Business Statistics: A Decision-Making Approach

Cover
Title Page
Copyright Page
Dedication
About the Authors
Brief Contents
Contents
Preface
1. The Where, Why, and How of Data Collection
	1.1. What Is Business Statistics?
		Descriptive Statistics
		Inferential Procedures
	1.2. Procedures for Collecting Data
		Primary Data Collection Methods
		Other Data Collection Methods
		Data Collection Issues
	1.3. Populations, Samples, and Sampling Techniques
		Populations and Samples
		Sampling Techniques
	1.4. Data Types and Data Measurement Levels
		Quantitative and Qualitative
		Time-Series Data and Cross-Sectional Data
		Data Measurement Levels
	1.5. A Brief Introduction to Data Mining
		Data Mining—Finding the Important, Hidden Relationships in Data
	1 Overview
		Summary
		Key Terms
		Chapter Exercises
2. Graphs, Charts, and Tables—Describing Your Data
	2.1. Frequency Distributions and Histograms
		Frequency Distributions
		Grouped Data Frequency Distributions
		Histograms
		Relative Frequency Histograms and Ogives
		Joint Frequency Distributions
	2.2. Bar Charts, Pie Charts, and Stem and Leaf Diagrams
		Bar Charts
		Pie Charts
		Stem and Leaf Diagrams
	2.3. Line Charts, Scatter Diagrams, and Pareto Charts
		Line Charts
		Scatter Diagrams
		Pareto Charts
	2. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 2.1: Server Downtime
		Case 2.2: Hudson Valley Apples, Inc.
		Case 2.3: Pine River Lumber Company—Part 1
3. Describing Data Using Numerical Measures
	3.1. Measures of Center and Location
		Parameters and Statistics
		Population Mean
		Sample Mean
		The Impact of Extreme Values on the Mean
		Median
		Skewed and Symmetric Distributions
		Mode
		Applying the Measures of Central Tendency
		Other Measures of Location
		Box and Whisker Plots
		Developing a Box and Whisker Plot in Excel 2016
		Data-Level Issues
	3.2. Measures of Variation
		Range
		Interquartile Range
		Population Variance and Standard Deviation
		Sample Variance and Standard Deviation
	3.3. Using the Mean and Standard Deviation Together
		Coefficient of Variation
		Tchebysheff’s Theorem
		Standardized Data Values
	3. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 3.1: SDW—Human Resources
		Case 3.2: National Call Center
		Case 3.3: Pine River Lumber Company—Part 2
		Case 3.4: AJ’s Fitness Center
1-3. Special Review Section
	Chapters 1–3
	Exercises
	Review Case 1. State Department of Insurance
	Term Project Assignments
4. Introduction to Probability
	4.1. The Basics of Probability
		Important Probability Terms
		Methods of Assigning Probability
	4.2. The Rules of Probability
		Measuring Probabilities
		Conditional Probability
		Multiplication Rule
		Bayes’ Theorem
	4. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 4.1: Great Air Commuter Service
		Case 4.2: Pittsburg Lighting
5. Discrete Probability Distributions
	5.1. Introduction to Discrete Probability Distributions
		Random Variables
		Mean and Standard Deviation of Discrete Distributions
	5.2. The Binomial Probability Distribution
		The Binomial Distribution
		Characteristics of the Binomial Distribution
	5.3. Other Probability Distributions
		The Poisson Distribution
		The Hypergeometric Distribution
	5. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 5.1: SaveMor Pharmacies
		Case 5.2: Arrowmark Vending
		Case 5.3: Boise Cascade Corporation
6. Introduction to Continuous Probability Distributions
	6.1. The Normal Distribution
		The Normal Distribution
		The Standard Normal Distribution
		Using the Standard Normal Table
	6.2. Other Continuous Probability Distributions
		The Uniform Distribution
		The Exponential Distribution
	6. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 6.1: State Entitlement Programs
		Case 6.2: Credit Data, Inc.
