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Table of Contents
                            Front Cover
Title Page
Copyright
Contents
Preface
About the Authors
Chapter 1 Data and Statistics
	Statistics in Practice: BusinessWeek
	1.1 Applications in Business and Economics
		Accounting
		Finance
		Marketing
		Production
		Economics
	1.2 Data
		Elements, Variables, and Observations
		Scales of Measurement
		Categorical and Quantitative Data
		Cross-Sectional and Time Series Data
	1.3 Data Sources
		Existing Sources
		Statistical Studies
		Data Acquisition Errors
	1.4 Descriptive Statistics
	1.5 Statistical Inference
	1.6 Computers and Statistical Analysis
	1.7 Data Mining
	1.8 Ethical Guidelines for Statistical Practice
	Summary
	Glossary
	Supplementary Exercises
	Appendix: An Introduction to StatTools
Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations
	Statistics in Practice: Colgate-Palmolive Company
	2.1 Summarizing Categorical Data
		Frequency Distribution
		Relative Frequency and Percent Frequency Distributions
		Bar Charts and Pie Charts
	2.2 Summarizing Quantitative Data
		Frequency Distribution
		Relative Frequency and Percent Frequency Distributions
		Dot Plot
		Histogram
		Cumulative Distributions
		Ogive
	2.3 Exploratory Data Analysis: The Stem-and-Leaf Display
	2.4 Crosstabulations and Scatter Diagrams
		Crosstabulation
		Simpson's Paradox
		Scatter Diagram and Trendline
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Pelican Stores
	Case Problem 2: Motion Picture Industry
	Appendix 2.1 Using Minitab for Tabular and Graphical Presentations
	Appendix 2.2 Using Excel for Tabular and Graphical Presentations
	Appendix 2.3 Using StatTools for Tabular and Graphical Presentations
Chapter 3 Descriptive Statistics: Numerical Measures
	Statistics in Practice: Small Fry Design
	3.1 Measures of Location
		Mean
		Median
		Mode
		Percentiles
		Quartiles
	3.2 Measures of Variability
		Range
		Interquartile Range
		Variance
		Standard Deviation
		Coefficient of Variation
	3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
		Distribution Shape
		z-Scores
		Chebyshev's Theorem
		Empirical Rule
		Detecting Outliers
	3.4 Exploratory Data Analysis
		Five-Number Summary
		Box Plot
	3.5 Measures of Association Between Two Variables
		Covariance
		Interpretation of the Covariance
		Correlation Coefficient
		Interpretation of the Correlation Coefficient
	3.6 The Weighted Mean and Working with Grouped Data
		Weighted Mean
		Grouped Data
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Pelican Stores
	Case Problem 2: Motion Picture Industry
	Case Problem 3: Business Schools of Asia-Pacific
	Case Problem 4: Heavenly Chocolates Website Transactions
	Appendix 3.1 Descriptive Statistics Using Minitab
	Appendix 3.2 Descriptive Statistics Using Excel
	Appendix 3.3 Descriptive Statistics Using StatTools
Chapter 4 Introduction to Probability
	Statistics in Practice: Oceanwide Seafood
	4.1 Experiments, Counting Rules, and Assigning Probabilities
		Counting Rules, Combinations, and Permutations
		Assigning Probabilities
		Probabilities for the KP&L Project
	4.2 Events and Their Probabilities
	4.3 Some Basic Relationships of Probability
		Complement of an Event
		Addition Law
	4.4 Conditional Probability
		Independent Events
		Multiplication Law
	4.5 Bayes' Theorem
		Tabular Approach
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: Hamilton County Judges
Chapter 5 Discrete Probability Distributions
	Statistics in Practice: Citibank
	5.1 Random Variables
		Discrete Random Variables
		Continuous Random Variables
	5.2 Discrete Probability Distributions
	5.3 Expected Value and Variance
		Expected Value
		Variance
	5.4 Binomial Probability Distribution
		A Binomial Experiment
		Martin Clothing Store Problem
		Using Tables of Binomial Probabilities
		Expected Value and Variance for the Binomial Distribution
