Basic Business Statistics Concepts And Applications

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