		Case 6.3: National Oil Company—Part 1
7. Introduction to Sampling Distributions
	7.1. Sampling Error: What It Is and Why It Happens
		Calculating Sampling Error
	7.2. Sampling Distribution of the Mean
		Simulating the Sampling Distribution for x̄
		The Central Limit Theorem
	7.3. Sampling Distribution of a Proportion
		Working with Proportions
		Sampling Distribution of p̄
	7. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 7.1: Carpita Bottling Company—Part 1
		Case 7.2: Truck Safety Inspection
8. Estimating Single Population Parameters
	8.1. Point and Confidence Interval Estimates for a Population Mean
		Point Estimates and Confidence Intervals
		Confidence Interval Estimate for the Population Mean, σ Known
		Confidence Interval Estimates for the Population Mean, σ Unknown
		Student’s t-Distribution
	8.2. Determining the Required Sample Size for Estimating a Population Mean
		Determining the Required Sample Size for Estimating μ, σ Known
		Determining the Required Sample Size for Estimating μ, σ Unknown
	8.3. Estimating a Population Proportion
		Confidence Interval Estimate for a Population Proportion
		Determining the Required Sample Size for Estimating a Population Proportion
	8. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 8.1: Management Solutions, Inc.
		Case 8.2: Federal Aviation Administration
		Case 8.3: Cell Phone Use
9. Introduction to Hypothesis Testing
	9.1. Hypothesis Tests for Means
		Formulating the Hypotheses
		Significance Level and Critical Value
		Hypothesis Test for μ, σ Known
		Types of Hypothesis Tests
		p-Value for Two-Tailed Tests
		Hypothesis Test for μ, σ Unknown
	9.2. Hypothesis Tests for a Proportion
		Testing a Hypothesis about a Single Population Proportion
	9.3. Type II Errors
		Calculating Beta
		Controlling Alpha and Beta
		Power of the Test
	9. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 9.1: Carpita Bottling Company—Part 2
		Case 9.2: Wings of Fire
10. Estimation and Hypothesis Testing for Two Population Parameters
	10.1. Estimation for Two Population Means Using Independent Samples
		Estimating the Difference between Two Population Means When σ1 and σ2 Are Known, Using Independent Samples
		Estimating the Difference between Two Population Means When σ1 and σ2 Are Unknown, Using Independent Samples
	10.2. Hypothesis Tests for Two Population Means Using Independent Samples
		Testing for μ1 - μ2 When σ1 and σ2 Are Known, Using Independent Samples
		Testing for μ1 - μ2 When σ1 and σ2 Are Unknown,Using Independent Samples
	10.3. Interval Estimation and Hypothesis Tests for Paired Samples
		Why Use Paired Samples?
		Hypothesis Testing for Paired Samples
	10.4. Estimation and Hypothesis Tests for Two Population Proportions
		Estimating the Difference between Two Population Proportions
		Hypothesis Tests for the Difference between Two Population Proportions
	10. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 10.1: Larabee Engineering—Part 1
		Case 10.2: Hamilton Marketing Services
		Case 10.3: Green Valley Assembly Company
		Case 10.4: U-Need-It Rental Agency
11. Hypothesis Tests and Estimation for Population Variances
	11.1. Hypothesis Tests and Estimation for a Single Population Variance
		Chi-Square Test for One Population Variance
		Interval Estimation for a Population Variance
	11.2. Hypothesis Tests for Two Population Variances
		F-Test for Two Population Variances
	11. Overview
		Summary
		Equations
		Key Term
		Chapter Exercises
		Case 11.1: Larabee Engineering—Part 2
12. Analysis of Variance
	12.1. One-Way Analysis of Variance
		Introduction to One-Way ANOVA
		Partitioning the Sum of Squares
		The ANOVA Assumptions
		Applying One-Way ANOVA
		The Tukey-Kramer Procedure for Multiple Comparisons
		Fixed Effects Versus Random Effects in Analysis of Variance
	12.2 Randomized Complete Block Analysis of Variance
		Randomized Complete Block ANOVA
		Fisher’s Least Significant Difference Test
	12.3. Two-Factor Analysis of Variance with Replication
		Two-Factor ANOVA with Replications
		A Caution about Interaction
	12. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 12.1: Agency for New Americans
		Case 12.2: McLaughlin Salmon Works
		Case 12.3: NW Pulp and Paper
		Case 12.4: Quinn Restoration
		Business Statistics Capstone Project: Theme: Analysis of Variance
8–12. Special Review Section
	Chapters 8–12
	Using the Flow Diagrams
