Statistics for Managers Using Microsoft Excel, 9th Edition By David Levine, David Stephan, Kathryn Szabat | eBook [PDF]

Statistics for Managers Using Microsoft Excel, 9th Edition By David Levine, David Stephan, Kathryn Szabat | eBook [PDF] MyLab Statistics Support You Need, When You Need It A Roadmap for Selecting a Statistical Method ...

Statistics for Managers Using Microsoft Excel, 9th Edition By David Levine, David Stephan, Kathryn Szabat | eBook [PDF] MyLab Statistics Support You Need, When You Need It A Roadmap for Selecting a Statistical Method Statistics for Managers Using Microsoft® Excel® About the Authors Brief Contents Contents Preface What’s New in This Edition? Continuing Features That Readers Have Come to Expect Chapter-by-Chapter Changes Made for This Edition Serious About Writing Improvements A Note of Thanks Contact Us! Get the most out of MyLab Statistics MyLab Statistics Online Course for Statistics for Managers Using Microsoft® Excel®, 9th Edition by Levine, Stephan, Szabat (access code required) Resources for Success Instructor Resources Student Resources First Things First Contents Objectives Now Appearing on Broadway … and Everywhere Else FTF.1 Think Differently About Statistics Statistics: A Way of Thinking DCOVA Framework Analytical Skills More Important Than Arithmetic Skills Statistics: An Important Part of Your Business Education FTF.2 Business Analytics: The Changing Face of Statistics “Big Data” Unstructured Data FTF.3 Starting Point for Learning Statistics Statistic Can Statistics (pl., statistic) Lie? FTF.4 Starting Point for Using Software Using Software Properly FTF.5 Starting Point for Using Microsoft Excel More About the Excel Guide Workbooks Reusability Excel Skills That Readers Need Excel Guide Instructions 1 Defining and Collecting Data Contents Objectives 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 Summary References Key Terms Checking Your Understanding Chapter Review Problems 2 Organizing and Visualizing Variables Contents Objectives 2.1 Organizing Categorical Variables The Summary Table The Contingency Table 2.2 Organizing Numerical Variables The Frequency Distribution 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 The Side-by-Side Chart The Doughnut Chart 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 (Tableau) Bubble Chart PivotChart Treemap Sparklines 2.8 Filtering and Querying Data Excel Slicers 2.9 Pitfalls in Organizing and Visualizing Variables Obscuring Data Creating False Impressions Chartjunk Summary References Key Equations Determining the Class Interval Width Computing the Proportion or Relative Frequency Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 3 Numerical Descriptive Measures Contents Objectives 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 Percentiles The Interquartile Range The Five-Number Summary The Boxplot 3.4Numerical 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 Summary References Key Equations Sample Mean Median Geometric Mean Geometric Mean Rate of Return Range Sample Variance Sample Standard Deviation Coefficient of Variation Z Score First Quartile, Q1 Third Quartile, Q3 Interquartile Range Population Mean Population Variance Population Standard Deviation Sample Covariance Sample Coefficient of Correlation Key Terms Checking Your Understanding Chapter Review Problems Report Writing ExerciseS 4 Basic Probability Contents Objectives 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 Calculating Conditional Probabilities Decision Trees Independence Multiplication Rules Marginal Probability Using the General Multiplication Rule 4.3 Ethical Issues and Probability 4.4 Bayes' Theorem 4.5 Counting Rules Summary References Key Equations Probability of Occurrence Marginal Probability General Addition Rule Conditional Probability Independence General Multiplication Rule Multiplication Rule for Independent Events Marginal Probability Using the General Multiplication Rule Key Terms Checking Your Understanding Chapter Review Problems 5 Discrete Probability Distributions Contents Objectives 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 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 Summary References Key Equations Expected Value, μ, of a Discrete Variable Variance of a Discrete Variable Standard Deviation of a Discrete Variable Combinations Binomial Distribution Mean of the Binomial Distribution Standard Deviation of the Binomial Distribution Poisson Distribution Key Terms Checking Your Understanding Chapter Review Problems 6 The Normal Distribution and Other Continuous Distributions