Title | Statistics for Business and Economics |
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File Size | 9.6 MB |

Total Pages | 1117 |

Front Cover Title Page Copyright Contents Preface About the Authors Chapter 1 Data and Statistics Statistics in Practice: BusinessWeek 1.1 Applications in Business and Economics Accounting Finance Marketing Production Economics 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 Statistical Studies Data Acquisition Errors 1.4 Descriptive Statistics 1.5 Statistical Inference 1.6 Computers and Statistical Analysis 1.7 Data Mining 1.8 Ethical Guidelines for Statistical Practice Summary Glossary Supplementary Exercises Appendix: An Introduction to StatTools Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations Statistics in Practice: Colgate-Palmolive Company 2.1 Summarizing Categorical Data Frequency Distribution Relative Frequency and Percent Frequency Distributions Bar Charts and Pie Charts 2.2 Summarizing Quantitative Data Frequency Distribution Relative Frequency and Percent Frequency Distributions Dot Plot Histogram Cumulative Distributions Ogive 2.3 Exploratory Data Analysis: The Stem-and-Leaf Display 2.4 Crosstabulations and Scatter Diagrams Crosstabulation Simpson's Paradox Scatter Diagram and Trendline Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Pelican Stores Case Problem 2: Motion Picture Industry Appendix 2.1 Using Minitab for Tabular and Graphical Presentations Appendix 2.2 Using Excel for Tabular and Graphical Presentations Appendix 2.3 Using StatTools for Tabular and Graphical Presentations Chapter 3 Descriptive Statistics: Numerical Measures Statistics in Practice: Small Fry Design 3.1 Measures of Location Mean Median Mode Percentiles Quartiles 3.2 Measures of Variability Range Interquartile Range Variance Standard Deviation Coefficient of Variation 3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers Distribution Shape z-Scores Chebyshev's Theorem Empirical Rule Detecting Outliers 3.4 Exploratory Data Analysis Five-Number Summary Box Plot 3.5 Measures of Association Between Two Variables Covariance Interpretation of the Covariance Correlation Coefficient Interpretation of the Correlation Coefficient 3.6 The Weighted Mean and Working with Grouped Data Weighted Mean Grouped Data 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 Appendix 3.1 Descriptive Statistics Using Minitab Appendix 3.2 Descriptive Statistics Using Excel Appendix 3.3 Descriptive Statistics Using StatTools Chapter 4 Introduction to Probability Statistics in Practice: Oceanwide Seafood 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: Hamilton County Judges Chapter 5 Discrete Probability Distributions Statistics in Practice: Citibank 5.1 Random Variables Discrete Random Variables Continuous Random Variables 5.2 Discrete Probability Distributions 5.3 Expected Value and Variance Expected Value Variance 5.4 Binomial Probability Distribution A Binomial Experiment Martin Clothing Store Problem Using Tables of Binomial Probabilities Expected Value and Variance for the Binomial Distribution 5.5 Poisson Probability Distribution An Example Involving Time Intervals An Example Involving Length or Distance Intervals 5.6 Hypergeometric Probability Distribution Summary Glossary Key Formulas Supplementary Exercises Appendix 5.1 Discrete Probability Distributions with Minitab Appendix 5.2 Discrete Probability Distributions with Excel 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 6.3 Normal Approximation of Binomial Probabilities 6.4 Exponential Probability Distribution Computing Probabilities for the Exponential Distribution Relationship Between the Poisson and Exponential Distributions Summary Glossary Key Formulas Supplementary Exercises Case Problem: Specialty Toys Appendix 6.1 Continuous Probability Distributions with Minitab Appendix 6.2 Continuous Probability Distributions with Excel 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 Expected Value of x Standard Deviation of x Form of the Sampling Distribution of x Sampling Distribution of x for the EAI Problem Practical Value of the Sampling Distribution of x Relationship Between the Sample Size and the Sampling Distribution of x 7.6 Sampling Distribution of p Expected Value of p Standard Deviation of p Form of the Sampling Distribution of p Practical Value of the Sampling Distribution of p 7.7 Properties of Point Estimators Unbiased Efficiency Consistency 7.8 Other Sampling Methods Stratified Random Sampling Cluster Sampling Systematic Sampling Convenience Sampling Judgment Sampling Summary Glossary Key Formulas Supplementary Exercises Appendix 7.1 The Expected Value and Standard Deviation of x Appendix 7.2 Random Sampling with Minitab Appendix 7.3 Random Sampling with Excel Appendix 7.4 Random Sampling with StatTools Chapter 8 Interval Estimation Statistics in Practice: Food Lion 8.1 Population Mean: σ Known Margin of Error and the Interval Estimate Practical Advice 8.2 Population Mean: σ Unknown Margin of Error and the Interval Estimate Practical