Basic Business Statistics, Global Edition

Cover
Half Title Page
Title Page
Copyright Page
About the Authors
Brief Contents
Contents
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
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
JMP Guide
JG.1 Getting Started with JMP
JG.2 Entering Data
JG.3 Create New Project or Data Table
JG.4 Open or Save Files
JG.5 Print Data Tables or Report Windows
JG.6 JMP Script Files
MINITAB GUIDE
MG.1 Getting Started with Minitab
MG.2 Entering Data
MG.3 Open or Save Files
MG.4 Insert or Copy Worksheets
MG.5 Print Worksheets
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 Collecting Data
EG1.3 Types of Sampling Methods
EG1.4 Data Cleaning
EG1.5 Other Data Preprocessing
Chapter 1 JMP Guide
JG1.1 Defining Variables
JG1.2 Collecting Data
JG1.3 Types of Sampling Methods
JG1.4 Data Cleaning
JG1.5 Other Preprocessing Tasks
Chapter 1 MINITAB Guide
MG1.1 Defining Variables
MG1.2 Collecting Data
MG1.3 Types of Sampling Methods
MG1.4 Data Cleaning
MG1.5 Other Preprocessing Tasks
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
Classes and Excel Bins
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
Bubble Charts
PivotChart (Excel)
Treemap (Excel, JMP)
Sparklines (Excel)
2.8 Filtering and Querying Data
Excel Slicers
2.9 Pitfalls in Organizing and Visualizing Variables
Obscuring Data
Creating False Impressions
Chartjunk
Exhibit: Best Practices for Creating Visual Summaries
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 Charts Group Reference
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 JMP Guide
JG2 JMP Choices for Creating Summaries
JG2.1 Organizing Categorical Variables
JG2.2 Organizing Numerical Variables
JG2.3 Visualizing Categorical Variables
JG2.4 Visualizing Numerical Variables
JG2.5 Visualizing Two Numerical Variables
JG2.6 Organizing a Mix of Variables
JG2.7 Visualizing a Mix of Variables
JG2.8 Filtering and Querying Data
JMP Guide Gallery
Chapter 2 MINITAB GUIDE
MG2.1 Organizing Categorical Variables
MG2.2 Organizing Numerical Variables
MG2.3 Visualizing Categorical Variables
MG2.4 Visualizing Numerical Variables
MG2.5 Visualizing Two Numerical Variables
MG2.6 Organizing a Mix of Variables
MG2.7 Visualizing a Mix of Variables
MG2.8 Filtering and Querying Data
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
Exhibit: Rules for Calculating the Quartiles from a Set of Ranked Values
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 JMP GUIDE
JG3.1 Measures of Central Tendency
JG3.2 Measures of Variation and Shape
JG3.3 Exploring Numerical Variables
JG3.4 Numerical Descriptive Measures for a Population
JG3.5 The Covariance and the Coefficient of Correlation
Chapter 3 MINITAB Guide
MG3.1 Measures of Central Tendency
MG3.2 Measures of Variation and Shape
MG3.3 Exploring Numerical Variables
MG3.4 Numerical Descriptive Measures for a Population
MG3.5 The Covariance and the Coefficient of Correlation
4 Basic Probability
USING STATISTICS: Possibilities at M&R Electronics World
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
Computing 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: Possibilities at M&R Electronics World, 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
EG4.4 Bayes’ Theorem
EG4.5 Counting Rules
Chapter 4 JMP GUIDE
JG4.4 Bayes’ Theorem
Chapter 4 MINITAB Guide
MG4.5 Counting Rules
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
Exhibit: Properties of the 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 (online)
5.6 Using the Poisson Distribution to Approximate the Binomial Distribution (online)
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
Chapter 5 JMP Guide
JG5.1 The Probability Distribution for a Discrete Variable
JG5.2 Binomial Distribution
JG5.3 Poisson Distribution
Chapter 5 MINITAB Guide
MG5.1 The Probability Distribution for a Discrete Variable
MG5.2 Binomial Distribution
MG5.3 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
Exhibit: Normal Distribution Important Theoretical Properties
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 (online)
6.6 The Normal Approximation to the Binomial Distribution (online)
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
EG6.2 The Normal Distribution
EG6.3 Evaluating Normality
Chapter 6 JMP Guide
JG6.2 The Normal Distribution
JG6.3 Evaluating Normality
Chapter 6 MINITAB Guide
MG6.2 The Normal Distribution
MG6.3 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
Exhibit: Normality and the Sampling Distribution of the Mean
VISUAL EXPLORATIONS: Exploring Sampling Distributions
7.3 Sampling Distribution of the Proportion
7.4 Sampling from Finite Populations (online)
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
EG7.2 Sampling Distribution of the Mean
Chapter 7 JMP Guide
JG7.2 Sampling Distribution of the Mean
Chapter 7 MINITAB GUIDE
MG7.2 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 ( Known)
Sampling Error
Can You Ever Know the Population Standard Deviation?