	Exercises
13. Goodness-of-Fit Tests and Contingency Analysis
	13.1. Introduction to Goodness-of-Fit Tests
		Chi-Square Goodness-of-Fit Test
	13.2. Introduction to Contingency Analysis
		2 × 2 Contingency Tables
		r × c Contingency Tables
		Chi-Square Test Limitations
	13. Overview
		Summary
		Equations
		Key Term
		Chapter Exercises
		Case 13.1: National Oil Company—Part 2
		Case 13.2: Bentford Electronics—Part 1
14. Introduction to Linear Regression and Correlation Analysis
	14.1. Scatter Plots and Correlation
		The Correlation Coefficient
	14.2. Simple Linear Regression Analysis
		The Regression Model Assumptions
		Meaning of the Regression Coefficients
		Least Squares Regression Properties
		Significance Tests in Regression Analysis
	14.3. Uses for Regression Analysis
		Regression Analysis for Description
		Regression Analysis for Prediction
		Common Problems Using Regression Analysis
	14. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 14.1: A & A Industrial Products
		Case 14.2: Sapphire Coffee—Part 1
		Case 14.3: Alamar Industries
		Case 14.4: Continental Trucking
15. Multiple Regression Analysis and Model Building
	15.1. Introduction to Multiple Regression Analysis
		Basic Model-Building Concepts
	15.2. Using Qualitative Independent Variables
	15.3. Working with Nonlinear Relationships
		Analyzing Interaction Effects
		Partial F-Test
	15.4. Stepwise Regression
		Forward Selection
		Backward Elimination
		Standard Stepwise Regression
		Best Subsets Regression
	15.5. Determining the Aptness of the Model
		Analysis of Residuals
		Corrective Actions
	15. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 15.1: Dynamic Weighing, Inc.
		Case 15.2: Glaser Machine Works
		Case 15.3: Hawlins Manufacturing
		Case 15.4: Sapphire Coffee—Part 2
		Case 15.5: Wendell Motors
16. Analyzing and Forecasting Time-Series Data
	16.1. Introduction to Forecasting and Time-Series Data
		General Forecasting Issues
		Components of a Time Series
		Introduction to Index Numbers
		Using Index Numbers to Deflate a Time Series
	16.2. Trend-Based Forecasting Techniques
		Developing a Trend-Based Forecasting Model
		Comparing the Forecast Values to the Actual Data
		Nonlinear Trend Forecasting
		Adjusting for Seasonality
	16.3. Forecasting Using Smoothing Methods
		Exponential Smoothing
		Forecasting with Excel 2016
	16. Overview
		Summary
		Equations
		Key Terms
		Chapter Exercises
		Case 16.1: Park Falls Chamber of Commerce
		Case 16.2: The St. Louis Companies
		Case 16.3: Wagner Machine Works
17. Introduction to Nonparametric Statistics
	17.1. The Wilcoxon Signed Rank Test for One Population Median
		The Wilcoxon Signed Rank Test—Single Population
	17.2. Nonparametric Tests for Two Population Medians
		The Mann–Whitney U-Test
		Mann–Whitney U-Test—Large Samples
	17.3. Kruskal–Wallis One-Way Analysis of Variance
		Limitations and Other Considerations
	17. Overview
		Summary
		Equations
		Chapter Exercises
		Case 17.1: Bentford Electronics—Part 2
18. Introducing Business Analytics
	18.1. What Is Business Analytics?
		Descriptive Analytics
		Predictive Analytics
	18.2. Data Visualization Using Microsoft Power BI Desktop
		Using Microsoft Power BI Desktop
	18. Overview
		Summary
		Key Terms
		Case 18.1: New York City Taxi Trips
Appendix Tables
	Appendix A: Random Numbers Table
	Appendix B: Cumulative Binomial Distribution Table
	Appendix C: Cumulative Poisson Probability Distribution Table
	Appendix D: Standard Normal Distribution Table
	Appendix E: Exponential Distribution Table
	Appendix F: Values of t for Selected Probabilities
	Appendix G: Values of χ2 for Selected Probabilities
	Appendix H: F-Distribution Table: Upper 5% Probability (or 5% Area) under F-Distribution Curve
	Appendix I: Distribution of the Studentized Range (q-values)
	Appendix J: Critical Values of r in the Runs Test
	Appendix K: Mann-Whitney U Test Probabilities (n < 9)
	Appendix L: Mann-Whitney U Test Critical Values (9 ≤ n ≤ 20)
	Appendix M: Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n ≤ 25)
	Appendix N: Critical Values dL and dU of the Durbin-Watson Statistic D (Critical Values Are One-Sided)
	Appendix O: Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test
	Appendix P: Control Chart Factors
Answers to Selected Odd-Numbered Exercises
References
Glossary
Index
Credits
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