Brief Contents 1 Introduction to Data A n a l y s i s and D e c i s i o n Making I Part 1 Getting, Describing, and Summarizing Data 2 D e s c r i b i n g Data: Graphs and Tables 31 3 Describing Data: Summary M e a s u r e s 79 4 G e t t i n g the Right Data 135 Part 2 Probability, Uncertainty, and Decision Making 5 Probability and Probability Distributions 6 195 Normal, Binomial, P o i s s o n , and Exponential Distributions 7 Decision Making under U n c e r t a i n t y 305 Part 3 Statistical Inference 8 Sampling and Sampling Distributions 9 C o n f i d e n c e Interval Estimation 10 Hypothesis Testing 377 421 487 Part 4 Regression, Forecasting, and Time Series 11 Regression Analysis: Estimating Relationships 12 R e g r e s s i o n Analysis: Statistical Inference 13 Time Series Analysis and Forecasting 633 703 Part 5 Decision Modeling 14 Introduction to Optimization Modeling 779 15 Optimization Modeling: A p p l i c a t i o n s 837 16 Introduction to Simulation Modeling 935 17 Simulation M o d e l s 999 Appendix A Statistical Reporting References Index 1077 1074 1055 561 245 Contents Introduction to Data Analysis and Decision Making 1.1 Introduction 2 1.2 An Overview of the Book 4 1.3 A Sampling of Examples 11 1.4 Modeling and Models 1.5 Conclusion 21 26 CASE I.I Entertainment on a Cruise Ship RT I 27 GETTING, DESCRIBING, AND SUMMARIZING DATA 29 2 Describing Data: Graphs and Tables 31 2.1 Introduction 33 2.2 Basic Concepts 33 2.3 Frequency Tables and Histograms 38 2.4 Analyzing Relationships with Scatterplots 2.5 Time Series Graphs 52 2.6 Exploring Data with Pivot Tables 2.7 Conclusion 57 68 CASE 2.1 Customer Arrivals at Banl<98 3 48 75 CASE 2.2 Automobile Production and Purchases 76 CASE 2.3 Saving, Spending, and Social Climbing 77 Describing Data: Summary Measures 3.1 Introduction 79 81 3.2 Measures of Central Location 3.3 Quartiles and Percentiles 82 85 3.4 Minimum, Maximum, and Range 85 3.5 Measures of Variability: Variance and Standard Deviation 3.6 Obtaining Summary Measures with Add-lns 91 3.7 Measures of Association: Covariance and Correlation 3.8 Describing Data Sets with Boxplots 3.9 Applying the Tools 3.10 Conclusion 99 104 124 CASE 3.1 The Dow Jones Averages CASE 3.2 Other Market Indexes 131 133 CASE 3.3 Correct Interpretation of Means 134 95 Getting the Right Data 4.1 Introduction 135 136 4.2 Sources of Data 137 4.3 Using Excels AutoFilter 140 4.4 Complex Queries with the Advanced Filter 4.5 Importing External Data from Access 146 152 4.6 Creating Pivot Tables from External Data 4.7 Web Queries 165 4.8 Other Data Sources on the Web 4.9 Cleansing the Data 4.10 Conclusion 173 179 186 CASE 4.1 EduToys, Inc. PART 2 163 191 PROBABILITY, UNCERTAINTY, AND DECISION MAKING 5 Probability and Probability Distributions 5.1 Introduction 193 195 196 5.2 Probability Essentials 197 5.3 Distribution of a Single Random Variable 5.4 An Introduction to Simulation 204 209 5.5 Distribution of Two Random Variables: Scenario Approach 213 5.6 Distribution of Two Random Variables: Joint Probability Approach 219 5.7 Independent Random Variables 225 5.8 Weighted Sums of Random Variables 5.9 Conclusion 236 CASE 5.1 Simpson's Paradox 6 229 243 Normal, Binomial, Poisson, and Exponential Distributions 6.1 Introduction 247 6.2 The Normal Distribution 247 6.3 Applications of the Normal Distribution 6.4 The Binomial Distribution 256 268 6.5 Applications of the Binomial Distribution 6.6 The Poisson and Exponential Distributions 273 284 6.7 Fitting a Probability Distribution to Data: BestFit 6.8 Conclusion 245 289 294 CASE 6.1 EuroWatch Company 301 CASE 6.2 Cashing in on the Lottery 302 VII Decision Making under Uncertainty 7.1 Introduction 305 306 7.2 Elements of a Decision Analysis 7.3 The PrecisionTree Add-In 7.4 Bayes'Rule 307 320 332 7.5 Multistage Decision Problems 337 7.6 Incorporating Attitudes Toward Risk 7.7 Conclusion 352 358 CASE 7.1 Jogger Shoe Company 370 CASE 7.2 Westhouser Paper Company CASE 7,3 Biotechnital Engineering PART 3 STATISTICAL INFERENCE 8 371 372 375 Sampling and Sampling Distributions 8.1 Introduction 377 378 8.2 Sampling Terminology 378 8.3 Methods for Selecting Random Samples 8.4 An Introduction to Estimation 8.5 Conclusion 379 393 411 CASE 8.1 Sampling from Videocassette Renters 9 Confidence Interval Estimation 9.1 Introduction 419 421 422 9.2 Sampling Distributions 423 9.3 Confidence Interval for a Mean 429 9.4 Confidence Interval for a