8.2 Confidence Interval Estimate for the Mean ( 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 (online)
8.7 Estimation and Sample Size Estimation for Finite Populations (online)
8.8 Bootstrapping (online)
USING STATISTICS: Getting Estimates , 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 ( Known)
EG8.2 Confidence Interval Estimate for the Mean ( Unknown)
EG8.3 Confidence Interval Estimate for the Proportion
EG8.4 Determining Sample Size
Chapter 8 JMP Guide
JG8.1 Confidence Interval Estimate for the Mean ( Known)
JG8.2 Confidence Interval Estimate for the Mean ( Unknown)
JG8.3 Confidence Interval Estimate for the Proportion
JG8.4 Determining Sample Size
Chapter 8 MINITAB Guide
MG8.1 Confidence Interval Estimate for the Mean ( Known)
MG8.2 Confidence Interval Estimate for the Mean ( Unknown)
MG8.3 Confidence Interval Estimate for the Proportion
MG8.4 Determining Sample Size
9 Fundamentals of Hypothesis Testing: One-Sample Tests
USING STATISTICS: Significant Testing at Oxford Cereals
9.1 Fundamentals of Hypothesis Testing
Exhibit: Fundamental Hypothesis Testing Concepts
The Critical Value of the Test Statistic
Regions of Rejection and Nonrejection
Risks in Decision Making Using Hypothesis Testing
Z Test for the Mean ( Known)
Hypothesis Testing Using the Critical Value Approach
Exhibit: The Critical Value Approach to Hypothesis Testing
Hypothesis Testing Using the p-Value Approach
Exhibit: The p-Value Approach to Hypothesis Testing
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 ( Unknown)
The Critical Value Approach
p-Value Approach
Checking the Normality Assumption
9.3 One-Tail Tests
The Critical Value Approach
The p-Value Approach
Exhibit: The Null and Alternative Hypotheses in One-Tail Tests
9.4 Z Test of Hypothesis for the Proportion
The Critical Value Approach
The p-Value Approach
9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues
Exhibit: Questions for the Planning Stage of Hypothesis Testing
Statistical Significance Versus Practical Significance
Statistical Insignificance Versus Importance
Reporting of Findings
Ethical Issues
9.6 Power of the Test (online)
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
Chpater 9 Excel Guide
EG9.1 Fundamentals of Hypothesis Testing
EG9.2 t Test of Hypothesis for the Mean ( Unknown)
EG9.3 One-Tail Tests
EG9.4 Z Test of Hypothesis for the Proportion
CHAPTER 9 JMP Guide
JG9.1 Fundamentals of Hypothesis Testing
JG9.2 t Test of Hypothesis for the Mean ( Unknown)
JG9.3 One-Tail Tests
JG9.4 Z Test of Hypothesis for the Proportion
Chapter 9 MINITAB Guide
MG9.1 Fundamentals of Hypothesis Testing
MG9.2 t Test of Hypothesis for the Mean ( Unknown)
MG9.3 One-Tail Tests
MG9.4 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 (online)
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
CHAPTER 10 JMP Guide
JG10.1 Comparing the Means of Two Independent Populations
JG10.2 Comparing the Means of Two Related Populations
JG10.3 Comparing the Proportions of Two Independent Populations
JG10.4 F Test for the Ratio of Two Variances
CHAPTER 10 MINITAB Guide
MG10.1 Comparing the Means of Two Independent Populations
MG10.2 Comparing the Means of Two Related Populations
MG10.3 Comparing the Proportions of Two Independent Populations
MG10.4 F Test for the Ratio of Two Variances
11 Analysis of Variance
USING STATISTICS: The Means to Find Differences at Arlingtons
11.1 The Completely Randomized Design: One-Way ANOVA
Analyzing Variation in 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
The Analysis of Means (ANOM)
11.2 The Factorial Design: 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 (online)
11.4 Fixed Effects, Random Effects, and Mixed Effects Models (online)
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
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
CHAPTER 11 JMP Guide
JG11.1 The Completely Randomized Design: One-Way Anova
JG11.2 The Factorial Design: Two-Way Anova
CHAPTER 11 MINITAB Guide
MG11.1 The Completely Randomized Design: One-Way Anova
MG11.2 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) (online)
12.7 Chi-Square Test for the Variance or Standard Deviation (online)