	5.5 Poisson Probability Distribution
		An Example Involving Time Intervals
		An Example Involving Length or Distance Intervals
	5.6 Hypergeometric Probability Distribution
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Appendix 5.1 Discrete Probability Distributions with Minitab
	Appendix 5.2 Discrete Probability Distributions with Excel
Chapter 6 Continuous Probability Distributions
	Statistics in Practice: Procter & Gamble
	6.1 Uniform Probability Distribution
		Area as a Measure of Probability
	6.2 Normal Probability Distribution
		Normal Curve
		Standard Normal Probability Distribution
		Computing Probabilities for Any Normal Probability Distribution
		Grear Tire Company Problem
	6.3 Normal Approximation of Binomial Probabilities
	6.4 Exponential Probability Distribution
		Computing Probabilities for the Exponential Distribution
		Relationship Between the Poisson and Exponential Distributions
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: Specialty Toys
	Appendix 6.1 Continuous Probability Distributions with Minitab
	Appendix 6.2 Continuous Probability Distributions with Excel
Chapter 7 Sampling and Sampling Distributions
	Statistics in Practice: MeadWestvaco Corporation
	7.1 The Electronics Associates Sampling Problem
	7.2 Selecting a Sample
		Sampling from a Finite Population
		Sampling from an Infinite Population
	7.3 Point Estimation
		Practical Advice
	7.4 Introduction to Sampling Distributions
	7.5 Sampling Distribution of x
		Expected Value of x
		Standard Deviation of x
		Form of the Sampling Distribution of x
		Sampling Distribution of x for the EAI Problem
		Practical Value of the Sampling Distribution of x
		Relationship Between the Sample Size and the Sampling Distribution of x
	7.6 Sampling Distribution of p
		Expected Value of p
		Standard Deviation of p
		Form of the Sampling Distribution of p
		Practical Value of the Sampling Distribution of p
	7.7 Properties of Point Estimators
		Unbiased
		Efficiency
		Consistency
	7.8 Other Sampling Methods
		Stratified Random Sampling
		Cluster Sampling
		Systematic Sampling
		Convenience Sampling
		Judgment Sampling
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Appendix 7.1 The Expected Value and Standard Deviation of x
	Appendix 7.2 Random Sampling with Minitab
	Appendix 7.3 Random Sampling with Excel
	Appendix 7.4 Random Sampling with StatTools
Chapter 8 Interval Estimation
	Statistics in Practice: Food Lion
	8.1 Population Mean: σ Known
		Margin of Error and the Interval Estimate
		Practical Advice
	8.2 Population Mean: σ Unknown
		Margin of Error and the Interval Estimate
		Practical Advice
		Using a Small Sample
		Summary of Interval Estimation Procedures
	8.3 Determining the Sample Size
	8.4 Population Proportion
		Determining the Sample Size
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Young Professional Magazine
	Case Problem 2: Gulf Real Estate Properties
	Case Problem 3: Metropolitan Research, Inc.
	Appendix 8.1 Interval Estimation with Minitab
	Appendix 8.2 Interval Estimation with Excel
	Appendix 8.3 Interval Estimation with StatTools
Chapter 9 Hypothesis Tests
	Statistics in Practice: John Morrell & Company
	9.1 Developing Null and Alternative Hypotheses
		The Alternative Hypothesis as a Research Hypothesis
		The Null Hypothesis as an Assumption to Be Challenged
		Summary of Forms for Null and Alternative Hypotheses
	9.2 Type I and Type II Errors
	9.3 Population Mean: σ Known
		One-Tailed Test
		Two-Tailed Test
		Summary and Practical Advice
		Relationship Between Interval Estimation and Hypothesis Testing
	9.4 Population Mean: σ Unknown
		One-Tailed Test
		Two-Tailed Test
		Summary and Practical Advice
	9.5 Population Proportion
		Summary
	9.6 Hypothesis Testing and Decision Making
	9.7 Calculating the Probability of Type II Errors
	9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Quality Associates, Inc.
