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• Half Title
• Title Page
• Copyright Page
• Pearson’s Commitment to Diversity, Equity, and Inclusion
• Dedication
• About the Authors
• Brief Contents
• Contents
• Preface
• Chapter 1. The Where, Why, and How of Data Collection
• 1.1 What Is Business Statistics?
• Descriptive Statistics
• Inferential Procedures
• 1.2 Procedures for Collecting Data
• Common Data Collection Methods
• Other Data Collection Methods
• Data Collection Issues
• 1.3 Populations, Samples, and Sampling Techniques
• Populations and Samples
• Sampling Techniques
• 1.4 Data Types and Data Measurement Levels
• Quantitative and Qualitative Data
• Time-Series Data and Cross-Sectional Data
• Data Measurement Levels
• 1.5 A Brief Introduction to Data Mining and Business Analytics
• Data Mining and Business Analytics—Finding the Important, Hidden Relationships in Data
• Summary
• Key Terms
• Chapter Exercises
• Chapter 2. Graphs, Charts, and Tables—Describing Your Data
• 2.1 Frequency Distributions and Histograms
• Frequency Distributions
• Grouped Data Frequency Distributions
• Histograms
• Relative Frequency Histograms and Ogives
• Joint Frequency Distributions
• 2.2 Bar Charts, Pie Charts, and Stem and Leaf Diagrams
• Bar Charts
• Pie Charts
• Stem and Leaf Diagrams
• 2.3 Line Charts, Scatter Diagrams, and Pareto Charts
• Line Charts
• Scatter Diagrams
• Pareto Charts
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 2.1 Server Downtime
• Case 2.2 Hudson Valley Apples, Inc.
• Case 2.3 Pine River Lumber Company—Part 1
• Chapter 3. Describing Data Using Numerical Measures
• 3.1 Measures of Center and Location
• Parameters and Statistics
• Population Mean
• Sample Mean
• The Impact of Extreme Values on the Mean
• Median
• Skewed and Symmetric Distributions
• Mode
• Applying the Measures of Central Tendency
• Other Measures of Location
• Box and Whisker Plots
• Developing a Box and Whisker Plot in Excel
• Data-Level Issues
• 3.2 Measures of Variation
• Range
• Interquartile Range
• Population Variance and Standard Deviation
• Sample Variance and Standard Deviation
• 3.3 Using the Mean and Standard Deviation Together
• Coefficient of Variation
• Tchebysheff’s Theorem
• Standardized Data Values
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 3.1 SDW—Human Resources
• Case 3.2 National Call Center
• Case 3.3 Pine River Lumber Company—Part 2
• Case 3.4 AJ’s Fitness Center
• Chapters 1–3. Special Review Section
• Chapters 1–3
• Exercises
• Review Case 1 State Department of Insurance
• Term Project Assignments
• Chapter 4. Introduction to Probability
• 4.1 The Basics of Probability
• Important Probability Terms
• Methods of Assigning Probability
• 4.2 The Rules of Probability
• Measuring Probabilities
• Conditional Probability
• Multiplication Rule
• Bayes’ Theorem
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 4.1 Great Air Commuter Service
• Case 4.2 Pittsburg Lighting
• Chapter 5. Discrete Probability Distributions
• 5.1 Introduction to Discrete Probability Distributions
• Random Variables
• Mean and Standard Deviation of Discrete Distributions
• 5.2 The Binomial Probability Distribution
• The Binomial Distribution
• Characteristics of the Binomial Distribution
• 5.3 Other Probability Distributions
• The Poisson Distribution
• The Hypergeometric Distribution
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 5.1 SaveMor Pharmacies
• Case 5.2 Arrowmark Vending
• Case 5.3 Boise Cascade Corporation
• Chapter 6. Introduction to Continuous Probability Distributions
• 6.1 The Normal Distribution
• The Normal Distribution
• The Standard Normal Distribution
• Using the Standard Normal Table
• 6.2 Other Continuous Probability Distributions
• The Uniform Distribution
• The Exponential Distribution
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 6.1 State Entitlement Programs
• Case 6.2 Credit Data, Inc.