Contents Objectives 6.1 Continuous Probability Distributions 6.2 The Normal Distribution Role of the Mean and the Standard Deviation Calculating Normal Probabilities Finding X Values 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 6.6 The Normal Approximation to the Binomial Distribution Summary References Key Equations Normal Probability Density Function Z Transformation Formula Finding an X Value Associated with a Known Probability Uniform Probability Density Function Mean of the Uniform Distribution Variance and Standard Deviation of the Uniform Distribution Key Terms Checking Your Understanding Chapter Review Problems 7 Sampling Distributions Contents Objectives 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 7.3 Sampling Distribution of the Proportion 7.4 Sampling from Finite Populations Summary References Key Equations Population Mean Population Standard Deviation Standard Error of the Mean Finding Z for the Sampling Distribution of the Mean Finding for the Sampling Distribution of the Mean Sample Proportion Standard Error of the Proportion Finding Z for the Sampling Distribution of the Proportion Key Terms Checking Your Understanding Chapter Review Problems 8 Confidence Interval Estimation Contents Objectives 8.1 Confidence Interval Estimate for the Mean (σ Known) Sampling Error Can You Ever Know the Population Standard Deviation? 8.2 Confidence Interval Estimate for the Mean (σ 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 8.7 Estimation and Sample Size Determination for Finite Populations 8.8 Bootstrapping Summary References Key Equations Confidence Interval for the Mean (σ Known) Confidence Interval for the Mean (σ Unknown) Confidence Interval Estimate for the Proportion Sample Size Determination for the Mean Sample Size Determination for the Proportion Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 9 Fundamentals of Hypothesis Testing: One-Sample Tests Contents Objectives 9.1 Fundamentals of Hypothesis Testing The Critical Value of the Test Statistic Regions of Rejection and Nonrejection Risks in Decision Making Using Hypothesis Testing Complements of Type I and Type II Errors Z Test for the Mean (σ Known) Hypothesis Testing Using the Critical Value Approach Hypothesis Testing Using the p-Value Approach 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 (σ Unknown) Using the Critical Value Approach Using the p-Value Approach Checking the Normality Assumption 9.3 One-Tail Tests Using the Critical Value Approach Using the p-Value Approach 9.4 Z Test of Hypothesis for the Proportion Using the Critical Value Approach Using the p-Value Approach 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues Important Planning Stage Questions Statistical Significance Versus Practical Significance Statistical Insignificance Versus Importance Reporting of Findings Ethical Issues 9.6 Power of the Test Summary References Key Equations Z Test for the Mean (σ Known) t Test for the Mean (σ Unknown) Z Test for the Proportion Z Test for the Proportion in Terms of the Number of Events of Interest Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 10 Two-Sample Tests Contents Objectives 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 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 Summary References Key Equations Pooled-Variance t Test for the Difference Between Two Means Confidence Interval Estimate for the Difference Between the Means of Two Independent Populations Paired t Test for the Mean Difference Confidence Interval Estimate for the Mean Difference Z Test for the Difference Between Two Proportions Confidence Interval Estimate for the Difference Between Two Proportions F Test Statistic for Testing the Ratio of Two Variances Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 11 Analysis of Variance Contents Objectives 11.1 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 11.2 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 11.4 Fixed Effects, Random Effects, and Mixed Effects Models Summary References Key Equations Total Variation in One-Way ANOVA Among-Group Variation in One-Way ANOVA Within-Group Variation in One-Way ANOVA Mean Squares in One-Way ANOVA One-Way ANOVA FSTAT Test Statistic Critical Range for the Tukey-Kramer Procedure Total Variation in Two-Way ANOVA Factor A Variation in Two-Way ANOVA Factor B Variation in Two-Way ANOVA Interaction Variation in Two-Way ANOVA Random Variation in Two-Way ANOVA Mean Squares in Two-Way ANOVA F Test for Factor A Effect F Test for Factor B Effect F Test for Interaction Effect Critical Range for Factor A Critical Range