Basic Business Statistics: Concepts And Applications, Global Edition, 14th Edition
- Length: 840 pages
- Edition: 14
- Language: English
- Publisher: Pearson
- Publication Date: 2019-07-22
- ISBN-10: 1292265035
- ISBN-13: 9781292265032
- Sales Rank: #3743307 (See Top 100 Books)
For one- or-two-semester courses in business statistics.
Give students the statistical foundation to hone their analysis skills for real-world decisions
Basic Business Statistics helps students see the essential role that statistics will play in their future careers by using examples drawn from all functional areas of real-world business. Guided by principles set forth by ASA’s Guidelines for Assessment and Instruction (GAISE) reports and the authors’ diverse teaching experiences, the text continues to innovate and improve the way this course is taught to students. The 14th Edition includes new and updated resources and tools to enhance students’ understanding, and provides the best framework for learning statistical concepts.
MyLab™ Business Statistics is not included. Students, if MyLab Business Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. MyLab Business Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.
Reach every student by pairing this text with MyLab Business Statistics
MyLab™ is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student.
Cover Half Title Page Title Page Copyright Page About the Authors Brief Contents Contents Preface First Things First USING STATISTICS: “The Price of Admission” FTF.1 Think Differently About Statistics Statistics: A Way of Thinking Statistics: An Important Part of Your Business Education FTF.2 Business Analytics: The Changing Face of Statistics “Big Data” FTF.3 Starting Point for Learning Statistics Statistic Can Statistics (pl., statistic) Lie? FTF.4 Starting Point for Using Software Using Software Properly REFERENCES Key Terms EXCEL GUIDE EG.1 Getting Started with Excel EG.2 Entering Data EG.3 Open or Save a Workbook EG.4 Working with a Workbook EG.5 Print a Worksheet EG.6 Reviewing Worksheets EG.7 If You Use the Workbook Instructions JMP Guide JG.1 Getting Started with JMP JG.2 Entering Data JG.3 Create New Project or Data Table JG.4 Open or Save Files JG.5 Print Data Tables or Report Windows JG.6 JMP Script Files MINITAB GUIDE MG.1 Getting Started with Minitab MG.2 Entering Data MG.3 Open or Save Files MG.4 Insert or Copy Worksheets MG.5 Print Worksheets 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 Cleaning Invalid Variable Values Coding Errors Data Integration Errors Missing Values Algorithmic Cleaning of Extreme Numerical Values 1.5 Other Data Preprocessing Tasks Data Formatting Stacking and Unstacking Data Recoding Variables 1.6 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 EG1.4 Data Cleaning EG1.5 Other Data Preprocessing Chapter 1 JMP Guide JG1.1 Defining Variables JG1.2 Collecting Data JG1.3 Types of Sampling Methods JG1.4 Data Cleaning JG1.5 Other Preprocessing Tasks Chapter 1 MINITAB Guide MG1.1 Defining Variables MG1.2 Collecting Data MG1.3 Types of Sampling Methods MG1.4 Data Cleaning MG1.5 Other Preprocessing Tasks 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 a Mix of Variables Drill-down 2.7 Visualizing a Mix of Variables Colored Scatter Plot Bubble Charts PivotChart (Excel) Treemap (Excel, JMP) Sparklines (Excel) 2.8 Filtering and Querying Data Excel Slicers 2.9 Pitfalls in Organizing and Visualizing Variables Obscuring Data Creating False Impressions Chartjunk Exhibit: Best Practices for Creating Visual Summaries 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 Charts Group Reference EG2.3 Visualizing Categorical Variables EG2.4 Visualizing Numerical Variables EG2.5 Visualizing Two Numerical Variables EG2.6 Organizing a Mix of Variables EG2.7 Visualizing a Mix of Variables EG2.8 Filtering and Querying Data Chapter 2 JMP Guide JG2 JMP Choices for Creating Summaries JG2.1 Organizing Categorical Variables JG2.2 Organizing Numerical Variables JG2.3 Visualizing Categorical Variables JG2.4 Visualizing Numerical Variables JG2.5 Visualizing Two Numerical Variables JG2.6 Organizing a Mix of Variables JG2.7 Visualizing a Mix of Variables JG2.8 Filtering and Querying Data JMP Guide Gallery Chapter 2 MINITAB GUIDE MG2.1 Organizing Categorical Variables MG2.2 Organizing Numerical Variables MG2.3 Visualizing Categorical Variables MG2.4 Visualizing Numerical Variables MG2.5 