Business Statistics and Analytics in Practice: 2025 Release ISE
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
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.
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