Bowerman 9e covers both standard business statistics and business analytics topics and provides them in a clear presentation that is organized so that business analytics topics may be used or not used. Bowerman provides a continuous case throughout chapters and business analytics topics that allow students to use data for a more applied and practical approach. Featuring Connect, Smartbook, Guided examples, Algorithmic Problems and a Business Statistics, Math and Excel prep component, Bowerman is a perfect fit for the instructor who wants a Business Stats with Business Analytics focus.
Bowerman 9e covers both standard business statistics and business analytics topics and provides them in a clear presentation that is organized so that business analytics topics may be used or not used. Bowerman provides a continuous case throughout chapters and business analytics topics that allow students to use data for a more applied and practical approach. Featuring Connect, Smartbook, Guided examples, Algorithmic Problems and a Business Statistics, Math and Excel prep component, Bowerman is a perfect fit for the instructor who wants a Business Stats with Business Analytics focus.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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