Statistics for managers using Microsoft Excel

Cover
A Roadmap for Selectinga Statistical Method
Title Page
Copyright Page
Dedication Page
About the Authors
Brief Contents
Contents
Preface
Resources for Success
First Things First
	Using Statistics: “The Price of Admission”
		Now Appearing on Broadway . . . and Everywhere Else
	FTF.1 Think Differently About Statistics
		Statistics: A Way of Thinking
		Analytical Skills More Important than Arithmetic Skills
		Statistics: An Important Part of Your Business Education
	FTF.2 Business Analytics: The Changing Face of Statistics
		“Big Data”
		Structured Versus Unstructured Data
	FTF.3 Getting Started Learning Statistics
		Statistic
		Can Statistics (pl., Statistic) Lie?
	FTF.4 Preparing to Use Microsoft Excel for Statistics
		Reusability Through Recalculation
		Practical Matters: Skills You Need
		Ways of Working with Excel
		Excel Guides
		Which Excel Version to Use?
		Conventions Used
	References
	Key Terms
	Excel Guide
		EG.1 Entering Data
		EG.2 Reviewing Worksheets
		EG.3 If You Plan to Use the Workbook Instructions
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 Preparation
		Data Cleaning
		Data Formatting
		Stacked and Unstacked Variables
		Recoding Variables
	1.5 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
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 and Visualizing a Mix of Variables
		Multidimensional Contingency Table
		Adding a Numerical Variable to a Multidimensional Contingency Table
		Drill Down
		Excel Slicers
		PivotChart
		Sparklines
	2.7 The Challenge in Organizing and Visualizing Variables
		Obscuring Data
		Creating False Impressions
		Chartjunk
		EXHIBIT: Best Practices for Creating Visualizations
	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
		EG2.3 Visualizing Categorical Variables
		EG2.4 Visualizing Numerical Variables
		EG2.5 Visualizing Two Numerical Variables
		EG2.6 Organizing and Visualizing a Set of Variables
3 Numerical Descriptive Measures
	Using Statistics: More Descriptive Choices
	3.1 Central Tendency
		The Mean
		The Median
		The Mode
		The Geometric Mean
	3.2 Variation and Shape
		The Range
		The Variance and the Standard Deviation
		EXHIBIT: Manually Calculating the Sample Variance, S2, and Sample Standard Deviation, S
		The Coefficient of Variation
		Z Scores
		Shape: Skewness
		Shape: Kurtosis
	3.3 Exploring Numerical Data
		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 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 3 136
		Managing Ashland MultiComm Services
		Digital Case
		CardioGood Fitness
		More Descriptive Choices Follow-up
		Clear Mountain State Student Survey
	Chapter 3 Excel Guide
		EG3.1 Central Tendency
		EG3.2 Variation and Shape
		EG3.3 Exploring Numerical Data
		EG3.4 Numerical Descriptive Measures for a Population
		EG3.5 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
		Contingency Tables
		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
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
	5.3 Poisson Distribution
	5.4 Covariance of a Probability Distribution and its Application in Finance
	5.5 Hypergeometric Distribution
	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
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
		Computing 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
	6.6 The Normal Approximation to the Binomial Distribution
	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
		EG6.1 Continuous Probability Distributions
		EG6.2 The Normal Distribution
		EG6.3 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
	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
8 Confidence Interval Estimation
	Using Statistics: Getting Estimates at Ricknel Home Centers
	8.1 Confidence Interval Estimate for the Mean (s Known)
		Can You Ever Know the Population Standard Deviation?
