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• Cover Page
• Title Page
• Copyright Page
• Dedication
• About the Authors
• Contents
• Videos in This Enhanced eBook
• Preface
• First Things First
• Using Statistics: “The Price of Admission”
• FTF.1 Think Differently About Statistics
• Statistics: A Way of Thinking
• Statistics: An Important Part of Your Business Education
• FTF.2 Business Analytics: The Changing Face of Statistics
• “Big Data”
• FTF.3 Starting Point for Learning Statistics
• Statistic
• Can Statistics ( pl., statistic) Lie?
• FTF.4 Starting Point for Using Software
• Using Software Properly
• FTF.5 Starting Point for Using Microsoft Excel
• More About the Excel Guide Workbooks
• Excel Skills That Readers Need
• References
• Key Terms
• Excel Guide
• EG.1 Getting Started with Excel
• EG.2 Entering Data
• EG.3 Open or Save a Workbook
• EG.4 Working with a Workbook
• EG.5 Print a Worksheet
• EG.6 Reviewing Worksheets
• EG.7 If You Use the Workbook Instructions
• Tableau Guide
• TG.1 Getting Started with Tableau
• TG.2 Entering Data
• TG.3 Open or Save a Workbook
• TG.4 Working with Data
• TG.5 Print a Workbook
• 1 Defining and Collecting Data
• Using Statistics: Defining Moments
• 1.1 Defining Variables
• Classifying Variables by Type
• Measurement Scales
• 1.2 Collecting Data
• Populations and Samples
• Data Sources
• 1.3 Types of Sampling Methods
• Simple Random Sample
• Systematic Sample
• Stratified Sample
• Cluster Sample
• 1.4 Data Cleaning
• Invalid Variable Values
• Coding Errors
• Data Integration Errors
• Missing Values
• Algorithmic Cleaning of Extreme Numerical Values
• 1.5 Other Data Preprocessing Tasks
• Data Formatting
• Stacking and Unstacking Data
• Recoding Variables
• 1.6 Types of Survey Errors
• Coverage Error
• Nonresponse Error
• Sampling Error
• Measurement Error
• Ethical Issues About Surveys
• Consider This: New Media Surveys/Old Survey Errors
• Using Statistics: Defining Moments, Revisited
• Summary
• References
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 1
• Managing Ashland MultiComm Services
• CardioGood Fitness
• Clear Mountain State Student Survey
• Learning With the Digital Cases
• Chapter 1 Excel Guide
• EG1.1 Defining Variables
• EG1.2 Types of Sampling Methods
• EG1.3 Data Cleaning
• EG1.4 Other Data Preprocessing
• Chapter 1 Tableau Guide
• TG1.1 Defining Variables
• TG1.2 Data Cleaning
• 2 Organizing and Visualizing Variables
• Using Statistics: “The Choice Is Yours”
• 2.1 Organizing Categorical Variables
• The Summary Table
• The Contingency Table
• 2.2 Organizing Numerical Variables
• The Frequency Distribution
• The Relative Frequency Distribution and the Percentage Distribution
• The Cumulative Distribution
• 2.3 Visualizing Categorical Variables
• The Bar Chart
• The Pie Chart and the Doughnut Chart
• The Pareto Chart
• Visualizing Two Categorical Variables
• 2.4 Visualizing Numerical Variables
• The Stem-and-Leaf Display
• The Histogram
• The Percentage Polygon
• The Cumulative Percentage Polygon (Ogive)
• 2.5 Visualizing Two Numerical Variables
• The Scatter Plot
• The Time-Series Plot
• 2.6 Organizing a Mix of Variables
• Drill-Down
• 2.7 Visualizing a Mix of Variables
• Colored Scatter Plot (Tableau)
• Bubble Chart
• PivotChart
• Treemap
• Sparklines
• 2.8 Filtering and Querying Data
• Excel Slicers
• 2.9 Pitfalls in Organizing and Visualizing Variables
• Obscuring Data
• Creating False Impressions
• Chartjunk
• Using Statistics: “The Choice Is Yours,” Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 2
• Managing Ashland MultiComm Services
• Digital Case
• CardioGood Fitness
• The Choice Is Yours Follow-Up
• Clear Mountain State Student Survey
• Chapter 2 Excel Guide
• EG2.1 Organizing Categorical Variables
• EG2.2 Organizing Numerical Variables
• EG2.3 Visualizing Categorical Variables
• EG2.4 Visualizing Numerical Variables