for Factor B Key Terms Checking Your Understanding Chapter Review Problems 12 Chi-Square and Nonparametric Tests Contents Objectives 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) 12.7 Chi-Square Test for the Variance or Standard Deviation 12.8 Wilcoxon Signed Ranks Test for Two Related Populations Summary Key Equations χ2 Test Statistic The Estimated Overall Proportion for Two Groups The Estimated Overall Proportion for c Groups Critical Range for the Marascuilo Procedure Calculating the Expected Frequency Checking the Rankings Large-Sample Wilcoxon Rank Sum Test Kruskal-Wallis Rank Test for Differences Among c Medians Key Terms Checking Your Understanding Chapter Review Problems 13 Simple Linear Regression Contents Objectives 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 Calculating the Slope, b1, and the Y Intercept, b0 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 Linearity  Independence  Normality  Equal Variance  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 Summary References Key Equations Simple Linear Regression Model Simple Linear Regression Equation: The Prediction Line Computational Formula for the Slope, b1 Computational Formula for the Y Intercept, b0 Measures of Variation in Regression Total Sum of Squares (SST) Regression Sum of Squares (SSR) Error Sum of Squares (SSE) Computational Formula for SST Computational Formula for SSR Computational Formula for SSE Coefficient of Determination Standard Error of the Estimate Residual Durbin-Watson Statistic t Test Statistic for Testing a Hypothesis for a Population Slope, β1 F Test Statistic for Testing a Hypothesis for a Population Slope, β1 Confidence Interval Estimate of the Slope, β1 Testing for the Existence of Correlation Confidence Interval Estimate for the Mean of Y Prediction Interval for an Individual Response, Y Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 14 Introduction to Multiple Regression Contents Objectives 14.1 Developing a Multiple Regression Model Interpreting the Regression Coefficients Predicting the Dependent Variable Y 14.2 Evaluating Multiple Regression Models Coefficient of Multiple Determination, r2 Adjusted r2 F 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 Cross-Validation Summary References Key Equations Multiple Regression Model with k Independent Variables Multiple Regression Model with Two Independent Variables Multiple Regression Equation with Two Independent Variables Coefficient of Multiple Determination Adjusted r2 Overall F Test Testing for the Slope in Multiple Regression Confidence Interval Estimate for the Slope Determining the Contribution of an Independent Variable to the Regression Model Contribution of Variable X1, Given That X2 Has Been Included Contribution of Variable X2, Given That X1 Has Been Included Partial F Test Statistic Relationship Between a t Statistic and an F Statistic Coefficients of Partial Determination for a Multiple Regression Model Containing Two Independent Variables Coefficient of Partial Determination for a Multiple Regression Model Containing k Independent Variables Odds Ratio Logistic Regression Model Logistic Regression Equation Estimated Odds Ratio Estimated Probability of an Event of Interest Key Terms Checking Your Understanding Chapter Review Problems 15 Multiple Regression Model Building Contents Objectives 15.1 The 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 The Stepwise Regression Approach to Model Building The Best Subsets Approach to Model Building 15.5 Pitfalls in Multiple Regression and Ethical Issues Pitfalls in Multiple Regression Ethical Issues Summary References Key Equations Quadratic Regression Model Quadratic Regression Equation Regression Model with a Square-Root Transformation Original Multiplicative Model Transformed Multiplicative Model Original Exponential Model Transformed Exponential Model Variance Inflationary Factor Cp Statistic Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 16 Time-Series Forecasting Contents Objectives 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 16.4 Autoregressive Modeling for Trend Fitting and Forecasting Selecting an Appropriate Autoregressive Model Determining the Appropriateness of a Selected Model 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 Summary REFERENCES Key Equations An Exponentially Smoothed Value for Time Period i Forecasting Time Period i + 1 Linear Trend Forecasting Equation Quadratic Trend Forecasting Equation Exponential Trend Model Transformed Exponential Trend Model Exponential Trend Forecasting Equation pth-Order Autoregressive