Business statistics

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Cover Title Page Copyright Dedication Meet the Authors Contents Preface Index of Applications Part I: Exploring and Collecting Data Chapter 1: Data and Decisions (H&M) 1.1 Data 1.2 The Role of Data in Decision Making 1.3 Variable Types 1.4 Data Sources: Where, How, and When Ethics in Action Chapter 1: From Learning to Earning Tech Support: Entering Data Brief Case: Credit Card Bank Chapter 2: Visualizing and Describing Categorical Data (Dalia Research) 2.1 Summarizing a Categorical Variable 2.2 Displaying a Categorical Variable 2.3 Exploring Relationships Between Two Categorical Variables: Contingency Tables 2.4 Segmented Bar Charts and Mosaic Plots 2.5 Three Categorical Variables 2.6 Simpson’s Paradox Ethics in Action Chapter 2: From Learning to Earning Tech Support: Displaying Categorical Data Brief Case: Credit Card Bank Chapter 3: Describing, Displaying, and Visualizing Quantitative Data (AIG) 3.1 Visualizing Quantitative Variables 3.2 Shape 3.3 Center 3.4 Spread of the Distribution 3.5 Shape, Center, and Spread—A Summary 3.6 Standardizing Variables 3.7 Five-Number Summary and Boxplots 3.8 Comparing Groups 3.9 Identifying Outliers 3.10 Time Series Plots *3.11 Transforming Skewed Data Ethics in Action Chapter 3: From Learning to Earning Tech Support: Displaying and Summarizing Quantitative Variables Brief Case: Detecting the Housing Bubble Chapter 4: Correlation and Linear Regression (Zillow.com) 4.1 Looking at Scatterplots 4.2 Assigning Roles to Variables in Scatterplots 4.3 Understanding Correlation 4.4 Lurking Variables and Causation 4.5 The Linear Model 4.6 Correlation and the Line 4.7 Regression to the Mean 4.8 Checking the Model 4.9 Variation in the Model and R2 4.10 Reality Check: Is the Regression Reasonable? 4.11 Nonlinear Relationships *4.12 Multiple Regression—A Glimpse Ahead Ethics in Action Chapter 4: From Learning to Earning Tech Support: Correlation and Regression Brief Case: Fuel Efficiency, Cost of Living, and Mutual Funds Case Study: Paralyzed Veterans of America Part II: Modeling with Probability Chapter 5: Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax) 5.1 Random Phenomena and Probability 5.2 The Nonexistent Law of Averages 5.3 Different Types of Probability 5.4 Probability Rules 5.5 Joint Probability and Contingency Tables 5.6 Conditional Probability and the General Multiplication Rule 5.7 Constructing Contingency Tables 5.8 Probability Trees *5.9 Reversing the Conditioning: Bayes’ Rule Ethics in Action Chapter 5: From Learning to Earning Tech Support: Generating Random Numbers Brief Case: Global Markets Chapter 6: Random Variables and Probability Models (Metropolitan Life Insurance Company) 6.1 Expected Value of a Random Variable 6.2 Standard Deviation of a Random Variable 6.3 Properties of Expected Values and Variances 6.4 Bernoulli Trials 6.5 Discrete Probability Models Ethics in Action Chapter 6: From Learning to Earning Tech Support: Random Variables and Probability Models Brief Case: Investment Options Chapter 7: The Normal and Other Continuous Distributions (The NYSE) 7.1 The Standard Deviation as a Ruler 7.2 The Normal Distribution 7.3 Normal Probability Plots 7.4 The Distribution of Sums of Normals 7.5 The Normal Approximation for the Binomial 7.6 Other Continuous Random Variables Ethics in Action Chapter 7: From Learning to Earning Tech Support: Probability Calculations and Plots Brief Case: Price/Earnings and Stock Value Part III: Gathering Data Chapter 8: Data Sources: Observational Studies and Surveys (Roper Polls) 8.1 Observational Studies and Found Data 8.2 Sample Surveys 8.3 Populations and Parameters 8.4 Common Sampling Designs 8.5 The Valid Survey 8.6 How to Sample Badly Ethics