Efnisyfirlit

• 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

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