Total 435 9.5 Confidence Interval for a Proportion 438 9.6 Confidence Interval for a Standard Deviation 443 9.7 Confidence Interval for the Difference Between Means 446 9.8 Confidence Interval for the Difference Between Proportions 9.9 Controlling Confidence Interval Length 9.10 Conclusion 467 475 CASE 9.1 Harrigan University Admissions CASE 9.2 Employee Retention at D&Y 482 483 CASE 9.3 Delivery Times at SnowPea Restaurant CASE 9.4 The Bodfish Lot Cruise 485 10 Hypothesis Testing 487 I O.I Introduction 488 10.2 Concepts in Hypothesis Testing VIII 489 484 461 10.3 Hypothesis Tests for a Population Mean 496 10.4 Hypothesis Tests for Other Parameters 10.5 Tests for Normality 525 10.6 Chi-Square Test for Independence 10.7 One-Way ANOVA 10.8 Conclusion 503 531 537 544 CASE 10.1 Regression Toward the Mean CASE 10.2 Baseball Statistics 551 552 CASE 10.3 The Wichita Anti-Drunk Driving Advertising Campaign 553 CASE 10.4 Deciding Whether to Switch to a New Toothpaste Dispenser CASE 10.5 Removing Vioxx from the Market PART 4 558 REGRESSION, FORECASTING, AND TIME SERIES 1 1 Regression Analysis: Estimating Relationships 1 1 . 1 Introduction 561 562 11.2 Scatterplots: Graphing Relationships 565 11.3 Correlations: Indicators of Linear Relationships 11.4 Simple Linear Regression 11.5 Multiple Regression 573 575 586 11.6 Modeling Possibilities 592 11.7 Validation of the Fit 618 11.8 Conclusion 620 CASE I I. I Quantity Discounts at the FirmChair Company CASE I 1.2 Housing Price Structure in MidCity CASE 11.3 Demand for French Bread at Howie's CASE I 1.4 Investing for Retirement 12 Regression Analysis: Statistical Inference 12.1 Introduction 629 630 631 633 635 12.2 The Statistical Model 635 12.3 Inferences About the Regression Coefficients 12.4 Multicollinearity 649 12.5 Include/Exclude Decisions 12.6 Stepwise Regression 12.7 The Partial F Test 12.8 Outliers 559 652 657 662 670 12.9 Violations of Regression Assumptions 12.10 Prediction 681 12.11 Conclusion 686 676 639 628 555 CASE 12.1 The Artsy Corporation 697 CASE 12.2 Heating Oil at Dupree Fuels Company 699 CASE 12.3 Developing a Flexible Budget at the Gunderson Plant CASE 12.4 Forecasting Overhead at Wagner Printers 701 13 Time Series Analysis and Forecasting 703 13.1 Introduction 704 13.2 Forecasting Methods: An Overview 13.3 Testing for Randomness 711 13.4 Regression-Based Trend Models 13.5 The Random Walk Model 13.6 Autoregression Models 13.7 Moving Averages 719 727 731 736 13.8 Exponential Smoothing 13.9 Seasonal Models 13.10 Conclusion 705 742 753 768 CASE 13.1 Arrivals at the Credit Union 774 CASE 13.2 Forecasting Weekly Sales at Amanta PART 5 775 DECISION MODELING 777 14 Introduction to Optimization Modeling 779 14.1 Introduction 780 14.2 Introduction to Optimization 14.3 A Two-Variable Model 14.4 Sensitivity Analysis 780 782 793 14.5 Properties of Linear Models 800 14.6 Infeasibility and Unboundedness 14.7 A Product Mix Model 803 805 14.8 A Multiperiod Production Model 814 14.9 A Comparison of Algebraic and Spreadsheet Models 14.10 A Decision Support System 1 4 . 1 1 Conclusion 824 826 Appendix Information on Solvers CASE 14.1 Shelby Shelving 832 833 CASE 14.2 Sonoma Valley Wines 835 15 Optimization Modeling: Applications 837 15.1 Introduction 838 15.2 Workforce Scheduling Models 839 823 700 15.3 Blending Models 846 15.4 Logistics Models 852 15.5 Aggregate Planning Models 15.6 Financial Models 870 879 15.7 Integer Programming Models 15.8 Nonlinear Models 15.9 Conclusion 889 908 921 CASE 15.1 Giant Motor Company CASE I5.2GMS Stock Hedging 928 930 CASE 15.3 Durham Asset Management 932 16 Introduction to Simulation Modeling 16.1 Introduction 935 936 16.2 Real Applications of Simulation 937 16.3 Probability Distributions for Input Variables 16.4 Simulation with Built-in Excel Tools 16.5 Introduction to @RISK 938 954 966 16.6 The Effects of Input Distributions on Results 16.7 Conclusion 989 CASE 16.1 Ski Jacket Production CASE 16.2 Ebony Bath Soap 17 Simulation Models 17.1 981 996 997 999 Introduction 17.2 Operations Models 17.3 Financial Models 17.4 Marketing Models 1014 1028 17.5 Simulating Games of Chance 17.6 Conclusion 1041 1047 CASE 17.1 College Fund Investment 1053 CASE 17.2 Bond Investment Strategy Appendix A Statistical Reporting A.I Introduction 1054 1055 1055 A.2 Suggestions for Good Statistical Reporting A.3 Examples of Statistical Reports A.4 Conclusion References Index 1056 1061 1073 1074 1077 XI