in Action Chapter 8: From Learning to Earning Tech Support Brief Case: Market Survey Research and The GfK Roper Reports Worldwide Survey Chapter 9: Data Sources: Experiments (Capital One) 9.1 Randomized, Comparative Experiments 9.2 The Four Principles of Experimental Design 9.3 Experimental Designs 9.4 Issues in Experimental Design 9.5 Displaying Data from Designed Experiments Ethics in Action Chapter 9: From Learning to Earning Brief Case: Design a Multifactor Experiment Part IV: Inference for Decision Making Chapter 10: Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story) 10.1 The Distribution of Sample Proportions 10.2 A Confidence Interval for a Proportion 10.3 Margin of Error: Certainty vs. Precision 10.4 Choosing the Sample Size Ethics in Action Chapter 10: From Learning to Earning Tech Support: Confidence Intervals for Proportions Brief Case: Has Gold Lost its Luster? and Forecasting Demand Case Study: Real Estate Simulation Chapter 11: Confidence Intervals for Means (Guinness & Co.) 11.1 The Central Limit Theorem 11.2 The Sampling Distribution of the Mean 11.3 How Sampling Distribution Models Work 11.4 Gosset and the t-Distribution 11.5 A Confidence Interval for Means 11.6 Assumptions and Conditions 11.7 Visualizing Confidence Intervals for the Mean Ethics in Action Chapter 11: From Learning to Earning Tech Support: Confidence Intervals for Means Brief Case: Real Estate and Donor Profiles Chapter 12: Testing Hypotheses (Casting Ingots) 12.1 Hypotheses 12.2 P-Values 12.3 The Reasoning of Hypothesis Testing 12.4 A Hypothesis Test for the Mean 12.5 Intervals and Tests 12.6 P-Values and Decisions: What to Tell About a Hypothesis Test Ethics in Action Chapter 12: From Learning to Earning Tech Support: Hypothesis Tests Brief Case: Real Estate and Donor Profiles Chapter 13: More About Tests and Intervals (Traveler’s Insurance) 13.1 How to Think About P-Values 13.2 Alpha Levels and Significance 13.3 Critical Values 13.4 Confidence Intervals and Hypothesis Tests 13.5 Two Types of Errors 13.6 Power Ethics in Action Chapter 13: From Learning to Earning Brief Case: Confidence Intervals and Hypothesis Tests Chapter 14: Comparing Two Means (Visa Global Organization) 14.1 Comparing Two Means 14.2 The Two-Sample t-Test 14.3 Assumptions and Conditions 14.4 A Confidence Interval for the Difference Between Two Means 14.5 The Pooled t-Test 14.6 Paired Data 14.7 Paired t-Methods Ethics in Action Chapter 14: From Learning to Earning Tech Support: Comparing Two Groups Brief Case: Real Estate and Consumer Spending Patterns (Data Analysis) Chapter 15: Inference for Counts: Chi-Square Tests (SAC Capital) 15.1 Goodness-of-Fit Tests 15.2 Interpreting Chi-Square Values 15.3 Examining the Residuals 15.4 The Chi-Square Test of Homogeneity 15.5 Comparing Two Proportions 15.6 Chi-Square Test of Independence Ethics in Action Chapter 15: From Learning to Earning Tech Support: Chi-Square Brief Case: Health Insurance and Loyalty Program Case Study: Investment Strategy Segmentation Part V: Models for Decision Making Chapter 16: Inference for Regression (Nambé Mills) 16.1 A Hypothesis Test and Confidence Interval for the Slope 16.2 Assumptions and Conditions 16.3 Standard Errors for Predicted Values 16.4 Using Confidence and Prediction Intervals Ethics in Action Chapter 16: From Learning to Earning Tech Support: Regression Analysis Brief Case: Frozen Pizza and Global Warming? Chapter 17: Understanding Residuals (Kellogg’s) 17.1 Examining Residuals for Groups 17.2 Extrapolation and Prediction 17.3 Unusual and Extraordinary Observations 17.4 Working with Summary Values 17.5 Autocorrelation 17.6 Transforming (Re-expressing) Data 17.7 The Ladder of Powers Ethics in Action Chapter 17: From Learning to Earning Tech Support: Examining