Statistics for Business and Economics: Metric Version

Cover
Title Page
Copyright Page
Brief Contents
Contents
About the Authors
Preface
CHAPTER 1 Data and Statistics
Statistics in Practice: Bloomberg Businessweek
1.1 Applications in Business and Economics
Accounting
Finance
Marketing
Production
Economics
Information Systems
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
Observational Study
Experiment
Time and Cost Issues
Data Acquisition Errors
1.4 Descriptive Statistics
1.5 Statistical Inference
1.6 Analytics
1.7 Big Data and Data Mining
1.8 Computers and Statistical Analysis
1.9 Ethical Guidelines for Statistical Practice
Summary
Glossary
Supplementary Exercises
Appendix 1.1 Opening and Saving DATA Files and Converting to Stacked form with JMP
CHAPTER 2 Descriptive Statistics: Tabular and Graphical Displays
Statistics in Practice: Colgate-Palmolive Company
2.1 Summarizing Data for a Categorical Variable
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Bar Charts and Pie Charts
2.2 Summarizing Data for a Quantitative Variable
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Dot Plot
Histogram
Cumulative Distributions
Stem-and-Leaf Display
2.3 Summarizing Data for Two Variables Using Tables
Crosstabulation
Simpson’s Paradox
2.4 Summarizing Data for Two Variables Using Graphical Displays
Scatter Diagram and Trendline
Side-by-Side and Stacked Bar Charts
2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays
Creating Effective Graphical Displays
Choosing the Type of Graphical Display
Data Dashboards
Data visualization in Practice: Cincinnati Zoo and Botanical Garden
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Movie Theater Releases
Case Problem 3: Queen City
Case Problem 4: Cut-Rate Machining, Inc.
Appendix 2.1 Creating Tabular and Graphical Presentations with JMP
Appendix 2.2 Creating Tabular and Graphical Presentations with Excel
CHAPTER 3 Descriptive Statistics: Numerical Measures
Statistics in Practice: Small Fry Design
3.1 Measures of Location
Mean
Weighted Mean
Median
Geometric Mean
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 Five-Number Summaries and Boxplots
Five-Number Summary
Boxplot
Comparative Analysis Using Boxplots
3.5 Measures of Association Between Two Variables
Covariance
Interpretation of the Covariance
Correlation Coefficient
Interpretation of the Correlation Coefficient
3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Movie Theater Releases
Case Problem 3: Business Schools of Asia-Pacific
Case Problem 4: Heavenly Chocolates Website Transactions
Case Problem 5: African Elephant Populations
Appendix 3.1 Descriptive Statistics with JMP
Appendix 3.2 Descriptive Statistics with Excel
CHAPTER 4 Introduction to Probability
Statistics in Practice: National Aeronautics and Space Administration
4.1 Random 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 1: Hamilton County Judges
Case Problem 2: Rob’s Market
CHAPTER 5 Discrete Probability Distributions
Statistics in Practice: Voter Waiting Times in Elections
5.1 Random Variables
Discrete Random Variables
Continuous Random Variables
5.2 Developing Discrete Probability Distributions
5.3 Expected Value and Variance
Expected Value
Variance
5.4 Bivariate Distributions, Covariance, and Financial Portfolios
A Bivariate Empirical Discrete Probability Distribution
Financial Applications
Summary
5.5 Binomial Probability Distribution
A Binomial Experiment
Martin Clothing Store Problem
Using Tables of Binomial Probabilities
Expected Value and Variance for the Binomial Distribution
5.6 Poisson Probability Distribution
An Example Involving Time Intervals
An Example Involving Length or Distance Intervals
5.7 Hypergeometric Probability Distribution
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Go Bananas! Breakfast Cereal
Case Problem 2: McNeil’s Auto Mall
Case Problem 3: Grievance Committee at Tuglar Corporation
Appendix 5.1 Discrete Probability Distributions with JMP
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 1: Specialty Toys
Case Problem 2: Gebhardt Electronics
Appendix 6.1 Continuous Probability Distributions with JMP
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
7.9 Big Data and Standard Errors of Sampling Distributions
Sampling Error
Nonsampling Error
Big Data
Understanding What Big Data Is
Implications of Big Data for Sampling Error
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Marion Dairies
Appendix 7.1 The Expected Value and Standard Deviation of x
Appendix 7.2 Random Sampling with JMP
Appendix 7.3 Random Sampling with Excel
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
8.5 Big Data and Confidence intervals
Big Data and the Precision of Confidence Intervals
Implications of Big Data for Confidence Intervals
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 JMP
Appendix 8.2 Interval Estimation Using Excel
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
9.9 Big Data and Hypothesis Testing
Big Data, Hypothesis Testing, and p Values
Implications of Big Data in Hypothesis Testing
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 JMP
Appendix 9.2 Hypothesis Testing with Excel
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: σ1 and σ2 Known
Interval Estimation of μ1 - μ2
Hypothesis Tests About μ1 - μ2
Practical Advice
10.2 Inferences About the Difference Between Two Population Means: σ1 and σ2 Unknown
Interval Estimation of μ1 - μ2
Hypothesis Tests About μ1 - μ2
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 p1 - p2
Hypothesis Tests About p1 - p2
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Par, Inc.
