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• Applet Correlation
• Half Title
• Title Page
• Copyright
• Contents
• Preface
• Acknowledgments
• MyLab Statistics Resources for Success
• Chapter 1. Statistics, Data, and Statistical Thinking
• 1.1 The Science of Statistics
• 1.2 Types of Statistical Applications in Business
• 1.3 Fundamental Elements of Statistics
• 1.4 Processes (Optional)
• 1.5 Types of Data
• 1.6 Collecting Data: Sampling and Related Issues
• 1.7 Business Analytics: Critical Thinking with Statistics
• Statistics In Action: A 20/20 View of Surveys and Studies: Facts or Fake News?
• Activity 1.1: Keep the Change: Collecting Data
• Activity 1.2: Identifying Misleading Statistics
• Using Technology: Accessing and Listing Data
• Chapter 2. Methods for Describing Sets of Data
• 2.1 Describing Qualitative Data
• 2.2 Graphical Methods for Describing Quantitative Data
• 2.3 Numerical Measures of Central Tendency
• 2.4 Numerical Measures of Variability
• 2.5 Using the Mean and Standard Deviation to Describe Data
• 2.6 Numerical Measures of Relative Standing
• 2.7 Methods for Detecting Outliers: Box Plots and z-Scores
• 2.8 Graphing Bivariate Relationships (Optional)
• 2.9 The Time Series Plot (Optional)
• 2.10 Distorting the Truth with Descriptive Techniques
• Statistics In Action: Can Money Buy Love?
• Activity 2.1: Real Estate Sales
• Activity 2.2: Keep the Change: Measures of Central Tendency and Variability
• Using Technology: Describing Data
• Making Business Decisions: The Kentucky Milk Case—Part I (Covers Chapters 1 and 2)
• Chapter 3. Probability
• 3.1 Events, Sample Spaces, and Probability
• 3.2 Unions and Intersections
• 3.3 Complementary Events
• 3.4 The Additive Rule and Mutually Exclusive Events
• 3.5 Conditional Probability
• 3.6 The Multiplicative Rule and Independent Events
• 3.7 Bayes’s Rule
• Statistics In Action: Lotto Buster!
• Activity 3.1: Exit Polls: Conditional Probability
• Activity 3.2: Keep the Change: Independent Events
• Using Technology: Combinations and Permutations
• Chapter 4. Random Variables and Probability Distributions
• 4.1 Two Types of Random Variables
• Part I: Discrete Random Variables
• 4.2 Probability Distributions for Discrete Random Variables
• 4.3 The Binomial Distribution
• 4.4 Other Discrete Distributions: Poisson and Hypergeometric
• Part II: Continuous Random Variables
• 4.5 Probability Distributions for Continuous Random Variables
• 4.6 The Normal Distribution
• 4.7 Descriptive Methods for Assessing Normality
• 4.8 Other Continuous Distributions: Uniform and Exponential
• Statistics In Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold?
• Activity 4.1: Warehouse Club Memberships: Exploring a Binomial Random Variable
• Activity 4.2: Identifying the Type of Probability Distribution
• Using Technology: Discrete Probabilities, Continuous Probabilities, and Normal Probability Plots
• Chapter 5. Sampling Distributions
• 5.1 The Concept of a Sampling Distribution
• 5.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance
• 5.3 The Sampling Distribution of the Sample Mean and the Central Limit Theorem
• 5.4 The Sampling Distribution of the Sample Proportion
• Statistics In Action: The Insomnia Pill: Is It Effective?