Visualizing Two Numerical Variables MG2.6 Organizing a Mix of Variables MG2.7 Visualizing a Mix of Variables MG2.8 Filtering and Querying Data 3 Numerical Descriptive Measures USING STATISTICS: More Descriptive Choices 3.1 Measures of Central Tendency The Mean The Median The Mode The Geometric Mean 3.2 Measures of Variation and Shape The Range The Variance and the Standard Deviation The Coefficient of Variation Z Scores Shape: Skewness Shape: Kurtosis 3.3 Exploring Numerical Variables 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 Descriptive 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 Managing Ashland MultiComm Services Digital Case CardioGood Fitness More Descriptive Choices Follow-up Clear Mountain State Student Survey Chapter 3 EXCEL GUIDE EG3.1 Measures of Central Tendency EG3.2 Measures of Variation and Shape EG3.3 Exploring Numerical Variables EG3.4 Numerical Descriptive Measures for a Population EG3.5 The Covariance and the Coefficient of Correlation Chapter 3 JMP GUIDE JG3.1 Measures of Central Tendency JG3.2 Measures of Variation and Shape JG3.3 Exploring Numerical Variables JG3.4 Numerical Descriptive Measures for a Population JG3.5 The Covariance and the Coefficient of Correlation Chapter 3 MINITAB Guide MG3.1 Measures of Central Tendency MG3.2 Measures of Variation and Shape MG3.3 Exploring Numerical Variables MG3.4 Numerical Descriptive Measures for a Population MG3.5 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 Types of Probability Summarizing Sample Spaces 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 EG4.5 Counting Rules Chapter 4 JMP GUIDE JG4.4 Bayes’ Theorem Chapter 4 MINITAB Guide MG4.5 Counting Rules 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 Exhibit: Properties of the Binomial Distribution Histograms for Discrete Variables Summary Measures for the Binomial Distribution 5.3 Poisson Distribution 5.4 Covariance of a Probability Distribution and Its Application in Finance 5.5 Hypergeometric Distribution (online) 5.6 Using the Poisson Distribution to Approximate the Binomial Distribution (online) 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 Chapter 5 JMP Guide JG5.1 The Probability Distribution for a Discrete Variable JG5.2 Binomial Distribution JG5.3 Poisson Distribution Chapter 5 MINITAB Guide MG5.1 The Probability Distribution for a Discrete Variable MG5.2 Binomial Distribution MG5.3 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 Role of the Mean and the Standard Deviation Calculating 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 (online) 6.6 The Normal Approximation to the Binomial Distribution (online) 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 EG6.2 The Normal Distribution EG6.3 Evaluating Normality Chapter 6 JMP Guide JG6.2 The Normal Distribution JG6.3 Evaluating Normality Chapter 6 MINITAB Guide MG6.2 The Normal Distribution MG6.3 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 7.4 Sampling from Finite Populations (online) 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 Chapter 7 JMP Guide JG7.2 Sampling Distribution of the Mean Chapter 7 MINITAB GUIDE MG7.2 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 ( Known) Sampling Error Can You Ever Know the Population Standard Deviation? 8.2 Confidence Interval Estimate for the Mean ( Unknown) Student’s t Distribution The Concept of Degrees of Freedom Properties of the t Distribution 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 (online) 8.7 Estimation and Sample Size Estimation for Finite Populations (online) 8.8 Bootstrapping (online) 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 ( Known) EG8.2 Confidence Interval Estimate for the Mean ( Unknown) EG8.3 Confidence Interval Estimate for the Proportion EG8.4 Determining Sample Size Chapter 8 JMP Guide JG8.1 Confidence Interval Estimate for the Mean ( Known) JG8.2 Confidence Interval Estimate for the Mean ( Unknown) JG8.3 Confidence Interval Estimate for the Proportion JG8.4 Determining Sample Size Chapter 8 MINITAB Guide MG8.1 Confidence Interval Estimate for the Mean ( Known) MG8.2 Confidence Interval Estimate for the Mean ( Unknown) MG8.3 Confidence Interval Estimate for the Proportion MG8.4 Determining Sample Size 9 Fundamentals of Hypothesis Testing: One-Sample Tests USING STATISTICS: Significant Testing at Oxford Cereals 9.1 Fundamentals of Hypothesis Testing Exhibit: Fundamental Hypothesis Testing