• EG2.5 Visualizing Two Numerical Variables
• EG2.6 Organizing a Mix of Variables
• EG2.7 Visualizing a Mix of Variables
• EG2.8 Filtering and Querying Data
• Chapter 2 Tableau Guide
• TG2.1 Organizing Categorical Variables
• TG2.2 Organizing Numerical Variables
• TG2.3 Visualizing Categorical Variables
• TG2.4 Visualizing Numerical Variables
• TG2.5 Visualizing Two Numerical Variables
• TG2.6 Organizing a Mix of Variables
• TG2.7 Visualizing a Mix of Variables
• 3 Numerical Descriptive Measures
• Using Statistics: More Descriptive Choices
• 3.1 Measures of Central Tendency
• The Mean
• The Median
• The Mode
• The Geometric Mean
• 3.2 Measures of Variation and Shape
• The Range
• The Variance and the Standard Deviation
• The Coefficient of Variation
• Z Scores
• Shape: Skewness
• Shape: Kurtosis
• 3.3 Exploring Numerical Variables
• Quartiles
• The Interquartile Range
• The Five-Number Summary
• The Boxplot
• 3.4 Numerical Descriptive Measures for a Population
• The Population Mean
• The Population Variance and Standard Deviation
• The Empirical Rule
• Chebyshev’s Theorem
• 3.5 The Covariance and the Coefficient of Correlation
• The Covariance
• The Coefficient of Correlation
• 3.6 Descriptive Statistics: Pitfalls and Ethical Issues
• Using Statistics: More Descriptive Choices, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 3
• Managing Ashland MultiComm Services
• Digital Case
• CardioGood Fitness
• More Descriptive Choices Follow-Up
• Clear Mountain State Student Survey
• Chapter 3 Excel Guide
• EG3.1 Measures of Central Tendency
• EG3.2 Measures of Variation and Shape
• EG3.3 Exploring Numerical Variables
• EG3.4 Numerical Descriptive Measures for a Population
• EG3.5 The Covariance and the Coefficient of Correlation
• Chapter 3 Tableau Guide
• TG3.1 Exploring Numerical Variables
• 4 Basic Probability
• Using Statistics: Probable Outcomes at Fredco Warehouse Club
• 4.1 Basic Probability Concepts
• Events and Sample Spaces
• Types of Probability
• Summarizing Sample Spaces
• Simple Probability
• Joint Probability
• Marginal Probability
• General Addition Rule
• 4.2 Conditional Probability
• Calculating Conditional Probabilities
• Decision Trees
• Independence
• Multiplication Rules
• Marginal Probability Using the General Multiplication Rule
• 4.3 Ethical Issues and Probability
• 4.4 Bayes’ Theorem
• Consider This: Divine Providence and Spam
• 4.5 Counting Rules
• Using Statistics: Probable Outcomes at Fredco Warehouse Club, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 4
• Digital Case
• CardioGood Fitness
• The Choice Is Yours Follow-Up
• Clear Mountain State Student Survey
• Chapter 4 Excel Guide
• EG4.1 Basic Probability Concepts
• EG4.2 Bayes’ Theorem
• 5 Discrete Probability Distributions
• Using Statistics: Events of Interest at Ricknel Home Centers
• 5.1 The Probability Distribution for a Discrete Variable
• Expected Value of a Discrete Variable
• Variance and Standard Deviation of a Discrete Variable
• 5.2 Binomial Distribution
• Histograms for Discrete Variables
• Summary Measures for the Binomial Distribution
• 5.3 Poisson Distribution
• 5.4 Covariance of a Probability Distribution and its Application in Finance
• 5.5 Hypergeometric Distribution
• Using Statistics: Events of Interest …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 5
• Managing Ashland MultiComm Services
• Digital Case
• Chapter 5 Excel Guide
• EG5.1 The Probability Distribution for a Discrete Variable
• EG5.2 Binomial Distribution
• EG5.3 Poisson Distribution
• 6 The Normal Distribution and Other Continuous Distributions
• Using Statistics: Normal Load Times at MyTVLab
• 6.1 Continuous Probability Distributions
• 6.2 The Normal Distribution
• Role of the Mean and the Standard Deviation
• Calculating Normal Probabilities
• Visual Explorations: Exploring the Normal Distribution
• Finding X Values
• Consider This: What Is Normal?