• Activity 5.1: Simulating a Sampling Distribution—Cell Phone Usage
• Using Technology: Simulating a Sampling Distribution
• Making Business Decisions: The Furniture Fire Case (Covers Chapters 3–5)
• Chapter 6. Inferences Based on a Single Sample: Estimation with Confidence Intervals
• 6.1 Identifying and Estimating the Target Parameter
• 6.2 Confidence Interval for a Population Mean: Normal (z) Statistic
• 6.3 Confidence Interval for a Population Mean: Student’s t-Statistic
• 6.4 Large-Sample Confidence Interval for a Population Proportion
• 6.5 Determining the Sample Size
• 6.6 Finite Population Correction for Simple Random Sampling (Optional)
• 6.7 Confidence Interval for a Population Variance (Optional)
• Statistics In Action: Medicare Fraud Investigations
• Activity 6.1: Conducting a Pilot Study
• Using Technology: Confidence Intervals and Sample Size Determination
• Chapter 7. Inferences Based on a Single Sample: Tests of Hypotheses
• 7.1 The Elements of a Test of Hypothesis
• 7.2 Formulating Hypotheses and Setting Up the Rejection Region
• 7.3 Observed Significance Levels: p-Values
• 7.4 Test of Hypothesis About a Population Mean: Normal (z) Statistic
• 7.5 Test of Hypothesis About a Population Mean: Student’s t-Statistic
• 7.6 Large-Sample Test of Hypothesis About a Population Proportion
• 7.7 Test of Hypothesis About a Population Variance
• 7.8 Calculating Type II Error Probabilities: More About β (Optional)
• Statistics In Action: Diary of a Kleenex® User—How Many Tissues in a Box?
• Activity 7.1: Challenging a Company’s Claim: Tests of Hypotheses
• Activity 7.2: Keep the Change: Tests of Hypotheses
• Using Technology: Tests of Hypotheses
• Chapter 8. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
• 8.1 Identifying the Target Parameter
• 8.2 Comparing Two Population Means: Independent Sampling
• 8.3 Comparing Two Population Means: Paired Difference Experiments
• 8.4 Comparing Two Population Proportions: Independent Sampling
• 8.5 Determining the Required Sample Size
• 8.6 Comparing Two Population Variances: Independent Sampling
• Statistics In Action: ZixIt Corp. v. Visa USA Inc.—A Libel Case
• Activity 8.1: Box Office Receipts: Comparing Population Means
• Activity 8.2: Keep the Change: Inferences Based on Two Samples
• Using Technology: Two-Sample Inferences
• Making Business Decisions: The Kentucky Milk Case—Part II (Covers Chapters 6–8)
• Chapter 9. Design of Experiments and Analysis of Variance
• 9.1 Elements of a Designed Experiment
• 9.2 The Completely Randomized Design: Single Factor
• 9.3 Multiple Comparisons of Means
• 9.4 The Randomized Block Design
• 9.5 Factorial Experiments: Two Factors
• Statistics In Action: Tax Compliance Behavior—Factors That Affect Your Level of Risk Taking When F
• Activity 9.1: Designed vs. Observational Experiments
• Using Technology: Analysis of Variance
• Chapter 10. Categorical Data Analysis
• 10.1 Categorical Data and the Multinomial Experiment
• 10.2 Testing Category Probabilities: One-Way Table
• 10.3 Testing Category Probabilities: Two-Way (Contingency) Table
• 10.4 A Word of Caution About Chi-Square Tests
• Statistics In Action: The Illegal Transplant Tissue Trade—Who Is Responsible for Paying Damages?
• Activity 10.1: Binomial vs. Multinomial Experiments
• Activity 10.2: Contingency Tables
• Using Technology: Chi-Square Analyses
• Making Business Decisions: Discrimination in the Workplace (Covers Chapters 9–10)
• Chapter 11. Simple Linear Regression
• 11.1 Probabilistic Models
• 11.2 Fitting the Model: The Least Squares Approach
• 11.3 Model Assumptions
• 11.4 Assessing the Utility of the Model: Making Inferences About the Slope β1
• 11.5 The Coefficients of Correlation and Determination
• 11.6 Using the Model for Estimation and Prediction
• 11.7 A Complete Example
• Statistics In Action: Legal Advertising—Does It Pay?