	Case Problem 2: Ethical Behavior of Business Students at Bayview University
	Appendix 9.1 Hypothesis Testing with Minitab
	Appendix 9.2 Hypothesis Testing with Excel
	Appendix 9.3 Hypothesis Testing with StatTools
Chapter 10 Inference About Means and Proportions with Two Populations
	Statistics in Practice: U.S. Food and Drug Administration
	10.1 Inferences About the Difference Between Two Population Means: σ[sub(1)] and σ[sub(2)] Known
		Interval Estimation of μ[sub(1)] – μ[sub(2)]
		Hypothesis Tests About μ[sub(1)] – μ[sub(2)]
		Practical Advice
	10.2 Inferences About the Difference Between Two Population Means: σ[sub(1)] and σ[sub(2)] Unknown
		Interval Estimation of μ[sub(1)] – μ[sub(2)]
		Hypothesis Tests About μ[sub(1)] – μ[sub(2)]
		Practical Advice
	10.3 Inferences About the Difference Between Two Population Means: Matched Samples
	10.4 Inferences About the Difference Between Two Population Proportions
		Interval Estimation of p[sub(1)] – p[sub(2)]
		Hypothesis Tests About p[sub(1)] – p[sub(2)]
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: Par, Inc.
	Appendix 10.1 Inferences About Two Populations Using Minitab
	Appendix 10.2 Inferences About Two Populations Using Excel
	Appendix 10.3 Inferences About Two Populations Using StatTools
Chapter 11 Inferences About Population Variances
	Statistics in Practice: U.S. Government Accountability Office
	11.1 Inferences About a Population Variance
		Interval Estimation
		Hypothesis Testing
	11.2 Inferences About Two Population Variances
	Summary
	Key Formulas
	Supplementary Exercises
	Case Problem: Air Force Training Program
	Appendix 11.1 Population Variances with Minitab
	Appendix 11.2 Population Variances with Excel
	Appendix 11.3 Population Standard Deviation with StatTools
Chapter 12 Tests of Goodness of Fit and Independence
	Statistics in Practice: United Way
	12.1 Goodness of Fit Test: A Multinomial Population
	12.2 Test of Independence
	12.3 Goodness of Fit Test: Poisson and Normal Distributions
		Poisson Distribution
		Normal Distribution
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: A Bipartisan Agenda for Change
	Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab
	Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel
Chapter 13 Experimental Design and Analysis of Variance
	Statistics in Practice: Burke Marketing Services, Inc.
