This text focuses on improving business processes by using all the important statistical techniques needed to facilitate effective decision making. The text follows a traditional approach and topic organization that is appropriate for most introductory business statistics courses, but also incorporates a more modern approach that infuses quality and emphasizes process improvement throughout. The authors' highest priority is developing a text that offers clear and understandable explanations of concepts and providing case studies and examples that reflect real applications of statistics today.
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Verlagsort
Verlagsgruppe
McGraw-Hill Education - Europe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Maße
Höhe: 279 mm
Breite: 221 mm
Dicke: 56 mm
Gewicht
ISBN-13
978-0-256-19386-2 (9780256193862)
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Schweitzer Klassifikation
Bruce L. Bowerman is professor of decision sciences at Miami University in Oxford, Ohio. He received his Ph.D. degree in statistics from Iowa State University in 1974, and he has over 40 years of experience teaching basic statistics, regression analysis, time series forecasting, survey sampling, and design of experiments to both undergraduate and graduate students. In 1987, Professor Bowerman received an Outstanding Teaching award from the Miami University senior class, and in 1992 he received an Effective Educator award from the Richard T. Farmer School of Business Administration. Together with Richard T. OConnell, Professor Bowerman has written 16 textbooks. These include Forecasting and Time Series: An Applied Approach; Forecasting, Time Series, and Regression: An Applied Approach (also coauthored with Anne B. Koehler); and Linear Statistical Models: An Applied Approach. The fi rst edition of Forecasting and Time Series earned an Outstanding Academic Book award from Choice magazine. Professor Bowerman has also published a number of articles in applied stochastic processes, time series forecasting, and statistical education. In his spare time, Professor Bowerman enjoys watching movies and sports, playing tennis, and designing houses.
Richard T. OConnell is associate professor of decision sciences at Miami University in Oxford, Ohio. He has more than 35 years of experience teaching basic statistics, statistical quality control and process improvement, regression analysis, time series forecasting, and design of experiments to both undergraduate and graduate business students. He also has extensive consulting experience and has taught workshops dealing with statistical process control and process improvement for a variety of companies in the Midwest. In 2000, Professor OConnell received an Effective Educator award from the Richard T. Farmer School of Business Administration. Together with Bruce L. Bowerman, he has written 16 textbooks. These include Forecasting and Time Series: An Applied Approach; Forecasting, Time Series, and Regression: An Applied Approach (also coauthored with Anne B. Koehler); and Linear Statistical Models: An Applied Approach. Professor OConnell has published a number of articles in the area of innovative statistical education. He is one of the first college instructors in the United States to integrate statistical process control and process improvement methodology into his basic business statistics course. He (with Professor Bowerman) has written several articles advocating this approach. He has also given presentations on this subject at meetings such as the Joint Statistical Meetings of the American Statistical Association and the Workshop on Total Quality Management: Developing Curricula and Research Agendas (sponsored by the Production and Operations Management Society). Professor OConnell received an M.S. degree in Decision Sciences from Northwestern University in 1973, and he is currently a member of both the Decision Sciences Institute and the American Statistical Association. In his spare time, Professor OConnell enjoys fishing, collecting 1950s and 1960s rock music, and following the Green Bay Packers and Purdue University sports.
1. An Introduction to Business Statistics2. Descriptive Statistics3. Probability and Random Variables4. Probability Distributions5. Sampling Distributions6. Confidence Intervals7. Hypothesis Testing8. Statistical Inferences Based on Two Samples9. Introduction to Quality Improvement10. Process Improvement Using Control Charts11. Simple Linear Regression Analysis12. Multiple Regression13. Model Building and Model Diagnostics14. Time Series Forecasting15. Experimental Design and Analysis of Variance16. Chi-Square TestsAppendix A. Statistical TablesAppendix B. Using Matrix Algebra to Perform Regression Calculation