• Case 6.3 National Oil Company—Part 1
• Chapter 7. Introduction to Sampling Distributions
• 7.1 Sampling Error: What It Is and Why It Happens
• Calculating Sampling Error
• 7.2 Sampling Distribution of the Mean
• Simulating the Sampling Distribution for x
• The Central Limit Theorem
• 7.3 Sampling Distribution of a Proportion
• Working with Proportions
• Sampling Distribution of p
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 7.1 Carpita Bottling Company—Part 1
• Case 7.2 Truck Safety Inspection
• Chapter 8. Estimating Single Population Parameters
• 8.1 Point and Confidence Interval Estimates for a Population Mean
• Point Estimates and Confidence Intervals
• Confidence Interval Estimate for the Population Mean, σ Known
• Confidence Interval Estimates for the Population Mean, σ Unknown
• Student’s t-Distribution
• 8.2 Determining the Required Sample Size for Estimating a Population Mean
• Determining the Required Sample Size for Estimating μ, σ Known
• Determining the Required Sample Size for Estimating μ, σ Unknown
• 8.3 Estimating a Population Proportion
• Confidence Interval Estimate for a Population Proportion
• Determining the Required Sample Size for Estimating a Population Proportion
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 8.1 Management Solutions, Inc.
• Case 8.2 Federal Aviation Administration
• Case 8.3 Cell Phone Use
• Chapter 9. Introduction to Hypothesis Testing
• 9.1 Hypothesis Tests for Means
• Formulating the Hypotheses
• Significance Level and Critical Value
• Hypothesis Test for μ, σ Known
• Types of Hypothesis Tests
• p-Value for Two-Tailed Tests
• Hypothesis Test for μ, σ Unknown
• 9.2 Hypothesis Tests for a Proportion
• Testing a Hypothesis about a Single Population Proportion
• 9.3 Type II Errors
• Calculating Beta
• Controlling Alpha and Beta
• Power of the Test
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 9.1 Carpita Bottling Company—Part 2
• Case 9.2 Wings of Fire
• Chapter 10. Estimation and Hypothesis Testing for Two Population Parameters
• 10.1 Estimation for Two Population Means Using Independent Samples
• Estimating the Difference between Two Population Means When σ1 and σ2 Are Known, Using Independent
• Estimating the Difference between Two Population Means When σ1 and σ2 Are Unknown, Using Independe
• 10.2 Hypothesis Tests for Two Population Means Using Independent Samples
• Testing for μ1 − μ2 When σ1 and σ2 Are Known, Using Independent Samples
• Testing for μ1 − μ2 When σ1 and σ2 Are Unknown, Using Independent Samples
• 10.3 Interval Estimation and Hypothesis Tests for Paired Samples
• Why Use Paired Samples?
• Hypothesis Testing for Paired Samples
• 10.4 Estimation and Hypothesis Tests for Two Population Proportions
• Estimating the Difference between Two Population Proportions
• Hypothesis Tests for the Difference between Two Population Proportions
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 10.1 Larabee Engineering—Part 1
• Case 10.2 Hamilton Marketing Services
• Case 10.3 Green Valley Assembly Company
• Case 10.4 U-Need-It Rental Agency
• Chapter 11. Hypothesis Tests and Estimation for Population Variances
• 11.1 Hypothesis Tests and Estimation for a Single Population Variance
• Chi-Square Test for One Population Variance
• Interval Estimation for a Population Variance
• 11.2 Hypothesis Tests for Two Population Variances
• F-Test for Two Population Variances
• Summary
• Equations
• Key Term
• Chapter Exercises
• Case 11.1 Larabee Engineering—Part 2
• Chapter 12. Analysis of Variance
• 12.1 One-Way Analysis of Variance
• Introduction to One-Way ANOVA
• Partitioning the Sum of Squares
• The ANOVA Assumptions
• Applying One-Way ANOVA
• The Tukey-Kramer Procedure for Multiple Comparisons
• Fixed Effects Versus Random Effects in Analysis of Variance
• 12.2 Randomized Complete Block Analysis of Variance
• Randomized Complete Block ANOVA
• Fisher’s Least Significant Difference Test
• 12.3 Two-Factor Analysis of Variance with Replication
• Two-Factor ANOVA with Replications
• A Caution about Interaction
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 12.1 Agency for New Americans
• Case 12.2 McLaughlin Salmon Works
• Case 12.3 NW Pulp and Paper
• Case 12.4 Brown Restoration
• Business Statistics Capstone Project
• Chapters 8–12. Special Review Section