Advice Using a Small Sample Summary of Interval Estimation Procedures 8.3 Determining the Sample Size 8.4 Population Proportion Determining the Sample Size 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. Appendix 8.1 Interval Estimation with Minitab Appendix 8.2 Interval Estimation with Excel Appendix 8.3 Interval Estimation with StatTools 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: σ Known One-Tailed Test Two-Tailed Test Summary and Practical Advice Relationship Between Interval Estimation and Hypothesis Testing 9.4 Population Mean: σ Unknown One-Tailed Test Two-Tailed Test Summary and Practical Advice 9.5 Population Proportion Summary 9.6 Hypothesis Testing and Decision Making 9.7 Calculating the Probability of Type II Errors 9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Quality Associates, Inc. Case Problem 2: Ethical Behavior of Business Students at Bayview University Appendix 9.1 Hypothesis Testing with Minitab Appendix 9.2 Hypothesis Testing with Excel Appendix 9.3 Hypothesis Testing with StatTools 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: σ[sub(1)] and σ[sub(2)] Known Interval Estimation of μ[sub(1)] – μ[sub(2)] Hypothesis Tests About μ[sub(1)] – μ[sub(2)] Practical Advice 10.2 Inferences About the Difference Between Two Population Means: σ[sub(1)] and σ[sub(2)] Unknown Interval Estimation of μ[sub(1)] – μ[sub(2)] Hypothesis Tests About μ[sub(1)] – μ[sub(2)] Practical Advice 10.3 Inferences About the Difference Between Two Population Means: Matched Samples 10.4 Inferences About the Difference Between Two Population Proportions Interval Estimation of p[sub(1)] – p[sub(2)] Hypothesis Tests About p[sub(1)] – p[sub(2)] Summary Glossary Key Formulas Supplementary Exercises Case Problem: Par, Inc. Appendix 10.1 Inferences About Two Populations Using Minitab Appendix 10.2 Inferences About Two Populations Using Excel Appendix 10.3 Inferences About Two Populations Using StatTools Chapter 11 Inferences About Population Variances Statistics in Practice: U.S. Government Accountability Office 11.1 Inferences About a Population Variance Interval Estimation Hypothesis Testing 11.2 Inferences About Two Population Variances Summary Key Formulas Supplementary Exercises Case Problem: Air Force Training Program Appendix 11.1 Population Variances with Minitab Appendix 11.2 Population Variances with Excel Appendix 11.3 Population Standard Deviation with StatTools Chapter 12 Tests of Goodness of Fit and Independence Statistics in Practice: United Way 12.1 Goodness of Fit Test: A Multinomial Population 12.2 Test of Independence 12.3 Goodness of Fit Test: Poisson and Normal Distributions Poisson Distribution Normal Distribution Summary Glossary Key Formulas Supplementary Exercises Case Problem: A Bipartisan Agenda for Change Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel 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 Computer Results for Analysis of Variance 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 13.5 Factorial Experiment ANOVA Procedure Computations and Conclusions Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Wentworth Medical Center Case Problem 2: Compensation for Sales Professionals Appendix 13.1 Analysis of Variance with Minitab Appendix 13.2 Analysis of Variance with Excel Appendix 13.3 Analysis of Variance with StatTools 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 14.3 Coefficient of Determination Correlation Coefficient 14.4 Model Assumptions 14.5 Testing for Significance Estimate of σ[sup(2)] t Test Confidence Interval for β[sub(1)] F Test Some Cautions About the Interpretation of Significance Tests 14.6 Using the Estimated Regression Equation for Estimation and Prediction Point Estimation Interval Estimation Confidence Interval for the Mean Value of y Prediction Interval for an Individual Value of y 14.7 Computer Solution 14.8 Residual Analysis: Validating Model Assumptions Residual Plot Against x Residual Plot Against y Standardized Residuals Normal Probability Plot 14.9 Residual Analysis: Outliers and Influential Observations Detecting Outliers Detecting Influential Observations Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Measuring Stock Market Risk Case Problem 2: U.S. Department of Transportation Case Problem 3: Alumni Giving Case Problem 4: PGA Tour Statistics Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas Appendix 14.2 A Test for Significance Using Correlation Appendix 14.3 Regression Analysis with Minitab Appendix 14.4 Regression Analysis with Excel Appendix 14.5 Regression Analysis with StatTools Chapter 15 Multiple Regression Statistics in Practice: dunnhumby 15.1 Multiple Regression Model Regression Model and Regression Equation Estimated Multiple Regression Equation 15.2 Least Squares Method An Example: Butler Trucking Company 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 Detecting Outliers Studentized Deleted Residuals and Outliers Influential Observations Using Cook’s Distance Measure to Identify Influential Observations 15.9 Logistic Regression