Statistics for Business & Economics

Cover
Brief Contents
Contents
About the Authors
Preface
Chapter 1: Data and Statistics
	1.1 Applications in Business and Economics
	1.2 Data
	1.3 Data Sources
	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
	Chapter 1 Appendix
Chapter 2: Descriptive Statistics: Tabular and Graphical Displays
	2.1 Summarizing Data for a Categorical Variable
	2.2 Summarizing Data for a Quantitative Variable
	2.3 Summarizing Data for Two Variables Using Tables
	2.4 Summarizing Data for Two Variables Using Graphical Displays
	2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays
	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.
	Chapter 2 Appendix
Chapter 3: Descriptive Statistics: Numerical Measures
	3.1 Measures of Location
	3.2 Measures of Variability
	3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
	3.4 Five-Number Summaries and Boxplots
	3.5 Measures of Association between Two Variables
	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
	Chapter 3 Appendix
Chapter 4: Introduction to Probability
	4.1 Random Experiments, Counting Rules, and Assigning Probabilities
	4.2 Events and Their Probabilities
	4.3 Some Basic Relationships of Probability
	4.4 Conditional Probability
	4.5 Bayes' Theorem
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Hamilton County Judges
	Case Problem 2: Rob's Market
Chapter 5: Discrete Probability Distributions
	5.1 Random Variables
	5.2 Developing Discrete Probability Distributions
	5.3 Expected Value and Variance
	5.4 Bivariate Distributions, Covariance, and Financial Portfolios
	5.5 Binomial Probability Distribution
	5.6 Poisson Probability Distribution
	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
	Chapter 5 Appendix
Chapter 6: Continuous Probability Distributions
	6.1 Uniform Probability Distribution
	6.2 Normal Probability Distribution
	6.3 Normal Approximation of Binomial Probabilities
	6.4 Exponential Probability Distribution
	Summary
	Glossary
	Key Formulas
	Suplementary Exercises
	Case Problem 1: Specialty Toys
	Case Problem 2 Gebhardt Electronics
	Chapter 6 Appendix
Chapter 7: Sampling and Sampling Distributions
	7.1 The Electronics Associates Sampling Problem
	7.2 Selecting a Sample
	7.3 Point Estimation
	7.4 Introduction to Sampling Distributions
	7.5 Sampling Distribution of x
	7.6 Sampling Distribution of p
	7.7 Properties of Point Estimators
	7.8 Other Sampling Methods
	7.9 Big Data and Standard Errors of Sampling Distributions
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: Marion Dairies
	Chapter 7 Appendix
Chapter 8: Interval Estimation
	8.1 Population Mean: o Known
	8.2 Population Mean: o Unknown
	8.3 Determining the Sample Size
	8.4 Population Proportion
	8.5 Big Data and 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.
	Chapter 8 Appendix
Chapter 9: Hypothesis Tests
	9.1 Developing Null and Alternative Hypotheses
	9.2 Type I and Type II Errors
	9.3 Population Mean: o Known
	9.4 Population Mean: o Unknown
	9.5 Population Proportion
	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
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Quality Associates, Inc.
	Case Problem 2: Ethical Behavior of Business Students at Bayview University
	Chapter 9 Appendix
Chapter 10: Inference about Means and Proportions with Two Populations
	10.1 Inferences about the Difference between Two Population Means: o1 and o2 Known
	10.2 Inferences about the Difference between Two Population Means: o1 and o2 Unknown
	10.3 Inferences about the Difference between Two Population Means: Matched Samples
	10.4 Inferences about the Difference between Two Population Proportions
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem: Par, Inc.
	Chapter 10 Appendix
Chapter 11: Inferences about Population Variances
	11.1 Inferences about a Population Variance
	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
	Chapter 11 Appendix
Chapter 12: Comparing Multiple Proportions, Test of Independence and Goodness of Fit
	12.1 Testing the Equality of Population Proportions for Three or More Populations
	12.2 Test of Independence
	12.3 Goodness of Fit Test
	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
	Chapter 12 Appendix
Chapter 13: Experimental Design and Analysis of Variance
	13.1 An Introduction to Experimental Design and Analysis of Variance
	13.2 Analysis of Variance and the Completely Randomized Design
	13.3 Multiple Comparison Procedures
	13.4 Randomized Block Design
	13.5 Factorial Experiment
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Wentworth Medical Center
	Case Problem 2: Compensation for Sales Professionals
	Case Problem 3: Touristopia Travel
	Chapter 13 Appendix
Chapter 14: Simple Linear Regression
	14.1 Simple Linear Regression Model
	14.2 Least Squares Method
	14.3 Coefficient of Determination
	14.4 Model Assumptions
	14.5 Testing for Significance
	14.6 Using the Estimated Regression Equation for Estimation and Prediction
	14.7 Computer Solution
	14.8 Residual Analysis: Validating Model Assumptions
	14.9 Residual Analysis: Outliers and 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
	Chapter 14 Appendix
Chapter 15: Multiple Regression
	15.1 Multiple Regression Model
	15.2 Least Squares Method
	15.3 Multiple Coefficient of Determination
	15.4 Model Assumptions
	15.5 Testing for Significance
	15.6 Using the Estimated Regression Equation for Estimation and Prediction
	15.7 Categorical Independent Variables
	15.8 Residual Analysis
	15.9 Logistic Regression
	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
	Chapter 15 Appendix
Chapter 16: Regression Analysis: Model Building
	16.1 General Linear Model
	16.2 Determining When to Add or Delete Variables
	16.3 Analysis of a Larger Problem
	16.4 Variable Selection Procedures
	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
	Chapter 16 Appendix
Chapter 17: Time Series Analysis and Forecasting
	17.1 Time Series Patterns
	17.2 Forecast Accuracy
	17.3 Moving Averages and Exponential Smoothing
	17.4 Trend Projection
	17.5 Seasonality and Trend
	17.6 Time Series Decomposition
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Case Problem 1: Forecasting Food and Beverage Sales
	Case Problem 2: Forecasting Lost Sales
	Chapter 17 Appendix
Chapter 18: Nonparametric Methods
	18.1 Sign Test
	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
	Supplemetary Exercises
	Case: Rainorshine.com
	Chapter 18 Appendix
Chapter 19: Decision Analysis
	19.1 Problem Formulation
	19.2 Decision Making with Probabilities
	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
	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
	20.5 Deflating a Series by Price Indexes
	20.6 Price Indexes: Other Considerations
	20.7 Quantity Indexes
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
Chapter 21: Statistical Methods for Quality Control
	21.1 Philosophies and Frameworks
	21.2 Statistical Process Control
	21.3 Acceptance Sampling
	Summary
	Glossary
	Key Formulas
	Supplementary Exercises
	Chapter 21 Appendix
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