• 6.3 Evaluating Normality
• Comparing Data Characteristics to Theoretical Properties
• Constructing the Normal Probability Plot
• 6.4 The Uniform Distribution
• 6.5 The Exponential Distribution
• 6.6 The Normal Approximation to the Binomial Distribution
• Using Statistics: Normal Load Times …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 6
• Managing Ashland MultiComm Services
• CardioGood Fitness
• More Descriptive Choices Follow-Up
• Clear Mountain State Student Survey
• Digital Case
• Chapter 6 Excel Guide
• EG6.1 The Normal Distribution
• EG6.2 Evaluating Normality
• 7 Sampling Distributions
• Using Statistics: Sampling Oxford Cereals
• 7.1 Sampling Distributions
• 7.2 Sampling Distribution of the Mean
• The Unbiased Property of the Sample Mean
• Standard Error of the Mean
• Sampling from Normally Distributed Populations
• Sampling from Non-Normally Distributed Populations—The Central Limit Theorem
• Visual Explorations: Exploring Sampling Distributions
• 7.3 Sampling Distribution of the Proportion
• 7.4 Sampling from Finite Populations
• Using Statistics: Sampling Oxford Cereals, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 7
• Managing Ashland MultiComm Services
• Digital Case
• Chapter 7 Excel Guide
• EG7.1 Sampling Distribution of the Mean
• 8 Confidence Interval Estimation
• Using Statistics: Getting Estimates at Ricknel Home Centers
• 8.1 Confidence Interval Estimate for the Mean (σ Known)
• Sampling Error
• Can You Ever Know the Population Standard Deviation?
• 8.2 Confidence Interval Estimate for the Mean (σ Unknown)
• Student’s t Distribution
• The Concept of Degrees of Freedom
• Properties of the t Distribution
• The Confidence Interval Statement
• 8.3 Confidence Interval Estimate for the Proportion
• 8.4 Determining Sample Size
• Sample Size Determination for the Mean
• Sample Size Determination for the Proportion
• 8.5 Confidence Interval Estimation and Ethical Issues
• 8.6 Application of Confidence Interval Estimation in Auditing
• 8.7 Estimation and Sample Size Determination for Finite Populations
• 8.8 Bootstrapping
• Using Statistics: Getting Estimates at Ricknel Home Centers, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 8
• Managing Ashland MultiComm Services
• Digital Case
• Sure Value Convenience Stores
• CardioGood Fitness
• More Descriptive Choices Follow-Up
• Clear Mountain State Student Survey
• Chapter 8 Excel Guide
• EG8.1 Confidence Interval Estimate for the Mean (σ Known)
• EG8.2 Confidence Interval Estimate for the Mean (σ Unknown)
• EG8.3 Confidence Interval Estimate for the Proportion
• EG8.4 Determining Sample Size
• 9 Fundamentals of Hypothesis Testing: One-Sample Tests
• Using Statistics: Significant Testing at Oxford Cereals
• 9.1 Fundamentals of Hypothesis Testing
• The Critical Value of the Test Statistic
• Regions of Rejection and Nonrejection
• Risks in Decision Making Using Hypothesis Testing
• Z Test for the Mean (σ Known)
• Hypothesis Testing Using the Critical Value Approach
• Hypothesis Testing Using the p-Value Approach
• A Connection Between Confidence Interval Estimation and Hypothesis Testing
• Can You Ever Know the Population Standard Deviation?