Models First-Order Autoregressive Model Second-Order Autoregressive Model t Test for Significance of the Highest-Order Autoregressive Parameter, AP Fitted pth-Order Autoregressive Equation pth-Order Autoregressive Forecasting Equation Mean Absolute Deviation Exponential Model With Quarterly Data Transformed Exponential Model With Quarterly Data Exponential Growth With Quarterly Data Forecasting Equation Exponential Model With Monthly Data Transformed Exponential Model With Monthly Data Exponential Growth With Monthly Data Forecasting Equation Key Terms Checking Your Understanding Chapter Review Problems Report Writing Exercise 17 Business Analytics Contents Objectives 17.1 Business Analytics Overview Business Analytics Categories Business Analytics Vocabulary Inferential Statistics and Predictive Analytics Microsoft Excel and Business Analytics Remainder of This Chapter 17.2 Descriptive Analytics Dashboards Data Dimensionality and Descriptive Analytics 17.3 Decision Trees Regression Trees Classification Trees Subjectivity and Interpretation 17.4 Clustering 17.5 Association Analysis 17.6 Text Analytics 17.7 Prescriptive Analytics Optimization and Simulation 18 Getting Ready to Analyze Data in the Future Contents Objectives 18.1 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? If the Grouping Variable Defines Two Independent Groups and You Are Interested in Central Tendency If the Grouping Variable Defines Two Groups of Matched Samples or Repeated Measurements and You Are Interested in Central Tendency If the Grouping Variable Defines Two Independent Groups and You Are Interested in Variability If the Grouping Variable Defines More Than Two Independent Groups and You Are Interested in Central Tendency If the Grouping Variable Defines More Than Two Groups of Matched Samples or Repeated Measurements and You Are Interested in Central Tendency 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 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? For Two Categories and Two Independent Groups For Two Categories and Two Groups of Matched or Repeated Measurements For Two Categories and More Than Two Independent Groups For More Than Two Categories and More Than Two Groups Predict the Proportion of Items of Interest Based on the Values of Other Variables? Cluster or Associate Items? Determine Whether the Proportion of Items of Interest Is Stable Over Time? 19 Statistical Applications in Quality Management Contents Objectives 19.1 The Theory of Control Charts The Causes of Variation 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 Summary References Key Equations Constructing Control Limits Control Limits for the p Chart Control Limits for the c Chart Control Limits for the Range Computing Control Limits for the Range Control Limits for the X¯ Chart Computing Control Limits for the Mean, Using the A2 Factor Estimating the Capability of a Process The Cp Index CPL and CPU Key Terms Chapter Review Problems Checking Your Understanding Applying the Concepts 20 Decision Making Contents Objectives 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 Summary References Key Equations Expected Monetary Value Expected Opportunity Loss Expected Value of Perfect Information Return-to-Risk Ratio Key Terms Chapter Review Problems Checking Your Understanding Applying the Concepts Appendices Appendix 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 Base 10 Base e A.5 Summation Notation References A.6 Greek Alphabet Appendix B Important Software Skills and Concepts B.1 Identifying the Software Version Excel Identify the build number Tableau (Public version) B.2 Formulas Entering a Formula Entering an Array Formula (Excel) Pasting with Paste Special (Excel) Verifying Formulas B.3 Excel Cell References Absolute and Relative Cell References Selecting Cell Ranges for Charts Selecting Non-contiguous Cell Ranges B.4 Excel Worksheet Formatting Format Cells Method Home Tab Shortcuts Method B.5E Excel Chart Formatting Most Commonly Made Changes Chart and Axis Titles Chart Axes Correcting the Display of the X Axis Emphasizing Histogram Bars B.5T Tableau Chart Formatting B.6 Creating Histograms for Discrete Probability Distributions (Excel) B.7 Deleting the “Extra” Histogram Bar (Excel) Using Non-numeric Labels in a Time-Series Plot Appendix C Online Resources C.1 About the Online Resources for This Book Access the Online Resources C.2 Data Files C.3 Microsoft Excel Files Integrated With This Book Excel Guide Workbooks PHStat Visual Explorations C.4 Supplemental Files Appendix D Configuring Software D.1 Microsoft Excel Configuration