• Chapters 8–12
• Using the Flow Diagrams
• Exercises
• Chapter 13. Goodness-of-Fit Tests and Contingency Analysis
• 13.1 Introduction to Goodness-of-Fit Tests
• Chi-Square Goodness-of-Fit Test
• 13.2 Introduction to Contingency Analysis
• 2 × 2 Contingency Tables
• r × c Contingency Tables
• Chi-Square Test Limitations
• Summary
• Equations
• Key Term
• Chapter Exercises
• Case 13.1 National Oil Company—Part 2
• Case 13.2 Bentford Electronics—Part 1
• Chapter 14. Introduction to Linear Regression and Correlation Analysis
• 14.1 Scatter Plots and Correlation
• The Correlation Coefficient
• 14.2 Simple Linear Regression Analysis
• The Regression Model Assumptions
• Meaning of the Regression Coefficients
• Least Squares Regression Properties
• Significance Tests in Regression Analysis
• 14.3 Uses for Regression Analysis
• Regression Analysis for Description
• Regression Analysis for Prediction
• Common Problems Using Regression Analysis
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 14.1 A & A Industrial Products
• Case 14.2 Sapphire Coffee—Part 1
• Case 14.3 Alamar Industries
• Case 14.4 Continental Trucking
• Chapter 15. Multiple Regression Analysis and Model Building
• 15.1 Introduction to Multiple Regression Analysis
• Basic Model-Building Concepts
• 15.2 Using Qualitative Independent Variables
• 15.3 Working with Nonlinear Relationships
• Analyzing Interaction Effects
• Partial F-Test
• 15.4 Stepwise Regression
• Forward Selection
• Backward Elimination
• Standard Stepwise Regression
• Best Subsets Regression
• 15.5 Determining the Aptness of the Model
• Analysis of Residuals
• Corrective Actions
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 15.1 Dynamic Weighing, Inc.
• Case 15.2 Glaser Machine Works
• Case 15.3 Hawlins Manufacturing
• Case 15.4 Sapphire Coffee—Part 2
• Case 15.5 Wendell Motors
• Chapter 16. Analyzing and Forecasting Time-Series Data
• 16.1 Introduction to Forecasting and Time-Series Data
• General Forecasting Issues
• Components of a Time Series
• Introduction to Index Numbers
• Using Index Numbers to Deflate a Time Series
• 16.2 Trend-Based Forecasting Techniques
• Developing a Trend-Based Forecasting Model
• Comparing the Forecast Values to the Actual Data
• Nonlinear Trend Forecasting
• Adjusting for Seasonality
• 16.3 Forecasting Using Smoothing Methods
• Exponential Smoothing
• Forecasting with Excel
• Summary
• Equations
• Key Terms
• Chapter Exercises
• Case 16.1 Park Falls Chamber of Commerce
• Case 16.2 The St. Louis Companies
• Case 16.3 Wagner Machine Works
• Chapter 17. Introduction to Nonparametric Statistics
• 17.1 The Wilcoxon Signed Rank Test for One Population Median
• The Wilcoxon Signed Rank Test—Single Population
• 17.2 Nonparametric Tests for Two Population Medians
• The Mann–Whitney U-Test
• Mann–Whitney U-Test—Large Samples
• 17.3 Kruskal–Wallis One-Way Analysis of Variance
• Limitations and Other Considerations
• Summary
• Equations
• Chapter Exercises
• Case 17.1 Bentford Electronics—Part 2
• Chapter 18. Introducing Business Analytics
• 18.1 What Is Business Analytics?
• Descriptive Analytics
• Predictive Analytics
• 18.2 Data Visualization Using Microsoft Power BI Desktop
• Using Microsoft Power BI Desktop
• Summary
• Key Terms
• Case 18.1 New York City Taxi Trips
• Appendices
• Appendix A. Random Numbers Table
• Appendix B. Cumulative Binomial Distribution Table
• Appendix C. Cumulative Poisson Probability Distribution Table
• Appendix D. Standard Normal Distribution Table
• Appendix E. Exponential Distribution Table
• Appendix F. Values of t for Selected Probabilities
• Appendix G. Values of x2 for Selected Probabilities
• Appendix H. F-Distribution Table
• Appendix I. Distribution of the Studentized Range (q-values)
• Appendix J. Critical Values of r in the Runs Test
• Appendix K. Mann-Whitney U Test Probabilities (n < 9)
• Appendix L. Mann-Whitney U Test Critical Values (9 ≤ n ≤ 20)
• Appendix M. Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n ≤ 25)
• Appendix N. Critical Values dL and du of the Durbin-Watson Statistic D
• Appendix O. Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test
• Appendix P. Control Chart Factors
• Answers to Selected Odd-Numbered Exercises
• References
• Glossary
• 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
• Z