• 9.2 t Test of Hypothesis for the Mean (σ Unknown)
• Using the Critical Value Approach
• Using the p-Value Approach
• Checking the Normality Assumption
• 9.3 One-Tail Tests
• Using the Critical Value Approach
• Using the p-Value Approach
• 9.4 Z Test of Hypothesis for the Proportion
• Using the Critical Value Approach
• Using the p-Value Approach
• 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues
• Important Planning Stage Questions
• Statistical Significance Versus Practical Significance
• Statistical Insignificance Versus Importance
• Reporting of Findings
• Ethical Issues
• 9.6 Power of the Test
• Using Statistics: Significant Testing …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 9
• Managing Ashland MultiComm Services
• Digital Case
• Sure Value Convenience Stores
• Chapter 9 Excel Guide
• EG9.1 Fundamentals of Hypothesis Testing
• EG9.2 t Test of Hypothesis for the Mean (σ Unknown)
• EG9.3 One-Tail Tests
• EG9.4 Z Test of Hypothesis for the Proportion
• 10 Two-Sample Tests
• Using Statistics: Differing Means for Selling Streaming Media Players at Arlingtons?
• 10.1 Comparing the Means of Two Independent Populations
• Pooled-Variance t Test for the Difference Between Two Means Assuming Equal Variances
• Evaluating the Normality Assumption
• Confidence Interval Estimate for the Difference Between Two Means
• Separate-Variance t Test for the Difference Between Two Means, Assuming Unequal Variances
• Consider This: Do People Really Do This?
• 10.2 Comparing the Means of Two Related Populations
• Paired t Test
• Confidence Interval Estimate for the Mean Difference
• 10.3 Comparing the Proportions of Two Independent Populations
• Z Test for the Difference Between Two Proportions
• Confidence Interval Estimate for the Difference Between Two Proportions
• 10.4 F Test for the Ratio of Two Variances
• 10.5 Effect Size
• Using Statistics: Differing Means for Selling …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 10
• Managing Ashland MultiComm Services
• Digital Case
• Sure Value Convenience Stores
• CardioGood Fitness
• More Descriptive Choices Follow-Up
• Clear Mountain State Student Survey
• Chapter 10 Excel Guide
• EG10.1 Comparing the Means of Two Independent Populations
• EG10.2 Comparing the Means of Two Related Populations
• EG10.3 Comparing the Proportions of Two Independent Populations
• EG10.4 F Test for the Ratio of Two Variances
• 11 Analysis of Variance
• Using Statistics: The Means to Find Differences at Arlingtons
• 11.1 One-Way ANOVA
• F Test for Differences Among More Than Two Means
• One-Way ANOVA F Test Assumptions
• Levene Test for Homogeneity of Variance
• Multiple Comparisons: The Tukey-Kramer Procedure
• 11.2 Two-Way ANOVA
• Factor and Interaction Effects
• Testing for Factor and Interaction Effects
• Multiple Comparisons: The Tukey Procedure
• Visualizing Interaction Effects: The Cell Means Plot
• Interpreting Interaction Effects
• 11.3 The Randomized Block Design
• 11.4 Fixed Effects, Random Effects, and Mixed Effects Models
• Using Statistics: The Means to Find Differences at Arlingtons, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 11
• Managing Ashland MultiComm Services
• Phase 1
• Phase 2
• Digital Case
• Sure Value Convenience Stores
• CardioGood Fitness
• More Descriptive Choices Follow-Up
• Clear Mountain State Student Survey
• Chapter 11 Excel Guide
• EG11.1 The Completely Randomized Design: One-Way ANOVA
• EG11.2 The Factorial Design: Two-Way ANOVA
• 12 Chi-Square and Nonparametric Tests
• Using Statistics: Avoiding Guesswork About Resort Guests
• 12.1 Chi-Square Test for the Difference Between Two Proportions
• 12.2 Chi-Square Test for Differences Among More Than Two Proportions
• The Marascuilo Procedure
• The Analysis of Proportions (ANOP)
• 12.3 Chi-Square Test of Independence
• 12.4 Wilcoxon Rank Sum Test for Two Independent Populations
• 12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA
• Assumptions of the Kruskal-Wallis Rank Test
• 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)
• 12.7 Chi-Square Test for the Variance or Standard Deviation
• 12.8 Wilcoxon Signed Ranks Test for Two Related Populations
• Using Statistics: Avoiding Guesswork …, Revisited
• References
• Summary
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 12
• Managing Ashland MultiComm Services
• Phase 1
• Phase 2
• Digital Case
• Sure Value Convenience Stores
• CardioGood Fitness
• More Descriptive Choices Follow-Up
• Clear Mountain State Student Survey
• Chapter 12 Excel Guide
• EG12.1 Chi-Square Test for the Difference Between Two Proportions
• EG12.2 Chi-Square Test for Differences Among More Than Two Proportions
