Business statistics in practice : using modeling, data, and analytics

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Cover Title page Copyright page ABOUT THE AUTHORS AUTHORS’ PREVIEW WHAT SOFTWARE IS AVAILABLE ACKNOWLEDGMENTS DEDICATION BRIEF CONTENTS CONTENTS Half-title page Chapter 01 An Introduction to Business Statistics and Analytics 1.1 Data 1.2 Data Sources, Data Warehousing and Big Data 1.3 Populations, Samples, and Traditional Statistics 1.4 Random Sampling, Three Case Studies That Illustrate Statistical Inference, and Statistical Modeling 1.5 Business Analytics and Data Mining (Optional) 1.6 Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional) 1.7 Stratified Random, Cluster, and Systematic Sampling (Optional) 1.8 More about Surveys and Errors in Survey Sampling (Optional) Appendix 1.1 ■ Getting Started with Excel Appendix 1.2 ■ Getting Started with MegaStat Appendix 1.3 ■ Getting Started with Minitab Chapter 02 Descriptive Statistics: Tabular and Graphical Methods and Descriptive Analytics 2.1 Graphically Summarizing Qualitative Data 2.2 Graphically Summarizing Quantitative Data 2.3 Dot Plots 2.4 Stem-and-Leaf Displays 2.5 Contingency Tables (Optional) 2.6 Scatter Plots (Optional) 2.7 Misleading Graphs and Charts (Optional) 2.8 Descriptive Analytics (Optional) Appendix 2.1 ■ Tabular and Graphical Methods Using Excel Appendix 2.2 ■ Tabular and Graphical Methods Using MegaStat Appendix 2.3 ■ Tabular and Graphical Methods Using Minitab Chapter 03 Descriptive Statistics: Numerical Methods and Some Predictive Analytics PART 1 Numerical Methods of Descriptive Statistics 3.1 Describing Central Tendency 3.2 Measures of Variation 3.3 Percentiles, Quartiles, and Box-and-Whiskers Displays 3.4 Covariance, Correlation, and the Least Squares Line (Optional) 3.5 Weighted Means and Grouped Data (Optional) 3.6 The Geometric Mean (Optional) PART 2 Some Predictive Analytics (Optional) 3.7 Decision Trees: Classification Trees and Regression Trees (Optional) 3.8 Cluster Analysis and Multidimensional Scaling (Optional) 3.9 Factor Analysis (Optional and Requires Section 3.4) 3.10 Association Rules (Optional) Appendix 3.1 ■ Numerical Descriptive Statistics Using Excel Appendix 3.2 ■ Numerical Descriptive Statistics Using MegaStat Appendix 3.3 ■ Numerical Descriptive Statistics Using Minitab Appendix 3.4 ■ Analytics Using JMP Chapter 04 Probability and Probability Models 4.1 Probability, Sample Spaces, and Probability Models 4.2 Probability and Events 4.3 Some Elementary Probability Rules 4.4 Conditional Probability and Independence 4.5 Bayes’ Theorem (Optional) 4.6 Counting Rules (Optional) Chapter 05 Discrete Random Variables 5.1 Two Types of Random Variables 5.2 Discrete Probability Distributions 5.3 The Binomial Distribution 5.4 The Poisson Distribution (Optional) 5.5 The Hypergeometric Distribution (Optional) 5.6 Joint Distributions and the Covariance (Optional) Appendix 5.1 ■ Binomial, Poisson, and Hypergeometric Probabilities Using Excel Appendix 5.2 ■ Binomial, Poisson, and Hypergeometric Probabilities Using MegaStat Appendix 5.3 ■ Binomial, Poisson, and Hypergeometric Probabilities Using Minitab Chapter 06 Continuous Random Variables 6.1 Continuous Probability Distributions 6.2 The Uniform Distribution 6.3 The Normal Probability Distribution 6.4 Approximating the Binomial Distribution by Using the Normal Distribution (Optional) 6.5 The Exponential Distribution (Optional) 6.6 The Normal Probability Plot (Optional) Appendix 6.1 ■ Normal Distribution Using Excel Appendix 6.2 ■ Normal Distribution Using MegaStat Appendix 6.3 ■ Normal Distribution Using Minitab Chapter 07 Sampling Distributions 7.1 The Sampling Distribution of the Sample Mean 7.2 The Sampling Distribution of the Sample Proportion 7.3 Derivation of the Mean and the Variance of the Sample Mean (Optional) Chapter 08 Confidence Intervals 8.1 z-Based Confidence Intervals for a Population Mean: s Known 8.2 t-Based Confidence Intervals for a Population Mean: s Unknown 8.3 Sample Size Determination 8.4 Confidence Intervals for a Population Proportion 8.5 Confidence Intervals for Parameters of Finite Populations (Optional) Appendix 8.1 ■ Confidence Intervals Using Excel Appendix 8.2 ■ Confidence Intervals Using MegaStat Appendix 8.3 ■ Confidence Intervals Using Minitab Chapter 09 Hypothesis Testing 9.1 The Null and Alternative Hypotheses and Errors in Hypothesis Testing 9.2 z Tests about a Population Mean: s Known 9.3 t Tests about a Population Mean: s Unknown 9.4 z Tests about a Population Proportion 9.5 Type II Error Probabilities and Sample Size Determination (Optional) 9.6 The Chi-Square Distribution 9.7 