College of Business and Economics at Boise State University. In addition to his administrative responsibilities, he has taught graduate and undergraduate courses in business statistics, quality management, and production and operations management. In addition, Dr. Shannon has lectured and consulted in the statistical analysis and quality management areas for more than 20 years. Among his consulting clients are Boise Cascade Corporation, Hewlett-Packard, PowerBar, Inc., Potlatch Corporation, Woodgrain Millwork, Inc., J.R. Simplot Company, Zilog Corporation, and numerous other public- and private-sector organizations. Professor Shannon has co-authored several university-level textbooks and has published numerous articles in such journals as Business Horizons, Interfaces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and Journal of Marketing Research. He obtained B.S. and M.S. degrees from the University of Montana and a Ph.D. in statistics and quantitative methods from the University of Oregon.
Phillip C. Fry is a professor in the College of Business and Economics at Boise State University, where he has taught since 1988. Phil received his B.A. and M.B.A. degrees from the University of Arkansas and his M.S. and Ph.D. degrees from Louisiana State University. His teaching and research interests are in the areas of business statistics, production management, and quantitative business modeling. In addition to his academic responsibilities, Phil has consulted with and provided training to small and large organizations, including Boise Cascade Corporation, Hewlett-Packard Corporation, the J.R. Simplot Company, United Water of Idaho, Woodgrain Millwork, Inc., Boise City, and Micron Electronics. Phil spends most of his free time with his wife, Susan, and his four children, Phillip Alexander, Alejandra Johanna, and twins Courtney Rene and Candace Marie.
Chapter 1: The Where, Why, and How of Data Collection Chapter 2: Graphs, Charts, and Tables—Describing Your Data Chapter 3: Describing Data Using Numerical Measures
Special Review Section
Chapter 4: Using Probability and Probability Distributions Chapter 5: Discrete Probability Distributions Chapter 6: Introduction to Continuous Probability Distributions Chapter 7: Introduction to Sampling Distributions Chapter 8: Estimating Single Population Parameters Chapter 9: Introduction to Hypothesis Testing Chapter 10: Estimation and Hypothesis Testing for Two Population Parameters Chapter 11: Hypothesis Tests and Estimation for Population Variances Chapter 12: Analysis of Variance
Special Review Section
Chapter 13: Goodness-of-Fit Tests and Contingency Analysis Chapter 14: Introduction to Linear Regression and Correlation Analysis Chapter 15: Multiple Regression Analysis and Model Building Chapter 16: Analyzing and Forecasting Time-Series Data Chapter 17: Introduction to Nonparametric Statistics Chapter 18: Introduction to Quality and Statistical Process Control