• Activity 11.1: Applying Simple Linear Regression to Your Favorite Data
• Using Technology: Simple Linear Regression
• Chapter 12. Multiple Regression and Model Building
• 12.1 Multiple Regression Models
• Part I: First-Order Models With Quantitative Independent variables
• 12.2 Estimating and Making Inferences About the β Parameters
• 12.3 Evaluating Overall Model Utility
• 12.4 Using the Model for Estimation and Prediction
• Part II: Model Building In Multiple Regression
• 12.5 Interaction Models
• 12.6 Quadratic and Other Higher-Order Models
• 12.7 Qualitative (Dummy) Variable Models
• 12.8 Models with Both Quantitative and Qualitative Variables
• 12.9 Comparing Nested Models
• 12.10 Stepwise Regression
• Part III: Multiple Regression Diagnostics
• 12.11 Residual Analysis: Checking the Regression Assumptions
• 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
• Statistics In Action: Bid Rigging in the Highway Construction Industry
• Activity 12.1: Insurance Premiums: Collecting Data for Several Variables
• Activity 12.2: Collecting Data and Fitting a Multiple Regression Model
• Using Technology: Multiple Regression
• Making Business Decisions: The Condo Sales Case (Covers Chapters 11–12)
• Chapter 13. Methods for Quality Improvement: Statistical Process Control
• 13.1 Quality, Processes, and Systems
• 13.2 Statistical Control
• 13.3 The Logic of Control Charts
• 13.4 A Control Chart for Monitoring the Mean of a Process: The x-Chart
• 13.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart
• 13.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart
• 13.7 Diagnosing the Causes of Variation
• 13.8 Capability Analysis
• Statistics In Action: Testing Jet Fuel Additive for Safety
• Activity 13.1: Quality Control: Consistency
• Using Technology: Control Charts
• Making Business Decisions: The Gasket Manufacturing Case (Covers Chapter 13)
• Chapter 14. Time Series: Descriptive Analyses, Models, and Forecasting
• 14.1 Descriptive Analysis: Index Numbers
• 14.2 Descriptive Analysis: Exponential Smoothing
• 14.3 Time Series Components
• 14.4 Forecasting: Exponential Smoothing
• 14.5 Forecasting Trends: Holt’s Method
• 14.6 Measuring Forecast Accuracy: MAD and RMSE
• 14.7 Forecasting Trends: Simple Linear Regression
• 14.8 Seasonal Regression Models
• 14.9 Autocorrelation and the Durbin-Watson Test
• Statistics In Action: Forecasting the Monthly Sales of a New Cold Medicine
• Activity 14.1: Time Series
• Using Technology: Forecasting
• Chapter 15. Nonparametric Statistics
• 15.1 Introduction: Distribution-Free Tests
• 15.2 Single Population Inferences
• 15.3 Comparing Two Populations: Independent Samples
• 15.4 Comparing Two Populations: Paired Difference Experiment
• 15.5 Comparing Three or More Populations: Completely Randomized Design
• 15.6 Comparing Three or More Populations: Randomized Block Design
• 15.7 Rank Correlation
• Statistics In Action: Pollutants at a Housing Development—A Case of Mishandling Small Samples
• Activity 15.1: Keep the Change: Nonparametric Statistics
• Using Technology: Nonparametric Tests
• Making Business Decisions: Detecting “Sales Chasing” (Covers Chapters 10 and 15)
• Appendix A: Summation Notation
• Appendix B: Basic Counting Rules
• Appendix C: Calculation Formulas for Analysis of Variance
• C.1 Formulas for the Calculations in the Completely Randomized Design
• C.2 Formulas for the Calculations in the Randomized Block Design
• C.3 Formulas for the Calculations for a Two-Factor Factorial Experiment
• C.4 Tukey’s Multiple Comparisons Procedure (Equal Sample Sizes)
• C.5 Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons)
• C.6 Scheffé’s Multiple Comparisons Procedure (Pairwise Comparisons)
• Appendix D: Tables
• Table I Binomial Probabilities
• Table II Normal Curve Areas
• Table III Critical Values of t
• Table IV Critical Values of x2
• Table V Percentage Points of the F-Distribution, α = .10
• Table VI Percentage Points of the F-Distribution, α = .05
• Table VII Percentage Points of the F-Distribution, α = .025
• Table VIII Percentage Points of the F-Distribution, α = .01
• Table IX Control Chart Constants
• Table X Critical Values for the Durbin-Watson d-Statistic, α = .05
• Table XI Critical Values for the Durbin-Watson d-Statistic, α = .01
• Table XII Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples
• Table XIII Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test
• Table XIV Critical Values of Spearman’s Rank Correlation Coefficient
• Table XV Critical Values of the Studentized Range, α = .05
• Answers to Selected Exercises
• Index
• Credits
• Selected Formulas