• EG12.3 Chi-Square Test of Independence
• EG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for Two Independent Populations
• EG12.5 Kruskal-Wallis Rank Test: A Nonparametric Method for the One-Way ANOVA
• 13 Simple Linear Regression
• Using Statistics: Knowing Customers at Sunflowers Apparel
• Preliminary Analysis
• 13.1 Simple Linear Regression Models
• 13.2 Determining the Simple Linear Regression Equation
• The Least-Squares Method
• Predictions in Regression Analysis: Interpolation Versus Extrapolation
• Calculating the Slope, b1, and the Y Intercept, b0
• Visual Explorations: Exploring Simple Linear Regression Coefficients
• 13.3 Measures of Variation
• Computing the Sum of Squares
• The Coefficient of Determination
• Standard Error of the Estimate
• 13.4 Assumptions of Regression
• 13.5 Residual Analysis
• Evaluating the Assumptions
• 13.6 Measuring Autocorrelation: The Durbin-Watson Statistic
• Residual Plots to Detect Autocorrelation
• The Durbin-Watson Statistic
• 13.7 Inferences About the Slope and Correlation Coefficient
• t Test for the Slope
• F Test for the Slope
• Confidence Interval Estimate for the Slope
• t Test for the Correlation Coefficient
• 13.8 Estimation of Mean Values and Prediction of Individual Values
• The Confidence Interval Estimate for the Mean Response
• The Prediction Interval for an Individual Response
• 13.9 Potential Pitfalls in Regression
• Using Statistics: Knowing Customers …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 13
• Managing Ashland MultiComm Services
• Digital Case
• Brynne Packaging
• Chapter 13 Excel Guide
• EG13.1 Determining the Simple Linear Regression Equation
• EG13.2 Measures of Variation
• EG13.3 Residual Analysis
• EG13.4 Measuring Autocorrelation: the Durbin-Watson Statistic
• EG13.5 Inferences About the Slope and Correlation Coefficient
• EG13.6 Estimation of Mean Values and Prediction of Individual Values
• Chapter 13 Tableau Guide
• TG13.1 Determining the Simple Linear Regression Equation
• TG13.2 Measures of Variation
• 14 Introduction to Multiple Regression
• Using Statistics: The Multiple Effects of OmniPower Bars
• 14.1 Developing a Multiple Regression Model
• Interpreting the Regression Coefficients
• Predicting the Dependent Variable Y
• 14.2 Evaluating Multiple Regression Models
• Coefficient of Multiple Determination, r2
• Adjusted r2
• F Test for the Significance of the Overall Multiple Regression Model
• 14.3 Multiple Regression Residual Analysis
• 14.4 Inferences About the Population Regression Coefficients
• Tests of Hypothesis
• Confidence Interval Estimation
• 14.5 Testing Portions of the Multiple Regression Model
• Coefficients of Partial Determination
• 14.6 Using Dummy Variables and Interaction Terms
• Interactions
• Consider This: What Is Not Normal? (Using a Categorical Dependent Variable)
• 14.7 Logistic Regression
• 14.8 Cross-Validation
• Using Statistics: The Multiple Effects …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 14
• Managing Ashland MultiComm Services
• Digital Case
• Chapter 14 Excel Guide
• EG14.1 Developing a Multiple Regression Model
• EG14.2 Evaluating Multiple Regression Models
• EG14.3 Multiple Regression Residual Analysis
• EG14.4 Inferences About the Population Regression Coefficients
• EG14.5 Testing Portions of the Multiple Regression Model
• EG14.6 Using Dummy Variables and Interaction Terms
• EG14.7 Logistic Regression
• 15 Multiple Regression Model Building
• Using Statistics: Valuing Parsimony at WSTA-TV
• 15.1 The Quadratic Regression Model
• Finding the Regression Coefficients and Predicting Y
• Testing for the Significance of the Quadratic Model
• Testing the Quadratic Effect
• The Coefficient of Multiple Determination
• 15.2 Using Transformations in Regression Models
• The Square-Root Transformation
• The Log Transformation
• 15.3 Collinearity
• 15.4 Model Building
• The Stepwise Regression Approach to Model Building
• The Best Subsets Approach to Model Building
• 15.5 Pitfalls in Multiple Regression and Ethical Issues
• Pitfalls in Multiple Regression
• Ethical Issues
• Using Statistics: Valuing Parsimony …, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 15
• The Mountain States Potato Company
• Sure Value Convenience Stores
• Digital Case
• The Craybill Instrumentation Company Case
• More Descriptive Choices Follow-Up
• Chapter 15 Excel Guide
• EG15.1 The Quadratic Regression Model
• EG15.2 Using Transformations in Regression Models
• EG15.3 Collinearity
• EG15.4 Model Building
• 16 Time-Series Forecasting
• Using Statistics: Is the ByYourDoor Service Trending?
