Essentials of Modern Business Statistics with Microsoft Excel

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Title Page Copyright Brief Contents Contents Preface Acknowledgments 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 Statistical Analysis Using Microsoft Excel Data Sets and Excel Worksheets Using Excel for Statistical Analysis 1.7 Analytics 1.8 Big Data and Data Mining 1.9 Ethical Guidelines for Statistical Practice Summary Glossary Supplementary Exercises 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 Using Excel to Construct a Frequency Distribution, a Relative Frequency Distribution, and a Percent Frequency Distribution Bar Charts and Pie Charts Using Excel to Construct a Bar Chart and a Pie Chart 2.2 Summarizing Data for a Quantitative Variable Frequency Distribution Relative Frequency and Percent Frequency Distributions Using Excel to Construct a Frequency Distribution Dot Plot Histogram Using Excel’s Recommended Charts Tool to Construct a Histogram Cumulative Distributions Stem-and-Leaf Display 2.3 Summarizing Data for Two Variables Using Tables Crosstabulation Using Excel’s PivotTable Tool to Construct a Crosstabulation Simpson’s Paradox 2.4 Summarizing Data for Two Variables Using Graphical Displays Scatter Diagram and Trendline Using Excel to Construct a Scatter Diagram and a Trendline Side-by-Side and Stacked Bar Charts Using Excel’s Recommended Charts Tool to Construct 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: Motion Picture Industry Case Problem 3: Queen City Case Problem 4: Cut-Rate Machining, Inc. Chapter 3: Descriptive Statistics: Numerical Measures Statistics In Practice: Small Fry Design 3.1 Measures of Location Mean Median Mode Using Excel to Compute the Mean, Median, and Mode Weighted Mean Geometric Mean Using Excel to Compute the Geometric Mean Percentiles Quartiles Using Excel to Compute Percentiles and Quartiles 3.2 Measures of Variability Range Interquartile Range Variance Standard Deviation Using Excel to Compute the Sample Variance and Sample Standard Deviation Coefficient of Variation Using Excel’s Descriptive Statistics Tool 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 Box Plots Five-Number Summary Box Plot Using Excel to Construct a Box Plot Comparative Analysis Using Box Plots Using Excel to Construct a Comparative Analysis Using Box Plots 3.5 Measures of Association Between Two Variables Covariance Interpretation of the Covariance Correlation Coefficient Interpretation of the Correlation Coefficient Using Excel to Compute the Sample Covariance and Sample 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: Motion Picture Industry Case Problem 3: Business Schools of Asia-Pacific Case Problem 4: Heavenly Chocolates Website Transactions Case Problem 5: African Elephant Populations Chapter 4: Introduction to Probability Statistics In Practice: National Aeronautics And Space Administration 4.1 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: Citibank 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 Using Excel to Compute the Expected Value, Variance, and Standard Deviation 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 Excel to Compute 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 Using Excel to Compute Poisson Probabilities 5.7 Hypergeometric Probability Distribution Using Excel to Compute Hypergeometric Probabilities Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Go Bananas! Case Problem 2: McNeil’s Auto Mall Case Problem 3: Grievance Committee at Tuglar Corporation Case Problem 4: Sagittarius Casino 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 Using Excel to Compute Normal Probabilities 6.3 Exponential Probability Distribution Computing Probabilities for the Exponential Distribution Relationship Between the Poisson and Exponential Distributions Using Excel to Compute Exponential Probabilities Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Specialty Toys Case Problem 2: Gebhardt Electronics 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_(Bar) Expected Value of x_(Bar) Standard Deviation of x_(Bar) Form of the Sampling Distribution of x_(Bar) Sampling Distribution of x_(Bar) for the EAI Problem Practical Value of the Sampling Distribution of x_(Bar) Relationship Between the Sample Size and the Sampling Distribution of x_(Bar) 7.6 Sampling Distribution of p_(Bar) Expected Value of p_(Bar) Standard Deviation of p_(Bar) Form of the Sampling Distribution of p_(Bar) Practical Value of the Sampling Distribution of p_(Bar) 7.7 Other Sampling Methods Stratified Random Sampling Cluster Sampling Systematic Sampling Convenience Sampling Judgment Sampling 7.8 Practical Advice: Big Data and Errors in Sampling Sampling Error Nonsampling Error Implications of Big Data Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Marion Dairies Chapter 8: Interval Estimation Statistics In Practice: Food Lion 8.1 Population Mean: sigma Known Margin of Error and the Interval Estimate Using Excel Practical Advice 8.2 Population Mean: sigma Unknown Margin of Error and the Interval Estimate