Appendix 10.1 Inferences About Two Populations with JMP
Appendix 10.2 Inferences About Two Populations with Excel
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 1: Air Force Training Program
Case Problem 2: Meticulous Drill & Reamer
Appendix 11.1 Population Variances with JMP
Appendix 11.2 Population Variances with Excel
CHAPTER 12 Comparing Multiple Proportions, Test of Independence and Goodness of Fit
Comparing Multiple
Proportions, Test of
Independence and
Goodness of Fit
12.1 Testing the Equality of Population Proportions for Three or More Populations
A multiple Comparison Procedure
12.2 Test of independence
12.3 Goodness of Fit Test
Multinomial Probability Distribution
Normal Probability Distribution
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: A Bipartisan Agenda for Change
Case Problem 2: Fuentes Salty Snacks, Inc.
Case Problem 3: Fresno Board Games
Appendix 12.1 Chi-Square Tests with JMP
Appendix 12.2 Chi-Square Tests with Excel
CHAPTER 13 Experimental Designand 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
Case Problem 3: Touristopia Travel
Appendix 13.1 Analysis of Variance with JMP
Appendix 13.2 Analysis of Variance with Excel
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 σ2
t Test
Confidence Interval for β1
F Test
Some Cautions About the Interpretation of Significance Tests
14.6 Using the Estimated Regression Equation for Estimation and Prediction
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 ŷ
Standardized Residuals
Normal Probability Plot
14.9 Residual Analysis: Outliers and influential Observations
Detecting Outliers
Detecting Influential Observations
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Measuring Stock Market Risk
Case Problem 2: U.S. Department of Transportation
Case Problem 3: Selecting a Point-and-Shoot Digital Camera
Case Problem 4: Finding the Best Car Value
Case Problem 5: Buckeye Creek Amusement Park
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
Appendix 14.2 A Test for Significance Using Correlation
Appendix 14.3 Simple Linear Regression with JMP
Appendix 14.4 Regression Analysis with Excel
CHAPTER 15 Multiple Regression
Statistics in Practice: 84.51°
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
15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Consumer Research, Inc.
Case Problem 2: Predicting Winnings for NASCAR Drivers
Case Problem 3: Finding the Best Car Value
Appendix 15.1 Multiple Linear Regression with JMP
Appendix 15.2 Logistic Regression with JMP
Appendix 15.3 Multiple Regression with Excel
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 LPGA Tour Statistics
Case Problem 2: Rating Wines from the Piedmont Region of Italy
Appendix 16.1 Variable Selection Procedures with JMP
CHAPTER 17 Time Series Analysis and Forecasting
Statistics in Practice: Nevada Occupational Health Clinic
17.1 Time Series Patterns
Horizontal Pattern
Trend Pattern
Seasonal Pattern
Trend and Seasonal Pattern
Cyclical Pattern
Selecting a Forecasting Method
17.2 Forecast Accuracy
17.3 Moving Averages and Exponential Smoothing
Moving Averages
Weighted Moving Averages
Exponential Smoothing
17.4 Trend Projection
Linear Trend Regression
Nonlinear Trend Regression
17.5 Seasonality and Trend
Seasonality Without Trend
Seasonality and Trend
Models Based on Monthly Data
17.6 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 17.1 Forecasting with JMP
Appendix 17.2 Forecasting with Excel
CHAPTER 18 Nonparametric Methods
Statistics in Practice: West Shell Realtors
18.1 Sign Test
Hypothesis Test About a Population Median
Hypothesis Test with Matched Samples
18.2 Wilcoxon Signed-Rank Test
18.3 Mann-Whitney-Wilcoxon Test
18.4 Kruskal-Wallis Test
18.5 Rank Correlation
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: RainOrShine.Com
Appendix 18.1 Nonparametric Methods with JMP
Appendix 18.2 Nonparametric Methods with Excel
CHAPTER 19 Decision Analysis
Statistics in Practice: Ohio Edison Company
19.1 Problem Formulation
Payoff Tables
Decision Trees
19.2 Decision Making with Probabilities
Expected Value Approach
Expected Value of Perfect Information
19.3 Decision Analysis with Sample Information
Decision Tree
Decision Strategy
Expected Value of Sample Information
19.4 Computing Branch Probabilities Using Bayes’ Theorem
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Lawsuit Defense Strategy
Case Problem 2: Property Purchase Strategy
CHAPTER 20 Index Numbers
Statistics in Practice: U.S. Department of Labor, Bureau
of Labor Statistics
20.1 Price Relatives
20.2 Aggregate Price Indexes
20.3 Computing an Aggregate Price Index from Price Relatives
20.4 Some Important Price Indexes
Consumer Price Index
Producer Price Index
Dow Jones Averages
20.5 Deflating a Series by Price Indexes
20.6 Price Indexes: Other Considerations
Selection of Items
Selection of a Base Period
Quality Changes
20.7 Quantity Indexes
Summary
Glossary
Key Formulas
Supplementary Exercises
CHAPTER 21 Statistical Methods for Quality Control
Statistics in Practice: Dow Chemical Company
21.1 Philosophies and Frameworks
Malcolm Baldrige National Quality Award
ISO 9000
Six Sigma
Quality in the Service Sector
21.2 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
21.3 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 21.1 Control Charts with JMP
APPENDIXES
Appendix A–References and Bibliography
Appendix B–Tables
Appendix C–Summation Notation
Appendix E–Microsoft Excel 2016 and Tools for Statistical Analysis
Appendix F–Computing p-Values with JMP and Excel
INDEX