12.8 Wilcoxon Signed Ranks Test for Two Related Populations (online)
12.9 Friedman Rank Test for the Randomized Block Design (online)
USING STATISTICS: Avoiding Guesswork , Revisited
SUMMARY
REFERENCES
KEY EQUATIONS
Key Terms
CHECKING YOUR UNDERSTANDING
CHAPTER REVIEW PROBLEMS
CASES for CHAPTER 12
Managing Ashland MultiComm Services
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
CHAPTER 12 JMP Guide
JG12.1 Chi‐Square Test for the Difference Between Two Proportions
JG12.2 Chi‐Square Test tor Difference Among More Than Two Proportions
JG12.3 Chi‐Square Test Of Independence
JG12.4 Wilcoxon Rank Sum Test for Two Independent Populations
JG12.5 Kruskal‐Wallis Rank Test for the One‐Way Anova
CHAPTER 12 MINITAB Guide
MG12.1 Chi‐Square Test for the Difference Between Two Proportions
MG12.2 Chi‐Square Test for Differences Among More Than Two Proportions
MG12.3 Chi‐Square Test of Independence
MG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for Two Independent Populations
MG12.5 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
Computing the Y Intercept, and the Slope,
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
Exhibit: Seven Steps for Avoiding the Potential Pitfalls
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
Rye Packaging
CHAPTER 13 Excel Guide
EG13.2 Determining the Simple Linear Regression Equation
EG13.3 Measures of Variation
EG13.4 Assumptions of Regression
EG13.5 Residual Analysis
EG13.6 Measuring Autocorrelation: the Durbin‐Watson Statistic
EG13.7 Inferences about the Slope and Correlation Coefficient
EG13.8 Estimation of Mean Values and Prediction of Individual Values
CHAPTER 13 JMP Guide
JG13.2 Determining the Simple Linear Regression Equation
JG13.3 Measures of Variation
JG13.4 Assumptions of Regression
JG13.5 Residual Analysis
JG13.6 Measuring Autocorrelation: the Durbin‐Watson Statistic
JG13.7 Inferences about the Slope and Correlation Coefficient
JG13.8 Estimation of Mean Values and Prediction of Individual Values
CHAPTER 13 MINITAB Guide
MG13.2 Determining the Simple Linear Regression Equation
MG13.3 Measures of Variation
MG13.4 Assumptions of Regression
MG13.5 Residual Analysis
MG13.6 Measuring Autocorrelation: the Durbin‐Watson Statistic
MG13.7 Inferences about the Slope and Correlation Coefficient
MG13.8 Estimation of Mean Values and Prediction of Individual Values
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 Adjusted and the Overall F Test
Coefficient of Multiple Determination
Adjusted
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
14.7 Logistic Regression
14.8 Influence Analysis (online)
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 Adjusted and the Overall F Test
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
Chapter 14 JMP Guide
JG14.1 Developing a Multiple Regression Model
JG14.2 Adjusted and the Overall F Test Measures of Variation
JG14.3 Multiple Regression Residual Analysis
JG14.4 Inferences about the Population
JG14.5 Testing Portions of the Multiple Regression Model
JG14.6 Using Dummy Variables and Interaction Terms
JG14.7 Logistic Regression
Chapter 14 MINITAB Guide
MG14.1 Developing a Multiple Regression Model
MG14.2 Adjusted and the Overall F Test
MG14.3 Multiple Regression Residual Analysis
MG14.4 Inferences about the Population Regression Coefficients
MG14.5 Testing Portions of the Multiple Regression Model
MG14.6 Using Dummy Variables and Interaction Terms in Regression Models
MG14.7 Logistic Regression
MG14.8 Influence Analysis
15 Multiple Regression Model Building
USING STATISTICS: Valuing Parsimony at WSTA‐TV
15.1 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
Exhibit: Sucessful Model Building
The Stepwise Regression Approach to Model Building
The Best Subsets Approach to Model Building
Model Validation
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
Chapter 15 JMP Guide
JG15.1 The Quadratic Regression Model
JG15.2 Using Transformations in Regression Models
JG15.3 Collinearity
JG15.4 Model Building
Chapter 15 MINITAB Guide
MG15.1 The Quadratic Regression Model
MG15.2 Using Transformations in Regression Models