	13.1 An Introduction to Experimental Design and Analysis of Variance
		Data Collection
		Assumptions for Analysis of Variance
		Analysis of Variance: A Conceptual Overview
	13.2 Analysis of Variance and the Completely Randomized Design
		Between-Treatments Estimate of Population Variance
		Within-Treatments Estimate of Population Variance
		Comparing the Variance Estimates: The F Test
		ANOVA Table
		Computer Results for Analysis of Variance
		Testing for the Equality of k Population Means: An Observational Study
	13.3 Multiple Comparison Procedures
		Fisher’s LSD
		Type I Error Rates
	13.4 Randomized Block Design
		Air Traffic Controller Stress Test
		ANOVA Procedure
		Computations and Conclusions
	13.5 Factorial Experiment
		ANOVA Procedure
		Computations and Conclusions
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Wentworth Medical Center
	Case Problem 2: Compensation for Sales Professionals
	Appendix 13.1 Analysis of Variance with Minitab
	Appendix 13.2 Analysis of Variance with Excel
	Appendix 13.3 Analysis of Variance with StatTools
Chapter 14 Simple Linear Regression
	Statistics in Practice: Alliance Data Systems
	14.1 Simple Linear Regression Model
		Regression Model and Regression Equation
		Estimated Regression Equation
	14.2 Least Squares Method
	14.3 Coefficient of Determination
		Correlation Coefficient
	14.4 Model Assumptions
	14.5 Testing for Significance
		Estimate of σ[sup(2)]
		t Test
		Confidence Interval for β[sub(1)]
		F Test
		Some Cautions About the Interpretation of Significance Tests
	14.6 Using the Estimated Regression Equation for Estimation and Prediction
		Point Estimation
		Interval Estimation
		Confidence Interval for the Mean Value of y
		Prediction Interval for an Individual Value of y
	14.7 Computer Solution
	14.8 Residual Analysis: Validating Model Assumptions
		Residual Plot Against x
		Residual Plot Against y
		Standardized Residuals
		Normal Probability Plot
	14.9 Residual Analysis: Outliers and Influential Observations
		Detecting Outliers
		Detecting Influential Observations
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Measuring Stock Market Risk
	Case Problem 2: U.S. Department of Transportation
	Case Problem 3: Alumni Giving
	Case Problem 4: PGA Tour Statistics
	Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
	Appendix 14.2 A Test for Significance Using Correlation
	Appendix 14.3 Regression Analysis with Minitab
	Appendix 14.4 Regression Analysis with Excel
	Appendix 14.5 Regression Analysis with StatTools
Chapter 15 Multiple Regression
	Statistics in Practice: dunnhumby
	15.1 Multiple Regression Model
		Regression Model and Regression Equation
		Estimated Multiple Regression Equation
	15.2 Least Squares Method
		An Example: Butler Trucking Company
		Note on Interpretation of Coefficients
	15.3 Multiple Coefficient of Determination
	15.4 Model Assumptions
	15.5 Testing for Significance
		F Test
		t Test
		Multicollinearity
	15.6 Using the Estimated Regression Equation for Estimation and Prediction
	15.7 Categorical Independent Variables
		An Example: Johnson Filtration, Inc.
		Interpreting the Parameters
		More Complex Categorical Variables
	15.8 Residual Analysis
		Detecting Outliers
		Studentized Deleted Residuals and Outliers
		Influential Observations
		Using Cook’s Distance Measure to Identify Influential Observations
	15.9 Logistic Regression
		Logistic Regression Equation
		Estimating the Logistic Regression Equation
		Testing for Significance
		Managerial Use
		Interpreting the Logistic Regression Equation
		Logit Transformation
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Consumer Research, Inc.
	Case Problem 2: Alumni Giving
	Case Problem 3: PGA Tour Statistics
	Case Problem 4: Predicting Winning Percentage for the NFL
	Appendix 15.1 Multiple Regression with Minitab
	Appendix 15.2 Multiple Regression with Excel
	Appendix 15.3 Logistic Regression with Minitab
	Appendix 15.4 Multiple Regression with StatTools
Chapter 16 Regression Analysis: Model Building
	Statistics in Practice: Monsanto Company
	16.1 General Linear Model
		Modeling Curvilinear Relationships
		Interaction
		Transformations Involving the Dependent Variable
		Nonlinear Models That Are Intrinsically Linear
	16.2 Determining When to Add or Delete Variables
		General Case