Concepts The Critical Value of the Test Statistic Regions of Rejection and Nonrejection Risks in Decision Making Using Hypothesis Testing Z Test for the Mean ( 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 ( 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 (online) 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 Chpater 9 Excel Guide EG9.1 Fundamentals of Hypothesis Testing EG9.2 t Test of Hypothesis for the Mean ( Unknown) EG9.3 One-Tail Tests EG9.4 Z Test of Hypothesis for the Proportion CHAPTER 9 JMP Guide JG9.1 Fundamentals of Hypothesis Testing JG9.2 t Test of Hypothesis for the Mean ( Unknown) JG9.3 One-Tail Tests JG9.4 Z Test of Hypothesis for the Proportion Chapter 9 MINITAB Guide MG9.1 Fundamentals of Hypothesis Testing MG9.2 t Test of Hypothesis for the Mean ( Unknown) MG9.3 One-Tail Tests MG9.4 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 Assuming Equal Variances Evaluating the Normality Assumption Confidence Interval Estimate for the Difference Between Two Means Separate-Variance 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 (online) 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 CHAPTER 10 JMP Guide JG10.1 Comparing the Means of Two Independent Populations JG10.2 Comparing the Means of Two Related Populations JG10.3 Comparing the Proportions of Two Independent Populations JG10.4 F Test for the Ratio of Two Variances CHAPTER 10 MINITAB Guide MG10.1 Comparing the Means of Two Independent Populations MG10.2 Comparing the Means of Two Related Populations MG10.3 Comparing the Proportions of Two Independent Populations MG10.4 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 (online) 11.4 Fixed Effects, Random Effects, and Mixed Effects Models (online) 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 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 CHAPTER 11 JMP Guide JG11.1 The Completely Randomized Design: One-Way Anova JG11.2 The Factorial Design: Two-Way Anova CHAPTER 11 MINITAB Guide MG11.1 The Completely Randomized Design: One-Way Anova MG11.2 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 for Two Independent Populations 12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA Assumptions of the Kruskal-Wallis Rank Test 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples) (online) 12.7 Chi-Square Test for the Variance or Standard Deviation (online) 12.8 Wilcoxon Signed Ranks Test for Two Related Populations (online) 12.9 Friedman Rank Test for the Randomized Block Design (online) 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 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 CHAPTER 12 JMP Guide JG12.1 Chi‐Square Test for the Difference Between Two Proportions JG12.2 Chi‐Square Test tor Difference Among More Than Two Proportions JG12.3 Chi‐Square Test Of Independence JG12.4 Wilcoxon Rank Sum Test for Two Independent Populations JG12.5 Kruskal‐Wallis Rank Test for the One‐Way Anova CHAPTER 12 MINITAB Guide MG12.1 Chi‐Square Test for the Difference Between Two Proportions MG12.2 Chi‐Square Test for Differences Among More Than Two Proportions MG12.3 Chi‐Square Test of Independence MG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for Two Independent Populations MG12.5 Kruskal‐Wallis Rank Test: A Nonparametric Method for the One‐Way Anova 13 Simple Linear Regression USING STATISTICS: Knowing Customers at Sunflowers Apparel Preliminary Analysis 13.1 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, and the Slope, 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: Seven 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 Rye 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 CHAPTER 13 JMP Guide JG13.2 Determining the Simple Linear Regression Equation JG13.3 Measures of Variation JG13.4 Assumptions of Regression JG13.5 Residual Analysis JG13.6 Measuring Autocorrelation: the Durbin‐Watson Statistic JG13.7 Inferences about the Slope and Correlation Coefficient JG13.8 Estimation of Mean Values and Prediction of Individual Values CHAPTER 13 MINITAB Guide MG13.2 Determining the Simple Linear Regression Equation MG13.3 Measures of Variation MG13.4 Assumptions of Regression MG13.5 Residual Analysis MG13.6 Measuring Autocorrelation: the Durbin‐Watson Statistic MG13.7 Inferences about the Slope and Correlation Coefficient MG13.8 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 Adjusted and the Overall F Test Coefficient of Multiple