MG15.3 Collinearity
MG15.4 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
Exhibit: 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
Exhibit: Autoregressive Modeling Steps
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 (online)
CONSIDER THIS: Let the Model User Beware
USING STATISTICS: Is the ByYourDoor , 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
EG16.2 Smoothing an Annual Time Series
EG16.3 Least‐Squares Trend Fitting and Forecasting
EG16.4 Autoregressive Modeling for Trend Fitting and Forecasting
EG16.5 Choosing an Appropriate Forecasting Model
EG16.6 Time‐Series Forecasting of Seasonal Data
Chapter 16 JMP Guide
JG16.2 Smoothing an Annual Time Series
JG16.3 Least‐Squares Trend Fitting and Forecasting
JG16.4 Autoregressive Modeling for Trend Fitting and Forecasting
JG16.5 Choosing an Appropriate Forecasting Model
JG16.6 Time‐Series Forecasting of Seasonal Data
Chapter 16 MINITAB Guide
MG16.2 Smoothing an Annual Time Series
MG16.3 Least‐Squares Trend Fitting and Forecasting
MG16.4 Autoregressive Modeling for Trend Fitting and Forecasting
MG16.5 Choosing an Appropriate Forecasting Model
MG16.6 Time‐Series Forecasting of Seasonal Data
17 Business Analytics
USING STATISTICS: Back to Arlingtons for the Future
17.1 Business Analytics Categories
Inferential Statistics and Predictive Analytics
Supervised and Unsupervised Methods
CONSIDER THIS: What’s My Major if I Want to be a Data Miner?
17.2 Descriptive Analytics
Dashboards
Data Dimensionality and Descriptive Analytics
17.3 Predictive Analytics for Prediction
17.4 Predictive Analytics for Classification
17.5 Predictive Analytics for Clustering
17.6 Predictive Analytics for Association
Multidimensional scaling (MDS)
17.7 Text Analytics
17.8 Prescriptive Analytics
USING STATISTICS: Back to Arlingtons , Revisited
REFERENCES
KEY EQUATIONS
Key Terms
CHECKING YOUR UNDERSTANDING
CHAPTER REVIEW PROBLEMS
CASES for Chapter 17
The Mountain States Potato Company
The Craybill Instrumentation Company
Chapter 17 Software Guide
Introduction
SG17.2 Descriptive Analytics
SG17.3 Predictive Analytics for Prediction
SG17.4 Predictive Analytics for Classification
SG17.5 Predictive Analytics for Clustering
SG17.6 Predictive Analytics for Association
18 Getting Ready to Analyze Data in the Future
USING STATISTICS: Mounting Future Analyses
18.1 Analyzing Numerical Variables
Exhibit: Questions to Ask When 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
Exhibit: Questions to Ask When 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?
Classify 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 (online)
USING STATISTICS: Finding Quality at the Beachcomber
19.1 The Theory of Control Charts
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
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
Chapter 19 JMP Guide
JG19.2 Control Chart for the Proportion: The p Chart
JG19.4 Control Chart for an Area of Opportunity: The c Chart
JG19.5 Control Charts for the Range and the Mean
JG19.6 Process Capability
Chapter 19 MINITAB Guide
MG19.2 Control Chart for the Proportion: The p Chart
MG19.4 Control Chart for an Area of Opportunity: The c Chart
MG19.5 Control Charts for the Range and the Mean
MG19.6 Process Capability
20 Decision Making (online)
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
EG20.1 Payoff Tables and Decision Trees
EG20.2 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.5J JMP Chart Formatting
B.5M Minitab 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 Files Integrated With Microsoft Excel
C.4 Supplemental Files
D. Configuring Software
D.1 Microsoft Excel Configuration
D.2 JMP Configuration
D.3 Minitab Configuration
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
E.5 Critical Values of F
E.6 Lower and Upper Critical Values, of the Wilcoxon Rank Sum Test
E.7 Critical Values of the Studentized Range, Q
E.8 Critical Values, and 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 Functions
G. Software FAQs
G.1 Microsoft Excel FAQs
G.2 PHStat FAQs
G.3 JMP FAQs
G.4 Minitab 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