• 16.1 Time-Series Component Factors
• 16.2 Smoothing an Annual Time Series
• Moving Averages
• Exponential Smoothing
• 16.3 Least-Squares Trend Fitting and Forecasting
• The Linear Trend Model
• The Quadratic Trend Model
• The Exponential Trend Model
• Model Selection Using First, Second, and Percentage Differences
• 16.4 Autoregressive Modeling for Trend Fitting and Forecasting
• Selecting an Appropriate Autoregressive Model
• Determining the Appropriateness of a Selected Model
• 16.5 Choosing an Appropriate Forecasting Model
• Residual Analysis
• The Magnitude of the Residuals Through Squared or Absolute Differences
• The Principle of Parsimony
• A Comparison of Four Forecasting Methods
• 16.6 Time-Series Forecasting of Seasonal Data
• Least-Squares Forecasting with Monthly or Quarterly Data
• 16.7 Index Numbers
• Consider This: Let the Model User Beware
• Using Statistics: Is the ByYourDoor Service Trending? Revisited
• Summary
• References
• Key Equations
• Key Terms
• Checking Your Understanding
• Chapter Review Problems
• Cases For Chapter 16
• Managing Ashland MultiComm Services
• Digital Case
• Chapter 16 Excel Guide
• EG16.1 Smoothing an Annual Time Series
• EG16.2 Least-Squares Trend Fitting and Forecasting
• EG16.3 Autoregressive Modeling for Trend Fitting and Forecasting
• EG16.4 Choosing an Appropriate Forecasting Model
• EG16.5 Time-Series Forecasting of Seasonal Data
• 17 Business Analytics
• Using Statistics: Back to Arlingtons for the Future
• 17.1 Business Analytics Overview
• Business Analytics Categories
• Business Analytics Vocabulary
• Consider This: What’s My Major If I Want to Be a Data Miner?
• Inferential Statistics and Predictive Analytics
• Microsoft Excel and Business Analytics
• Remainder of This Chapter
• 17.2 Descriptive Analytics
• Dashboards
• Data Dimensionality and Descriptive Analytics
• 17.3 Decision Trees
• Regression Trees
• Classification Trees
• Subjectivity and Interpretation
• 17.4 Clustering
• 17.5 Association Analysis
• 17.6 Text Analytics
• 17.7 Prescriptive Analytics
• Optimization and Simulation
• Using Statistics: Back to Arlingtons …, Revisited
• References
• Key Terms
• Checking Your Understanding
• Chapter 17 Software Guide
• SG17.1 Descriptive Analytics
• SG17.2 Predictive Analytics for Clustering
• 18 Getting Ready to Analyze Data in the Future
• Using Statistics: Mounting Future Analyses
• 18.1 Analyzing Numerical Variables
• Describe the Characteristics of a Numerical Variable
• Reach Conclusions About the Population Mean or the Standard Deviation
• Determine Whether the Mean and/or Standard Deviation Differs Depending on the Group
• Determine Which Factors Affect the Value of a Variable
• Predict the Value of a Variable Based on the Values of Other Variables
• Classify or Associate Items
• Determine Whether the Values of a Variable Are Stable Over Time
• 18.2 Analyzing Categorical Variables
• Describe the Proportion of Items of Interest in Each Category
• Reach Conclusions About the Proportion of Items of Interest
• Determine Whether the Proportion of Items of Interest Differs Depending on the Group
• Predict the Proportion of Items of Interest Based on the Values of Other Variables
• Cluster or Associate Items
• Determine Whether the Proportion of Items of Interest Is Stable Over Time
• Using Statistics: The Future to Be Visited
• Chapter Review Problems
• 19 Statistical Applications in Quality Management