Determination Adjusted Test for the Significance of the Overall Multiple Regression Model 14.3 Multiple Regression Residual Analysis 14.4 Inferences About 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 Interactions 14.7 Logistic Regression 14.8 Influence Analysis (online) 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 Adjusted and the Overall F Test EG14.3 Multiple Regression Residual Analysis EG14.4 Inferences about the Population Regression Coefficients EG14.5 Testing Portions of the Multiple Regression Model EG14.6 Using Dummy Variables and Interaction Terms EG14.7 Logistic Regression Chapter 14 JMP Guide JG14.1 Developing a Multiple Regression Model JG14.2 Adjusted and the Overall F Test Measures of Variation JG14.3 Multiple Regression Residual Analysis JG14.4 Inferences about the Population JG14.5 Testing Portions of the Multiple Regression Model JG14.6 Using Dummy Variables and Interaction Terms JG14.7 Logistic Regression Chapter 14 MINITAB Guide MG14.1 Developing a Multiple Regression Model MG14.2 Adjusted and the Overall F Test MG14.3 Multiple Regression Residual Analysis MG14.4 Inferences about the Population Regression Coefficients MG14.5 Testing Portions of the Multiple Regression Model MG14.6 Using Dummy Variables and Interaction Terms in Regression Models MG14.7 Logistic Regression MG14.8 Influence Analysis 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 Exhibit: Sucessful Model Building The Stepwise Regression Approach to Model Building The Best Subsets Approach to Model Building Model Validation 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 Chapter 15 JMP Guide JG15.1 The Quadratic Regression Model JG15.2 Using Transformations in Regression Models JG15.3 Collinearity JG15.4 Model Building Chapter 15 MINITAB Guide MG15.1 The Quadratic Regression Model MG15.2 Using Transformations in Regression Models MG15.3 Collinearity MG15.4 Model Building 16 Time-Series Forecasting USING STATISTICS: Is the ByYourDoor Service Trending? 16.1 Time Series Component Factors 16.2 Smoothing an Annual Time Series Moving Averages Exponential Smoothing 16.3 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 Exhibit: Model Selection Using First, Second, and Percentage Differences 16.4 Autoregressive Modeling for Trend Fitting and Forecasting Selecting an Appropriate Autoregressive Model Determining the Appropriateness of a Selected Model Exhibit: Autoregressive Modeling Steps 16.5 Choosing an Appropriate Forecasting Model Residual Analysis The Magnitude of the Residuals Through Squared or Absolute Differences The Principle of Parsimony A Comparison of Four Forecasting Methods 16.6 Time-Series Forecasting of Seasonal Data Least‐Squares Forecasting with Monthly or Quarterly Data 16.7 Index Numbers (online) CONSIDER THIS: Let the Model User Beware USING STATISTICS: Is the ByYourDoor , 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 EG16.2 Smoothing an Annual Time Series EG16.3 Least‐Squares Trend Fitting and Forecasting EG16.4 Autoregressive Modeling for Trend Fitting and Forecasting EG16.5 Choosing an Appropriate Forecasting Model EG16.6 Time‐Series Forecasting of Seasonal Data Chapter 16 JMP Guide JG16.2 Smoothing an Annual Time Series JG16.3 Least‐Squares Trend Fitting and Forecasting JG16.4 Autoregressive Modeling for Trend Fitting and Forecasting JG16.5 Choosing an Appropriate Forecasting Model JG16.6 Time‐Series Forecasting of Seasonal Data Chapter 16 MINITAB Guide MG16.2 Smoothing an Annual Time Series MG16.3 Least‐Squares Trend Fitting and Forecasting MG16.4 Autoregressive Modeling for Trend Fitting and Forecasting MG16.5 Choosing an Appropriate Forecasting Model MG16.6 Time‐Series Forecasting of Seasonal Data 17 Business Analytics USING STATISTICS: Back to Arlingtons for the Future 17.1 Business Analytics Categories Inferential Statistics and Predictive Analytics Supervised and Unsupervised Methods CONSIDER THIS: What’s My Major if I Want to be a Data Miner? 