Residuals Brief Case: Gross Domestic Product and Energy Sources Chapter 18: Multiple Regression (Zillow.com) 18.1 The Multiple Regression Model 18.2 Interpreting Multiple Regression Coefficients 18.3 Assumptions and Conditions for the Multiple Regression Model 18.4 Testing the Multiple Regression Model 18.5 Adjusted R2 and the F-statistic *18.6 The Logistic Regression Model Ethics in Action Chapter 18: From Learning to Earning Tech Support: Regression Analysis Brief Case: Golf Success Chapter 19: Building Multiple Regression Models (Bolliger and Mabillard) 19.1 Indicator (or Dummy) Variables 19.2 Adjusting for Different Slopes—Interaction Terms 19.3 Multiple Regression Diagnostics 19.4 Building Regression Models 19.5 Collinearity 19.6 Quadratic Terms Ethics in Action Chapter 19: From Learning to Earning Tech Support: Building Multiple Regression Models Brief Case: Building Models Chapter 20: Time Series Analysis (Whole Foods Market®) 20.1 What Is a Time Series? 20.2 Components of a Time Series 20.3 Smoothing Methods 20.4 Summarizing Forecast Error 20.5 Autoregressive Models 20.6 Multiple Regression–Based Models 20.7 Choosing a Time Series Forecasting Method 20.8 Interpreting Time Series Models: The Whole Foods Data Revisited Ethics in Action Chapter 20: From Learning to Earning Tech Support: Time Series Brief Case: U.S. Trade with the European Union Case Study: Health Care Costs Part VI: Analytics Chapter 21: Introduction to Big Data and Data Mining (Paralyzed Veterans of America) 21.1 Data Mining and the Big Data Revolution 21.2 The Data Mining Process 21.3 Data Mining Algorithms: A Sample 21.4 Models Built from Combining Other Models 21.5 Comparing Models 21.6 Summary Ethics in Action Chapter 21: From Learning to Earning Part VII: Online Topics Chapter 22: Quality Control (Sony) 22.1 A Short History of Quality Control 22.2 Control Charts for Individual Observations (Run Charts) 22.3 Control Charts for Measurements: x̅ and R Charts 22.4 Actions for Out-of-Control Processes 22.5 Control Charts for Attributes: p Charts and c Charts 22.6 Philosophies of Quality Control Ethics in Action Chapter 22: From Learning to Earning Tech Support: Quality Control Charts Brief Case: Laptop Touchpad Quality Chapter 23: Nonparametric Methods (i4cp) 23.1 Ranks 23.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic 23.3 Kruskal-Wallis Test 23.4 Paired Data: The Wilcoxon Signed-Rank Test *23.5 Friedman Test for a Randomized Block Design 23.6 Kendall’s Tau: Measuring Monotonicity 23.7 Spearman’s Rho 23.8 When Should You Use Nonparametric Methods? Ethics in Action Chapter 23: From Learning to Earning Tech Support: Nonparametric Methods Brief Case: Real Estate Reconsidered Chapter 24: Decision Making and Risk (Data Description, Inc.) 24.1 Actions, States of Nature, and Outcomes 24.2 Payoff Tables and Decision Trees 24.3 Minimizing Loss and Maximizing Gain 24.4 The Expected Value of an Action 24.5 Expected Value with Perfect Information 24.6 Decisions Made with Sample Information 24.7 Estimating Variation 24.8 Sensitivity 24.9 Simulation 24.10 More Complex Decisions Ethics in Action Chapter 24: From Learning to Earning Brief Case: Texaco-Pennzoil and Insurance Services, Revisited Chapter 25: Analysis of Experiments and Observational Studies 25.1 Analyzing a Design in One Factor—The One-Way Analysis of Variance 25.2 Assumptions and Conditions for ANOVA *25.3 Multiple Comparisons 25.4 ANOVA on Observational Data 25.5 Analysis of Multifactor Designs Chapter 25: From Learning to Earning Tech Support: Analysis of Variance Brief Case: Analyze your Multifactor Experiment Appendixes Appendix A: Answers Appendix B: Tables and Selected Formulas Appendix C: Photo Acknowledgments Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z