• Using Statistics: Finding Quality at the Beachcomber
• 19.1 The Theory of Control Charts
• The Causes of Variation
• 19.2 Control Chart for the Proportion: The p Chart
• 19.3 The Red Bead Experiment: Understanding Process Variability
• 19.4 Control Chart for an Area of Opportunity: The c Chart
• 19.5 Control Charts for the Range and the Mean
• The R Chart
• The X Chart
• 19.6 Process Capability
• Customer Satisfaction and Specification Limits
• Capability Indices
• CPL, CPU, and Cpk
• 19.7 Total Quality Management
• 19.8 Six Sigma
• The DMAIC Model
• Roles in a Six Sigma Organization
• Lean Six Sigma
• Using Statistics: Finding Quality at the Beachcomber, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Chapter Review Problems
• Cases For Chapter 19
• The Harnswell Sewing Machine Company Case
• Phase 1
• Phase 2
• Phase 3
• Phase 4
• Phase 5
• Managing Ashland Multicomm Services
• Chapter 19 Excel Guide
• EG19.2 Control Chart for the Proportion: The p Chart
• EG19.4 Control Chart for an Area of Opportunity: The c Chart
• EG19.5 Control Charts for the Range and the Mean
• EG19.6 Process Capability
• 20 Decision Making
• Using Statistics: Reliable Decision Making
• 20.1 Payoff Tables and Decision Trees
• 20.2 Criteria for Decision Making
• Maximax Payoff
• Maximin Payoff
• Expected Monetary Value
• Expected Opportunity Loss
• Return-to-Risk Ratio
• 20.3 Decision Making with Sample Information
• 20.4 Utility
• Consider This: Risky Business
• Using Statistics: Reliable Decision Making, Revisited
• Summary
• References
• Key Equations
• Key Terms
• Chapter Review Problems
• Cases For Chapter 20
• Digital Case
• Chapter 20 Excel Guide
• EG 20.1 Payoff Tables and Decision Trees
• EG 20.2 Criteria for Decision Making
• Appendices
• A. Basic Math Concepts and Symbols
• A.1 Operators
• A.2 Rules for Arithmetic Operations
• A.3 Rules for Algebra: Exponents and Square Roots
• A.4 Rules for Logarithms
• A.5 Summation Notation
• A.6 Greek Alphabet
• B. Important Software Skills and Concepts
• B.1 Identifying the Software Version
• B.2 Formulas
• B.3 Excel Cell References
• B.4 Excel Worksheet Formatting
• B.5E Excel Chart Formatting
• B.5T Tableau Chart Formatting
• B.6 Creating Histograms for Discrete Probability Distributions (Excel)
• B.7 Deleting the “Extra” Histogram Bar (Excel)
• C. Online Resources
• C.1 About the Online Resources for This Book
• C.2 Data Files
• C.3 Microsoft Excel Files Integrated With This Book
• C.4 Supplemental Files
• D. Configuring Software
• D.1 Microsoft Excel Configuration
• D.2 Supplemental Files
• E. Table
• E.1 Table of Random Numbers
• E.2 The Cumulative Standardized Normal Distribution
• E.3 Critical Values of t
• E.4 Critical Values of χ2
• E.5 Critical Values of F
• E.6 Lower and Upper Critical Values, T1, of the Wilcoxon Rank Sum Test
• E.7 Critical Values of the Studentized Range, Q
• E.8 Critical Values, dL and dU, of the Durbin-Watson Statistic, D (Critical Values Are One-Sided)
• E.9 Control Chart Factors
• E.10 The Standardized Normal Distribution
• F. Useful Knowledge
• F.1 Keyboard Shortcuts
• F.2 Understanding the Nonstatistical Excel Functions
• G. Software FAQs
• G.1 Microsoft Excel FAQs
• G.2 PHStat FAQs
• G.3 Tableau FAQs
• H. All About PHStat
• H.1 What is PHStat?
• H.2 Obtaining and Setting Up PHStat
• H.3 Using PHStat
• H.4 PHStat Procedures, by Category
• Self-Test Solutions and Answers to Selected Even-Numbered Problems
• Index
• Credits