17.2 Descriptive Analytics Dashboards Data Dimensionality and Descriptive Analytics 17.3 Predictive Analytics for Prediction 17.4 Predictive Analytics for Classification 17.5 Predictive Analytics for Clustering 17.6 Predictive Analytics for Association Multidimensional scaling (MDS) 17.7 Text Analytics 17.8 Prescriptive Analytics USING STATISTICS: Back to Arlingtons , Revisited REFERENCES KEY EQUATIONS Key Terms CHECKING YOUR UNDERSTANDING CHAPTER REVIEW PROBLEMS CASES for Chapter 17 The Mountain States Potato Company The Craybill Instrumentation Company Chapter 17 Software Guide Introduction SG17.2 Descriptive Analytics SG17.3 Predictive Analytics for Prediction SG17.4 Predictive Analytics for Classification SG17.5 Predictive Analytics for Clustering SG17.6 Predictive Analytics for Association 18 Getting Ready to Analyze Data in the Future USING STATISTICS: Mounting Future Analyses 18.1 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? Classify or Associate Items Determine Whether the Values of a Variable Are Stable Over Time? 18.2 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? Classify or Associate Items Determine Whether the Proportion of Items of Interest Is Stable Over Time? USING STATISTICS: The Future to Be Visited CHAPTER REVIEW PROBLEMS 19 Statistical Applications in Quality Management (online) USING STATISTICS: Finding Quality at the Beachcomber 19.1 The Theory of Control Charts 19.2 Control Chart for the Proportion: The p Chart 19.3 The Red Bead Experiment: Understanding Process Variability 19.4 Control Chart for an Area of Opportunity: The c Chart 19.5 Control Charts for the Range and the Mean The R Chart The X Chart 19.6 Process Capability Customer Satisfaction and Specification Limits Capability Indices CPL, CPU, and Cpk 19.7 Total Quality Management 19.8 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 CASES for Chapter 19 The Harnswell Sewing Machine Company Case Managing Ashland Multicomm Services Chapter 19 Excel Guide EG19.2 Control Chart for the Proportion: The p Chart EG19.4 Control Chart for an Area of Opportunity: The c Chart EG19.5 Control Charts for the Range and the Mean EG19.6 Process Capability Chapter 19 JMP Guide JG19.2 Control Chart for the Proportion: The p Chart JG19.4 Control Chart for an Area of Opportunity: The c Chart JG19.5 Control Charts for the Range and the Mean JG19.6 Process Capability Chapter 19 MINITAB Guide MG19.2 Control Chart for the Proportion: The p Chart MG19.4 Control Chart for an Area of Opportunity: The c Chart MG19.5 Control Charts for the Range and the Mean MG19.6 Process Capability 20 Decision Making (online) USING STATISTICS: Reliable Decision Making 20.1 Payoff Tables and Decision Trees 20.2 Criteria for Decision Making Maximax Payoff Maximin Payoff Expected Monetary Value Expected Opportunity Loss Return‐to‐Risk Ratio 20.3 Decision Making with Sample Information 20.4 Utility CONSIDER THIS: Risky Business USING STATISTICS: Reliable Decision-Making, Revisited Summary REFERENCES KEY EQUATIONS Key Terms CHAPTER REVIEW PROBLEMS CASES for Chapter 20 Digital Case Chapter 20 Excel Guide EG20.1 Payoff Tables and Decision Trees EG20.2 Criteria for Decision Making Appendices A. Basic Math Concepts and Symbols A.1 Operators A.2 Rules for Arithmetic Operations A.3 Rules for Algebra: Exponents and Square Roots A.4 Rules for Logarithms A.5 Summation Notation A.6 Greek Alphabet B. Important Software Skills and Concepts B.1 Identifying the Software Version B.2 Formulas B.3 Excel Cell References B.4 Excel Worksheet Formatting B.5E Excel Chart Formatting B.5J JMP Chart Formatting B.5M Minitab Chart Formatting B.6 Creating Histograms for Discrete Probability Distributions (Excel) B.7 Deleting the “Extra” Histogram Bar (Excel) C. Online Resources C.1 About the Online Resources for This Book C.2 Data Files C.3 Files Integrated With Microsoft Excel C.4 Supplemental Files D. Configuring Software D.1 Microsoft Excel Configuration D.2 JMP Configuration D.3 Minitab Configuration E. Table E.1 Table of Random Numbers E.2 The Cumulative Standardized Normal Distribution E.3 Critical Values of t E.4 Critical Values of E.5 Critical Values of F E.6 Lower and Upper Critical Values, of the Wilcoxon Rank Sum Test E.7 Critical Values of the Studentized Range, Q E.8 Critical Values, and of the Durbin–Watson Statistic, D (Critical Values Are One–Sided) E.9 Control Chart Factors E.10 The Standardized Normal Distribution F. Useful Knowledge F.1 Keyboard Shortcuts F.2 Understanding the Nonstatistical Functions G. Software FAQs G.1 Microsoft Excel FAQs G.2 PHStat FAQs G.3 JMP FAQs G.4 Minitab FAQs H. All About PHStat H.1 What is PHStat? H.2 Obtaining and Setting Up PHStat H.3 Using PHStat H.4 PHStat Procedures, by Category Self-Test Solutions and Answers to Selected Even-Numbered Problems Index Credits
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