	8.2 Confidence Interval Estimate for the Mean (s Unknown)
		Student’s t Distribution
		Properties of the t Distribution
		The Concept of Degrees of Freedom
		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
	8.7 Estimation and Sample Size Estimation for Finite Populations
	8.8 Bootstrapping
	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 (s Known)
		EG8.2 Confidence Interval Estimate for the Mean (s Unknown)
		EG8.3 Confidence Interval Estimate for the Proportion
		EG8.4 Determining Sample Size
9 Fundamentals of Hypothesis Testing: One-Sample Tests
	Using Statistics: Significant Testing at Oxford Cereals
	9.1 Fundamentals of Hypothesis-Testing Methodology
		The Null and Alternative Hypotheses
		The Critical Value of the Test Statistic
		Regions of Rejection and Nonrejection
		Risks in Decision Making Using Hypothesis Testing
		Z Test for the Mean (s 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 (s 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
	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
	Chapter 9 Excel Guide
		EG9.1 Fundamentals of Hypothesis-Testing Methodology
		EG9.2 t Test of Hypothesis for the Mean (s Unknown)
		EG9.3 One-Tail Tests
		EG9.4 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
		Confidence Interval Estimate for the Difference Between Two Means
		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
	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
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
	11.4 Fixed Effects, Random Effects, and Mixed Effects Models
	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 Phase 1
		Phase 2
		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
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: A Nonparametric Method for Two Independent Populations
	12.5 Kruskal-Wallis Rank Test: A Nonparametric Method for the One-Way ANOVA
		Assumptions
	12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)
	12.7 Chi-Square Test for the Variance or Standard Deviation
	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 Phase 1
		Phase 2
		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
13 Simple Linear Regression
	Using Statistics: Knowing Customers at Sunflowers Apparel
	13.1 Types of Regression Models
		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, b0 and the Slope, b1
		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: Six 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
		Brynne 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
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 r2, Adjusted r2, and the Overall F Test
		Coefficient of Multiple Determination
		Adjusted r2
		Test for the Significance of the Overall Multiple Regression Model
	14.3 Residual Analysis for the Multiple Regression Model
	14.4 Inferences Concerning 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 in Regression Models
		Interactions
	14.7 Logistic Regression
	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 r2, Adjusted r2, and the Overall F Test
		EG14.3 Residual Analysis for the Multiple Regression Model
		EG14.4 Inferences Concerning the Population Regression Coefficients
		EG14.5 Testing Portions of the Multiple Regression Model
		EG14.6 Using Dummy Variables and Interaction Terms in Regression Models
		EG14.7 Logistic Regression
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
		The Stepwise Regression Approach to Model Building
		The Best Subsets Approach to Model Building
		Model Validation
		EXHIBIT: Steps for Successful Model Building
	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
16 Time-Series Forecasting
	Using Statistics: Principled Forecasting
	16.1 The Importance of Business Forecasting
	16.2 Component Factors of Time-Series Models
	16.3 Smoothing an Annual Time Series
		Moving Averages
		Exponential Smoothing
	16.4 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
	16.5 Autoregressive Modeling for Trend Fitting and Forecasting
		Selecting an Appropriate Autoregressive Model
		Determining the Appropriateness of a Selected Model
		EXHIBIT: Autoregressive Modeling Steps
	16.6 Choosing an Appropriate Forecasting Model
		Performing a Residual Analysis
		Measuring the Magnitude of the Residuals Through Squared or Absolute Differences
		Using the Principle of Parsimony
		A Comparison of Four Forecasting Methods
	16.7 Time-Series Forecasting of Seasonal Data
		Least-Squares Forecasting with Monthly or Quarterly Data
	16.8 Index Numbers
	CONSIDER THIS: Let the Model User Beware
	Using Statistics: Principled Forecasting, 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
		Eg16.3 Smoothing an Annual Time Series
		Eg16.4 Least-Squares Trend Fitting and Forecasting
		Eg16.5 Autoregressive Modeling for Trend Fitting and Forecasting
		Eg16.6 Choosing an Appropriate Forecasting Model
		Eg16.7 Time-Series Forecasting of Seasonal Data
17 Getting Ready to Analyze Data in the Future
	Using Statistics: Mounting Future Analyses
	17.1 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?
		Determine Whether the Values of a Variable Are Stable Over Time?
	17.2 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?
		Determine Whether the Proportion of Items of Interest Is Stable Over Time?
