The 2025 release of Business Statistics and Analytics in Practice offers a comprehensive approach to teaching business statistics and analytics. It covers essential topics like probability modeling, regression, and time series, while seamlessly integrating modern tools such as data mining and predictive analytics. Real-world case studies and early introductions to advanced visualizations enhance practical learning, with Business Improvement conclusions-highlighted in yellow and marked by BI icons-demonstrating how statistical analyses lead to actionable business decisions. With hands-on…mehr
The 2025 release of Business Statistics and Analytics in Practice offers a comprehensive approach to teaching business statistics and analytics. It covers essential topics like probability modeling, regression, and time series, while seamlessly integrating modern tools such as data mining and predictive analytics. Real-world case studies and early introductions to advanced visualizations enhance practical learning, with Business Improvement conclusions-highlighted in yellow and marked by BI icons-demonstrating how statistical analyses lead to actionable business decisions. With hands-on experience using Excel, MegaStat, Minitab, JMP, and R, students are equipped with the skills needed to thrive in today's data-driven business world.
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.
Inhaltsangabe
1. An Introduction to Business Statistics and Analytics 2. Descriptive Statistics and Analytics: Tabular and Graphical Methods 3. Descriptive Statistics and Analytics: Numerical Method 4. Probability and Probability Models 5. Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes', and Ensemble Estimates 6. Discrete Random Variables 7. Continuous Random Variables 8. Sampling Distributions 9. Confidence Intervals 10. Hypothesis Testing 11. Statistical Inferences Based on Two Samples 12. Experimental Design and Analysis of Variance 13. Chi-Square Tests 14. Simple Linear Regression Analysis 15. Multiple Regression and Model Building 16. Predictive Analytics II: Logistic Regression, Discriminate Analysis, and Neural Networks 17. Time Series Forecasting and Index Numbers 18. Nonparametric Methods 19. Decision Theory 20. (Online) Process Improvement Using Control Charts for Website Appendix A: Statistical Tables Appendix B: (Online) Chapter by Chapter MegaStat Appendices
1. An Introduction to Business Statistics and Analytics 2. Descriptive Statistics and Analytics: Tabular and Graphical Methods 3. Descriptive Statistics and Analytics: Numerical Method 4. Probability and Probability Models 5. Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes', and Ensemble Estimates 6. Discrete Random Variables 7. Continuous Random Variables 8. Sampling Distributions 9. Confidence Intervals 10. Hypothesis Testing 11. Statistical Inferences Based on Two Samples 12. Experimental Design and Analysis of Variance 13. Chi-Square Tests 14. Simple Linear Regression Analysis 15. Multiple Regression and Model Building 16. Predictive Analytics II: Logistic Regression, Discriminate Analysis, and Neural Networks 17. Time Series Forecasting and Index Numbers 18. Nonparametric Methods 19. Decision Theory 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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