Statistical Inference for a Population Variance (Optional) Appendix 9.1 ■ One-Sample Hypothesis Testing Using Excel Appendix 9.2 ■ One-Sample Hypothesis Testing Using MegaStat Appendix 9.3 ■ One-Sample Hypothesis Testing Using Minitab Chapter 10 Statistical Inferences Based on Two Samples 10.1 Comparing Two Population Means by Using Independent Samples 10.2 Paired Difference Experiments 10.3 Comparing Two Population Proportions by Using Large, Independent Samples 10.4 The F Distribution 10.5 Comparing Two Population Variances by Using Independent Samples Appendix 10.1 ■ Two-Sample Hypothesis Testing Using Excel Appendix 10.2 ■ Two-Sample Hypothesis Testing Using MegaStat Appendix 10.3 ■ Two-Sample Hypothesis Testing Using Minitab Chapter 11 Experimental Design and Analysis of Variance 11.1 Basic Concepts of Experimental Design 11.2 One-Way Analysis of Variance 11.3 The Randomized Block Design 11.4 Two-Way Analysis of Variance Appendix 11.1 ■ Experimental Design and Analysis of Variance Using Excel Appendix 11.2 ■ Experimental Design and Analysis of Variance Using MegaStat Appendix 11.3 ■ Experimental Design and Analysis of Variance Using Minitab Chapter 12 Chi-Square Tests 12.1 Chi-Square Goodness-of-Fit Tests 12.2 A Chi-Square Test for Independence Appendix 12.1 ■ Chi-Square Tests Using Excel Appendix 12.2 ■ Chi-Square Tests Using MegaStat Appendix 12.3 ■ Chi-Square Tests Using Minitab Chapter 13 Simple Linear Regression Analysis 13.1 The Simple Linear Regression Model and the Least Squares Point Estimates 13.2 Simple Coefficients of Determination and Correlation 13.3 Model Assumptions andt he Standard Error 13.4 Testing the Significance of the Slope and y-Intercept 13.5 Confidence and Prediction Intervals 13.6 Testing the Significance of the Population Correlation Coefficient (Optional) 13.7 Residual Analysis Appendix 13.1 ■ Simple Linear Regression Analysis Using Excel Appendix 13.2 ■ Simple Linear Regression Analysis Using MegaStat Appendix 13.3 ■ Simple Linear Regression Analysis Using Minitab Chapter 14 Multiple Regression and Model Building 14.1 The Multiple Regression Model and the Least Squares Point Estimates 14.2 R2 and Adjusted R2 14.3 Model Assumptions and the Standard Error 14.4 The Overall F Test 14.5 Testing the Significance of an Independent Variable 14.6 Confidence and Prediction Intervals 14.7 The Sales Representative Case: Evaluating Employee Performance 14.8 Using Dummy Variables to Model Qualitative Independent Variables (Optional) 14.9 Using Squared and Interaction Variables (Optional) 14.10 Multicollinearity, Model Building, and Model Validation (Optional) 14.11 Residual Analysis and Outlier Detection in Multiple Regression (Optional) 14.12 Logistic Regression (Optional) 14.13 Neural Networks (Optional) Appendix 14.1 ■ Multiple Regression Analysis Using Excel Appendix 14.2 ■ Multiple Regression Analysis Using MegaStat Appendix 14.3 ■ Multiple Regression Analysis Using Minitab Appendix 14.4 ■ Neural Network Analysis in JMP Chapter 15 Time Series Forecasting and Index Numbers 15.1 Time Series Components and Models 15.2 Time Series Regression 15.3 Multiplicative Decomposition 15.4 Simple Exponential Smoothing 15.5 Holt–Winters’ Models 15.6 Forecast Error Comparisons 15.7 Index Numbers Appendix 15.1 ■ Time Series Analysis Using Excel Appendix 15.2 ■ Time Series Analysis Using MegaStat Appendix 15.3 ■ Time Series Analysis Using Minitab Chapter 16 Process Improvement Using Control Charts 16.1 Quality: Its Meaning and a Historical Perspective 16.2 Statistical Process Control and Causes of Process Variation 16.3 Sampling a Process, Rational Subgrouping, and Control Charts 16.4 ̶ x and R Charts 16.5 Comparison of a Process with Specifications: Capability Studies 16.6 Charts for Fraction Nonconforming 16.7 Cause-and-Effect and Defect Concentration Diagrams (Optional) Appendix 16.1 ■ Control Charts Using MegaStat Appendix 16.2 ■ Control Charts Using Minitab Chapter 17 Nonparametric Methods 17.1 The Sign Test: A Hypothesis Test about the Median 17.2 The Wilcoxon Rank Sum Test 17.3 The Wilcoxon Signed Ranks Test 17.4 Comparing Several Populations Using the Kruskal–Wallis H Test 17.5 Spearman’s Rank Correlation Coefficient Appendix 17.1 ■ Nonparametric Methods Using MegaStat Appendix 17.2 ■ Nonparametric Methods Using Minitab Chapter 18 Decision Theory 18.1 Introduction to Decision Theory 18.2 Decision Making Using Posterior Probabilities 18.3 Introduction to Utility Theory Appendix A Statistical Tables Appendix B An Introduction to Box–Jenkins Models ANSWERS TO MOST ODD-NUMBERED EXERCISES REFERENCES PHOTO CREDITS INDEX A B C D E F G H I J K L M N O P Q R S T U V W X Y Z