	Using Statistics: Back to Arlingtons for the Future
	17.3 Introduction to Business Analytics
		Data Mining
		Power Pivot
	17.4 Descriptive Analytics
		Dashboards
		Dashboard Elements
	17.5 Predictive Analytics
		Classification and Regression Trees
	Using Statistics: The Future to be Visited
	References
	Chapter Review Problems
	Chapter 17 Excel Guide
		EG17.3 Introduction to Business Analytics
		EG17.4 Descriptive Analytics
18 Statistical Applications in Quality Management (online)
	Using Statisßtics: Finding Quality at the Beachcomber
	18.1 The Theory of Control Charts
	18.2 Control Chart for the Proportion: The p Chart
	18.3 The Red Bead Experiment: Understanding Process Variability
	18.4 Control Chart for an Area of Opportunity: The c Chart
	18.5 Control Charts for the Range and the Mean
		The R Chart
		The _X Chart
	18.6 Process Capability
		Customer Satisfaction and Specification Limits
		Capability Indices
		CPL, CPU, and Cpk
	18.7 Total Quality Management
	18.8 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
	The Harnswell Sewing Machine Company Case
	Managing Ashland Multicomm Services
	Chapter 18 Excel Guide
		EG18.1 The Theory of Control Charts
		EG18.2 Control Chart for the Proportion: The p Chart
		EG18.3 The Red Bead Experiment: Understanding Process Variability
		EG18.4 Control Chart for an Area of Opportunity: The c Chart
		EG18.5 Control Charts for the Range and the Mean
		EG18.6 Process Capability
19 Decision Making (online)
	Using Statistics: Reliable Decision Making
	19.1 Payoff Tables and Decision Trees
	19.2 Criteria for Decision Making
		Maximax Payoff
		Maximin Payoff
		Expected Monetary Value
		Expected Opportunity Loss
		Return-to-Risk Ratio
	19.3 Decision Making with Sample Information
	19.4 Utility 1
	Consider This: Risky Business
	Using Statistics: Reliable Decision-Making, Revisited
	Summary
	References
	Key Equations
	Key Terms
	Chapter Review Problems
	Cases For Chapter 19
		Digital Case 19-26
	Chapter 19 Excel Guide
		EG19.1 Payoff Tables and Decision Trees
		EG19.2 Criteria for Decision Making
Appendices
	A. Basic Math Concepts and Symbols
		A.1 Rules for Arithmetic Operations
		A.2 Rules for Algebra: Exponents and Square Roots
		A.3 Rules for Logarithms
		A.4 Summation Notation
		A.5 Statistical Symbols
		A.6 Greek Alphabet
	B Important Excel Skills and Concepts
		B.1 Which Excel Do You Use?
		B.2 Basic Operations
		B.3 Formulas and Cell References
		B.4 Entering a Formula
		B.5 Formatting Cell Contents
		B.6 Formatting Charts
		B.7 Selecting Cell Ranges for Charts
		B.8 Deleting the “Extra” Histogram Bar
		B.9 Creating Histograms for Discrete Probability Distributions
	C. Online Resources
		C.1 About the Online Resources for This Book
		C.2 Accessing the Online Resources
		C.3 Details of Online Resources
		C.4 PHStat
	D. Configuring Microsoft Excel
		D.1 Getting Microsoft Excel Ready for Use
		D.2 Checking for the Presence of the Analysis ToolPak or Solver Add-Ins
		D.3 Configuring Microsoft Windows Excel Security Settings
		D.4 Opening Pearson-Supplied Add-Ins
	E. Tables
		E.1 Table of Random Numbers
		E.2 The Cumulative Standardized Normal Distribution
		E.3 Critical Values of t
		E.4 Critical Values of x2
		E.5 Critical Values of F
		E.6 Lower and Upper Critical Values, T1, of the Wilcoxon Rank Sum Test
		E.7 Critical Values of the Studentized Range, Q
		E.8 Critical Values, dL and dU, of the Durbin–Watson Statistic, D (Critical Values Are One-Sided)
		E.9 Control Chart Factors
		E.10 The Standardized Normal Distribution
	F. Useful Excel Knowledge
		F.1 Useful Keyboard Shortcuts
		F.2 Verifying Formulas and Worksheets
		F.3 New Function Names
		F.4 Understanding the Nonstatistical Functions
	G. Software FAQs 659
		G.1 PHStat FAQs
		G.2 Microsoft Excel FAQs
Self-Test Solutions and Answers to Selected Even-Numbered Problems
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y
	Z
Credits
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The Cumulative Standardized Normal Distribution