Statistics for Business & Economics, Global Edition, 14th Edition

Cover
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 Filing Your Federal Tax Return
	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