Using Excel Practical Advice Using a Small Sample Summary of Interval Estimation Procedures 8.3 Determining the Sample Size 8.4 Population Proportion Using Excel Determining the Sample Size 8.5 Practical Advice: Big Data and Interval Estimation Big Data and the Precision of Confidence Intervals Implications of Big Data 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 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: sigma Known One-Tailed Test Two-Tailed Test Using Excel Summary and Practical Advice Relationship Between Interval Estimation and Hypothesis Testing 9.4 Population Mean: sigma Unknown One-Tailed Test Two-Tailed Test Using Excel Summary and Practical Advice 9.5 Population Proportion Using Excel Summary 9.6 Practical Advice: Big Data and Hypothesis Testing Big Data and p-Values Implications of Big Data Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Quality Associates, Inc. Case Problem 2: Ethical Behavior of Business Students at Bayview University 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: sigma(subscript_1) and sigma(subscript_2) Known Interval Estimation of mu(subscript_1) - mu(subscript_2) Using Excel to Construct a Confidence Interval Hypothesis Tests About mu(subscript_1) - mu(subscript_2) Using Excel to Conduct a Hypothesis Test Practical Advice 10.2 Inferences About the Difference Between Two Population Means: sigma(subscript_1) and sigma(subscript_2) Unknown Interval Estimation of mu(subscript_1) - mu(subscript_2) Using Excel to Construct a Confidence Interval Hypothesis Tests About mu(subscript_1) - mu(subscript_2) Using Excel to Conduct a Hypothesis Test Practical Advice 10.3 Inferences About the Difference Between Two Population Means: Matched Samples Using Excel to Conduct a Hypothesis Test 10.4 Inferences About the Difference Between Two Population Proportions Interval Estimation of p(subscript_1) - (subscript_2) Using Excel to Construct a Confidence Interval Hypothesis Tests About p(subscript_1) - p(subscript_2) Using Excel to Conduct a Hypothesis Test Summary Glossary Key Formulas Supplementary Exercises Case Problem: Par, Inc. Chapter 11: Inferences About Population Variances Statistics In Practice: U.S. Government Accountability Office 11.1 Inferences About a Population Variance Interval Estimation Using Excel to Construct a Confidence Interval Hypothesis Testing Using Excel to Conduct a Hypothesis Test 11.2 Inferences About Two Population Variances Using Excel to Conduct a Hypothesis Test Summary Key Formulas Supplementary Exercises Case Problem 1: Air Force Training Program Case Problem 2: Meticulous Drill & Reamer Chapter 12: Tests of Goodness of Fit, Independence, and Multiple Proportions Statistics In Practice: United Way 12.1 Goodness of Fit Test Multinomial Probability Distribution Using Excel to Conduct a Goodness of Fit Test 12.2 Test of Independence Using Excel to Conduct a Test of Independence 12.3 Testing for Equality of Three or More Population Proportions A Multiple Comparison Procedure Using Excel to Conduct a Test of Multiple Proportions 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 13: Experimental Design and 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 Using Excel 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 Using Excel 13.5 Factorial Experiment ANOVA Procedure Computations and Conclusions Using Excel 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 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 Using Excel to Construct a Scatter Diagram, Display the Estimated Regression Line, and Display the Estimated Regression Equation 14.3 Coefficient of Determination Using Excel to Compute the Coefficient of Determination Correlation Coefficient 14.4 Model Assumptions 14.5 Testing for Significance Estimate of sigma(superscript2) t Test Confidence Interval for Bita(subscript1) 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 Excel’s Regression Tool Using Excel’s Regression Tool for the Armand’s Pizza Parlors Example Interpretation of Estimated Regression Equation Output Interpretation of ANOVA Output Interpretation of Regression Statistics Output 14.8 Residual Analysis: Validating Model Assumptions Residual Plot Against x Residual Plot Against y(Bar) Standardized Residuals Using Excel to Construct a Residual Plot Normal Probability Plot 14.9 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 Chapter 15: Multiple Regression Statistics In Practice: International Paper 15.1 Multiple Regression Model Regression Model and Regression Equation Estimated Multiple Regression Equation 15.2 Least Squares Method An Example: Butler Trucking Company Using Excel’s Regression Tool to Develop the Estimated Multiple Regression Equation 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 Residual Plot Against y⁄ Standardized Residual Plot Against y⁄ 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 A: References and Bibliography Appendix B: Tables Appendix C: Summation Notation Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises (online) Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis Index