		Use of p-Values
	16.3 Analysis of a Larger Problem
	16.4 Variable Selection Procedures
		Stepwise Regression
		Forward Selection
		Backward Elimination
		Best-Subsets Regression
		Making the Final Choice
	16.5 Multiple Regression Approach to Experimental Design
	16.6 Autocorrelation and the Durbin-Watson Test
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Analysis of PGA Tour Statistics
	Case Problem 2: Fuel Economy for Cars
	Appendix 16.1 Variable Selection Procedures with Minitab
	Appendix 16.2 Variable Selection Procedures with StatTools
Chapter 17 Index Numbers
	Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics
	17.1 Price Relatives
	17.2 Aggregate Price Indexes
	17.3 Computing an Aggregate Price Index from Price Relatives
	17.4 Some Important Price Indexes
		Consumer Price Index
		Producer Price Index
		Dow Jones Averages
	17.5 Deflating a Series by Price Indexes
	17.6 Price Indexes: Other Considerations
		Selection of Items
		Selection of a Base Period
		Quality Changes
	17.7 Quantity Indexes
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
Chapter 18 Time Series Analysis and Forecasting
	Statistics in Practice: Nevada Occupational Health Clinic
	18.1 Time Series Patterns
		Horizontal Pattern
		Trend Pattern
		Seasonal Pattern
		Trend and Seasonal Pattern
		Cyclical Pattern
		Selecting a Forecasting Method
	18.2 Forecast Accuracy
	18.3 Moving Averages and Exponential Smoothing
		Moving Averages
		Weighted Moving Averages
		Exponential Smoothing
	18.4 Trend Projection
		Linear Trend Regression
		Holt’s Linear Exponential Smoothing
		Nonlinear Trend Regression
	18.5 Seasonality and Trend
		Seasonality Without Trend
		Seasonality and Trend
		Models Based on Monthly Data
	18.6 Time Series Decomposition
		Calculating the Seasonal Indexes
		Deseasonalizing the Time Series
		Using the Deseasonalized Time Series to Identify Trend
		Seasonal Adjustments
		Models Based on Monthly Data
		Cyclical Component
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Forecasting Food and Beverage Sales
	Case Problem 2: Forecasting Lost Sales
	Appendix 18.1 Forecasting with Minitab
	Appendix 18.2 Forecasting with Excel
	Appendix 18.3 Forecasting with StatTools
Chapter 19 Nonparametric Methods
	Statistics in Practice: West Shell Realtors
	19.1 Sign Test
		Hypothesis Test About a Population Median
		Hypothesis Test with Matched Samples
	19.2 Wilcoxon Signed-Rank Test
	19.3 Mann-Whitney-Wilcoxon Test
	19.4 Kruskal-Wallis Test
	19.5 Rank Correlation
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Appendix 19.1 Nonparametric Methods with Minitab
	Appendix 19.2 Nonparametric Methods with Excel
	Appendix 19.3 Nonparametric Methods with StatTools
Chapter 20 Statistical Methods for Quality Control
	Statistics in Practice: Dow Chemical Company
	20.1 Philosophies and Frameworks
		Malcolm Baldrige National Quality Award
		ISO 9000
		Six Sigma
	20.2 Statistical Process Control
		Control Charts
		x Chart: Process Mean and Standard Deviation Known
		x Chart: Process Mean and Standard Deviation Unknown
		R Chart
		p Chart
		np Chart
		Interpretation of Control Charts
	20.3 Acceptance Sampling
		KALI, Inc.: An Example of Acceptance Sampling
		Computing the Probability of Accepting a Lot
		Selecting an Acceptance Sampling Plan
		Multiple Sampling Plans
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Appendix 20.1 Control Charts with Minitab
	Appendix 20.2 Control Charts with StatTools
Chapter 21 Decision Analysis
	Statistics in Practice: Ohio Edison Company
	21.1 Problem Formulation
		Payoff Tables
		Decision Trees
	21.2 Decision Making with Probabilities
		Expected Value Approach
		Expected Value of Perfect Information
	21.3 Decision Analysis with Sample Information
		Decision Tree
		Decision Strategy
		Expected Value of Sample Information
	21.4 Computing Branch Probabilities Using Bayes’ Theorem
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: Lawsuit Defense Strategy
	Appendix: An Introduction to PrecisionTree
Appendix A: References and Bibliography
Appendix B: Tables
Appendix C: Summation Notation
Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises
Appendix E: Using Excel Functions
Appendix F: Computing p-Values Using Minitab and Excel
Index
                        

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