Step 1: Update Excel Step 2: Verify Microsoft Add-Ins Step 3: Verify Excel Security Settings Step 4: Opening Add-ins D.2 Supplemental Files Appendix E Table Appendix F Useful Knowledge F.1 Keyboard Shortcuts Editing Shortcuts Excel Formatting & Utility Shortcuts Tableau Utility Commands F.2 Understanding the Nonstatistical Excel Functions Appendix G Software FAQs G.1 Microsoft Excel FAQs G.2 PHStat FAQs G.3 Tableau FAQs Appendix H All About PHStat H.1 What is PHStat? How PHStat Works Preparing Data for PHStat Analysis 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 Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Index A B C D E F G H I J K L M N O P Q R S T U V W Y Z Credits Photos Cover Chapter 00 Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 The Cumulative Standardized Normal Distribution The Analysis of Means (ANOM) The Analysis of Proportions (ANOP) Bayesian Analysis Problems for Bayesian Analysis Learning the Basics Applying the Concepts EG Bayesian Analysis Key Technique Example Workbook Binomial Probabilities and Cumulative Binomial Probabilities Tables (tables begin on the next page) All About 401(k) Retirement Funds References Selecting a Simple Random Sample by Using a Table of Random Numbers 4.5 Counting Rules Problems for Section 4.5 Applying the Concepts 5.4 Covariance of a Probability Distribution and Its Application in Finance Problems for Section 5.4 Learning the Basics Applying the Concepts EG5.4 Covariance of a Probability Distribution and its Application in Finance 5.5 Hypergeometric Distribution Problems for Section 5.5 Learning the Basics Applying the Concepts EG5.5 Hypergeometric Distribution 6.5 The Exponential Distribution Problems for Section 6.5 Learning the Basics Applying the Concepts EG6.5 The Exponential Distribution 6.6 The Normal Approximation to the Binomial Distribution Problems for Section 6.6 Learning the Basics Applying the Concepts 7.4 Sampling from Finite Populations Problems for Section 7.4 Learning the Basics Applying the Concepts 8.6 Applications of Confidence Interval Estimation in Auditing Problems for Section 8.6 Learning the Basics Applying the Concepts EG8.6 Applications of Confidence Interval Estimation in Auditing Estimating the Population Total Amount Example PHStat Workbook Difference Estimation Example PHStat Workbook 8.7 Estimation and Sample Size Determination for Finite Populations Problems for Section 8.7 Learning the Basics Applying the Concepts 8.8 Bootstrapping References 9.6 The Power of a Test Problems for Section 9.6 Applying the Concepts 10.5 Effect Size Effect Size for the Difference Between Two Proportions References 11.3 The Randomized Block Design Problems for Section 11.3 Learning the Basics Applying the Concepts EG11.2 The Randomized Block Design Key Technique Example PHStat Workbook Analysis ToolPak 11.4 Fixed Effects Models, Random Effects Models, and Mixed Effects Models 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples) Problems for Section 12.6 Learning the Basics Applying the Concepts 12.7 Chi-Square Test for the Variance or Standard Deviation Problems for Section 12.7 Learning the Basics Applying the Concepts EG12.7 CHI-SQUARE TEST for the VARIANCE or STANDARD DEVIATION 12.8 Wilcoxon Signed Ranks Test: Nonparametric Analysis for Two Related Populations Problems for Section 12.8 Learning the Basics Applying the Concepts 16.7 Index Numbers Problems for Section 16.7 Learning the Basics Applying the Concepts Short Takes for Chapter 1 For 1.1 Defining Variables Measurement Scales for Variables For Nominal and Ordinal Scales For Interval and Ratio Scales For 1.2 Collecting Data For Data Sources For EG1.3 Types of Sampling Methods For Simple Random Sample For EG1.4 Data Cleaning Short Takes for Chapter 3 For 3.2 Measures of Variation and Shape For The Coefficient of Variation For Shape: Skewness For Shape: Kurtosis For 3.3 Exploring Numerical Data For EG3.3 Exploring Numerical Data For Quartiles For The Five-Number Summary and the Boxplot For EG3.5 The Covariance and the Coefficient of Correlation For The Covariance Short Takes for Chapter 5 For EG5.2 Binomial Distribution For EG5.3 Poisson Distribution For EG5.5 Hypergeometric Distribution Short Takes for Chapter 6 For EG6.2 The Normal Distribution Short Takes for Chapter 7 For 7.2 Sampling Distribution of the Mean For The Unbiased Property of the Sample Mean Short Takes for Chapter 11 For EG11.2 The Factorial Design: Two-Way Analysis of Variance Short Takes for Chapter 14 For EG14.1 Developing a Multiple Regression Model Interpreting the Regression Coefficients