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

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
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