ISE Business Statistics and Analytics in Practice

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. O’Connell, 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. O’Connell 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 O’Connell 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 O’Connell 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 O’Connell 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 O’Connell enjoys fishing, collecting 1950s’ and 1960s’ rock music, and following the Green Bay Packers and Purdue University sports. Emily S. Murphree Emily S. Murphree is associate professor of statistics in the Department of Mathematics and Statistics at Miami University in Oxford, Ohio. She received her Ph.D. degree in statistics from the University of North Carolina and does research in applied probability. Professor Murphree received Miami’s College of Arts and Science Distinguished Educator Award in 1998. In 1996, she was named one of Oxford’s Citizens of the Year for her work with Habitat for Humanity and for organizing annual Sonia Kovalevsky Mathematical Sciences Days for area high school girls. Her enthusiasm for hiking in wilderness areas of the West motivated her current research on estimating animal population sizes.

Chapter 1 An Introduction to Business Statistics and Analytics
Chapter 2 Descriptive Statistics and Analytics: Tabular and Graphical Methods
Chapter 3 Descriptive Statistics and Analytics: Numerical Methods
Chapter 4 Probability and Probability Models
Chapter 5 Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes’, and Ensemble Estimates
Chapter 6 Discrete Random Variables
Chapter 7 Continuous Random Variables
Chapter 8 Sampling Distributions
Chapter 9 Confidence Intervals
Chapter 10 Hypothesis Testing
Chapter 11 Statistical Inferences Based on Two Samples
Chapter 12 Experimental Design and Analysis of Variance
Chapter 13 Chi-Square Tests
Chapter 14 Simple Linear Regression Analysis
Chapter 15 Multiple Regression and Model Building
Chapter 16 Predictive Analytics II: Logis¬tic Regression, Discriminate Analysis, and Neural Networks
Chapter 17 Time Series Forecasting and Index Numbers
Chapter 18 Nonparametric Methods
Chapter 19 Decision Theory
Chapter 20 (Online) Process Improvement Using Control Charts for Website
Appendix A Statistical Tables
Appendix B (Online) Chapter by Chapter MegaStat Appendices