圖書描述
1. A modern, practical and unique framework for teaching an introductory course in Business Statistics.
2. Employs realistic examples, continuing case studies and a business improvement theme to teach the material.
3. Features more concise and lucid explanations, an improved topic flow and a sensible use of the best and most compelling examples.
2. Employs realistic examples, continuing case studies and a business improvement theme to teach the material.
3. Features more concise and lucid explanations, an improved topic flow and a sensible use of the best and most compelling examples.
著者信息
作者簡介
Bruce L. Bowerman
現職：Miami University
Anne M. Drougas
現職：Dominican University
William M. Duckworth
現職：Creighton University
Amy G. Froelich
現職：Iowa State University
Ruth M. Hummel
現職：JMP
Kyle B. Moninger
現職：Bowling Green State University
Patrick J. Schur
現職：Miami University
Bruce L. Bowerman
現職：Miami University
Anne M. Drougas
現職：Dominican University
William M. Duckworth
現職：Creighton University
Amy G. Froelich
現職：Iowa State University
Ruth M. Hummel
現職：JMP
Kyle B. Moninger
現職：Bowling Green State University
Patrick J. Schur
現職：Miami University
圖書目錄
Ch 1 An Introduction to Business Statistics and Analytics
Ch 2 Descriptive Statistics and Analytics: Tabular and Graphical Methods
Ch 3 Descriptive Statistics and Analytics: Numerical Methods
Ch 4 Probability and Probability Models
Ch 5 Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes’, and Ensemble Estimates
Ch 6 Discrete Random Variables
Ch 7 Continuous Random Variables
Ch 8 Sampling Distributions
Ch 9 Confidence Intervals
Ch10 Hypothesis Testing
Ch11 Statistical Inferences Based on Two Samples
Ch12 Experimental Design and Analysis of Variance
Ch13 Chi-Square Tests
Ch14 Simple Linear Regression Analysis
Ch15 Multiple Regression and Model Building
Ch16 Predictive Analytics II: Logis¬tic Regression, Discriminate Analysis, and Neural Networks
Ch17 Time Series Forecasting and Index Numbers
Ch18 Nonparametric Methods
Ch19 Decision Theory
Ch20 (Online) Process Improvement Using Control Charts for Website
Appendix A Statistical Tables
Appendix B (Online) Chapter by Chapter MegaStat
Ch 2 Descriptive Statistics and Analytics: Tabular and Graphical Methods
Ch 3 Descriptive Statistics and Analytics: Numerical Methods
Ch 4 Probability and Probability Models
Ch 5 Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes’, and Ensemble Estimates
Ch 6 Discrete Random Variables
Ch 7 Continuous Random Variables
Ch 8 Sampling Distributions
Ch 9 Confidence Intervals
Ch10 Hypothesis Testing
Ch11 Statistical Inferences Based on Two Samples
Ch12 Experimental Design and Analysis of Variance
Ch13 Chi-Square Tests
Ch14 Simple Linear Regression Analysis
Ch15 Multiple Regression and Model Building
Ch16 Predictive Analytics II: Logis¬tic Regression, Discriminate Analysis, and Neural Networks
Ch17 Time Series Forecasting and Index Numbers
Ch18 Nonparametric Methods
Ch19 Decision Theory
Ch20 (Online) Process Improvement Using Control Charts for Website
Appendix A Statistical Tables
Appendix B (Online) Chapter by Chapter MegaStat
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