Logistic Regression Equation Estimating the Logistic Regression Equation Testing for Significance Managerial Use Interpreting the Logistic Regression Equation Logit Transformation Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Consumer Research, Inc. Case Problem 2: Alumni Giving Case Problem 3: PGA Tour Statistics Case Problem 4: Predicting Winning Percentage for the NFL Appendix 15.1 Multiple Regression with Minitab Appendix 15.2 Multiple Regression with Excel Appendix 15.3 Logistic Regression with Minitab Appendix 15.4 Multiple Regression with StatTools Chapter 16 Regression Analysis: Model Building Statistics in Practice: Monsanto Company 16.1 General Linear Model Modeling Curvilinear Relationships Interaction Transformations Involving the Dependent Variable Nonlinear Models That Are Intrinsically Linear 16.2 Determining When to Add or Delete Variables General Case Use of p-Values 16.3 Analysis of a Larger Problem 16.4 Variable Selection Procedures Stepwise Regression Forward Selection Backward Elimination Best-Subsets Regression Making the Final Choice 16.5 Multiple Regression Approach to Experimental Design 16.6 Autocorrelation and the Durbin-Watson Test Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Analysis of PGA Tour Statistics Case Problem 2: Fuel Economy for Cars Appendix 16.1 Variable Selection Procedures with Minitab Appendix 16.2 Variable Selection Procedures with StatTools Chapter 17 Index Numbers Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics 17.1 Price Relatives 17.2 Aggregate Price Indexes 17.3 Computing an Aggregate Price Index from Price Relatives 17.4 Some Important Price Indexes Consumer Price Index Producer Price Index Dow Jones Averages 17.5 Deflating a Series by Price Indexes 17.6 Price Indexes: Other Considerations Selection of Items Selection of a Base Period Quality Changes 17.7 Quantity Indexes Summary Glossary Key Formulas Supplementary Exercises Chapter 18 Time Series Analysis and Forecasting Statistics in Practice: Nevada Occupational Health Clinic 18.1 Time Series Patterns Horizontal Pattern Trend Pattern Seasonal Pattern Trend and Seasonal Pattern Cyclical Pattern Selecting a Forecasting Method 18.2 Forecast Accuracy 18.3 Moving Averages and Exponential Smoothing Moving Averages Weighted Moving Averages Exponential Smoothing 18.4 Trend Projection Linear Trend Regression Holt’s Linear Exponential Smoothing Nonlinear Trend Regression 18.5 Seasonality and Trend Seasonality Without Trend Seasonality and Trend Models Based on Monthly Data 18.6 Time Series Decomposition Calculating the Seasonal Indexes Deseasonalizing the Time Series Using the Deseasonalized Time Series to Identify Trend Seasonal Adjustments Models Based on Monthly Data Cyclical Component Summary Glossary Key Formulas Supplementary Exercises Case Problem 1: Forecasting Food and Beverage Sales Case Problem 2: Forecasting Lost Sales Appendix 18.1 Forecasting with Minitab Appendix 18.2 Forecasting with Excel Appendix 18.3 Forecasting with StatTools Chapter 19 Nonparametric Methods Statistics in Practice: West Shell Realtors 19.1 Sign Test Hypothesis Test About a Population Median Hypothesis Test with Matched Samples 19.2 Wilcoxon Signed-Rank Test 19.3 Mann-Whitney-Wilcoxon Test 19.4 Kruskal-Wallis Test 19.5 Rank Correlation Summary Glossary Key Formulas Supplementary Exercises Appendix 19.1 Nonparametric Methods with Minitab Appendix 19.2 Nonparametric Methods with Excel Appendix 19.3 Nonparametric Methods with StatTools Chapter 20 Statistical Methods for Quality Control Statistics in Practice: Dow Chemical Company 20.1 Philosophies and Frameworks Malcolm Baldrige National Quality Award ISO 9000 Six Sigma 20.2 Statistical Process Control Control Charts x Chart: Process Mean and Standard Deviation Known x Chart: Process Mean and Standard Deviation Unknown R Chart p Chart np Chart Interpretation of Control Charts 20.3 Acceptance Sampling KALI, Inc.: An Example of Acceptance Sampling Computing the Probability of Accepting a Lot Selecting an Acceptance Sampling Plan Multiple Sampling Plans Summary Glossary Key Formulas Supplementary Exercises Appendix 20.1 Control Charts with Minitab Appendix 20.2 Control Charts with StatTools Chapter 21 Decision Analysis Statistics in Practice: Ohio Edison Company 21.1 Problem Formulation Payoff Tables Decision Trees 21.2 Decision Making with Probabilities Expected Value Approach Expected Value of Perfect Information 21.3 Decision Analysis with Sample Information Decision Tree Decision Strategy Expected Value of Sample Information 21.4 Computing Branch Probabilities Using Bayes’ Theorem Summary Glossary Key Formulas Supplementary Exercises Case Problem: Lawsuit Defense Strategy Appendix: An Introduction to PrecisionTree Appendix A: References and Bibliography Appendix B: Tables Appendix C: Summation Notation Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises Appendix E: Using Excel Functions Appendix F: Computing p-Values Using Minitab and Excel Index

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