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The emphasis of the text is on data analysis, modeling, and spreadsheet use in statistics and management science. This text contains professional Excel software add-ins. The authors maintain the elements that have made this text a market leader in its first edition: clarity of writing, a teach-by-example approach, and complete Excel integration.

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CONTENTS
Preface = xv
Chapter 1 : Introduction to Data Analysis and Decision Making = 1
 1.1 Introduction = 2
 1.2 An Overview of the Book = 4
 1.3 A Sampling of Examples = 10
 1.4 Modeling and Models = 21
 1.5 Conclusion = 26
 CASE 1.1 : Entertainment on a Cruise Ship = 27
Part 1 Getting, Describing, and Summarizing Data
 Chapter 2 : Describing Data : Graphs and Tables = 29
  2.1 Introduction = 31
  2.2 Basic Concepts = 32
  2.3 Frequency Tables and Histograms = 36
  2.4 Analyzing Relationships with Scatterplots = 47
  2.5 Time Series Plots = 51
  2.6 Exploring Data with Pivot Tables = 55
  2.7 Conclusion = 67
  CASE 2.1 : Customer Arrivals at Bank98 = 73
  CASE 2.2 : Automobile Production and Purchases = 73
  CASE 2.3 : Saving, Spending, and Social Climbing = 74
 Chapter 3 : Describing Data : Summary Measures = 75
  3.1 Introduction = 76
  3.2 Measures of Central Location = 78
  3.3 Quartiles and Percentiles = 80
  3.4 Minimum, Maximum, and Range = 81
  3.5 Measures of Variability : Variance and Standard Deviation = 82
  3.6 Obtaining Summary Measures with Add-Ins = 87
  3.7 Measures of Association : Covariance and Correlation = 91
  3.8 Describing Data Sets with Boxplots = 95
  3.9 Applying the Tools = 100
  3.10 Conclusion = 117
  CASE 3.1 : The Dow Jones Averages = 125
  CASE 3.2 : Other Market Indexes = 127
  CASE 3.3 : Correct Interpretation of Means = 128
 Chapter 4 : Getting the Right Data = 129
  4.1 Introduction = 130
  4.2 Sources of Data = 131
  4.3 Using Excel's AutoFilter = 134
  4.4 Complex Queries with the Advanced Filter = 140
  4.5 Importing External Data from Access = 146
  4.6 Creating Pivot Tables from External Data = 158
  4.7 Web Queries = 160
  4.8 Other Data Sources on the Web = 170
  4.9 Cleansing the Data = 176
  4.10 Conclusion = 183
  CASE 4.1 : EduToys, Inc. = 187
Part 2 Probability, Uncertainty, and Decision Making
 Chapter 5 : Probability and Probability Distributions = 189
  5.1 Introduction = 190
  5.2 Probability Essentials = 191
  5.3 Distribution of a Single Random Variable = 199
  5.4 An Introduction to Simulation = 203
  5.5 Distribution of Two Random Variables : Scenario Approach = 207
  5.6 Distribution of Two Random Variables : Joint Probability Approach = 213
  5.7 Independent Random Variables = 220
  5.8 Weighted Sums of Random Variables = 224
  5.9 Conclusion = 231
  CASE 5.1 : Simpson's Paradox = 238
 Chapter 6 : Normal, Binomial, Poisson, and Exponential Distributions = 239
  6.1 Introduction = 240
  6.2 The Normal Distribution = 241
  6.3 Applications of the Normal Distribution = 250
  6.4 The Binomial Distribution = 262
  6.5 Applications of the Binomial Distribution = 266
  6.6 The Poisson and Exponential Distributions = 278
  6.7 Fitting a Probability Distribution to Data : BestFit = 283
  6.8 Conclusion = 288
  CASE 6.1 : EuroWatch Company = 296
  CASE 6.2 : Cashing in on the Lottery = 297
 Chapter 7 : Decision Making Under Uncertainty = 299
  7.1 Introduction = 300
  7.2 Elements of a Decision Analysis = 302
  7.3 The PrecisionTree Add-In = 311
  7.4 More Single-Stage Examples = 320
  7.5 Multistage Decision Problems = 329
  7.6 Bayes' Rule = 337
  7.7 Incorporating Attitudes Toward Risk = 345
  7.8 Conclusion 354
  CASE 7.1 : Jogger Shoe Company = 363
  CASE 7.2 : Westhouser Paper Company = 364
Part 3 Statistical Inference
 Chapter 8 : Sampling and Sampling Distributions = 365
  8.1 Introduction = 366
  8.2 Sampling Terminology = 366
  8.3 Methods for Selecting Random Samples = 367
  8.4 An Introduction to Estimation = 384
  8.5 Conclusion = 404
  CASE 8.1 : Sampling from Videocassette Renters = 413
 Chapter 9 : Confidence Interval Estimation = 415
  9.1 Introduction = 416
  9.2 Sampling Distributions = 417
  9.3 Confidence Interval for a Mean = 423
  9.4 Confidence Interval for a Total = 429
  9.5 Confidence Interval for a Proportion = 431
  9.6 Confidence Interval for a Standard Deviation = 437
  9.7 Confidence Interval for the Difference Between Means = 440
  9.8 Confidence Interval for the Difference Between Proportions = 453
  9.9 Controlling Confidence Interval Length = 458
  9.10 Conclusion = 466
  CASE 9.1 : Harrigan University Admissions = 474
  CASE 9.2 : Employee Retention at D&Y = 475
  CASE 9.3 : Delivery Times at SnowPea Restaurant = 476
  CASE 9.4 : The Bodfish Lot Cruise = 477
 Chapter 10 : Hypothesis Testing = 479
  10.1 Introduction = 480
  10.2 Concepts in Hypothesis Testing = 481
  10.3 Hypothesis Tests for a Population Mean = 488
  10.4 Hypothesis Tests for Other Parameters = 495
  10.5 Tests for Normality = 516
  10.6 Chi-Square Test for Independence = 522
  10.7 One-Way ANOVA = 526
  10.8 Conclusion = 534
  CASE 10.1 : Regression Toward the Mean = 540
  CASE 10.2 : Baseball Statistics = 541
  CASE 10.3 : The Wichita Anti-Drunk Driving Advertising Campaign = 542
  CASE 10.4 : Deciding Whether to Switch to a New Toothpaste Dispenser = 544
Part 4 Regression, Forecasting, and Time Series
 Chapter 11 : Regression Analysis : Estimating Relationships = 547
  11.1 Introduction = 548
  11.2 Scatterplots : Graphing Relationships = 551
  11.3 Correlations : Indicators of Linear Relationships = 560
  11.4 Simple Linear Regression = 562
  11.5 Multiple Regression = 573
  11.6 Modeling Possibilities = 579
  11.7 Validation of the Fit = 606
  11.8 Conclusion = 608
  CASE 11.1 : Quantity Discounts at the FirmChair Company = 616
  CASE 11.2 : Housing Price Structure in MidCity = 616
  CASE 11.3 : Demand for French Bread at Howie's = 617
  CASE 11.4 : Investing for Retirement = 618
 Chapter 12 : Regression Analysis : Statistical Inference = 619
  12.1 Introduction = 620
  12.2 The Statistical Model = 621
  12.3 Inferences About the Regression Coefficients = 625
  12.4 Multicollinearity = 635
  12.5 Include／Exclude Decisions = 639
  12.6 Stepwise Regression = 644
  12.7 The Partial F Test = 648
  12.8 Outliers = 656
  12.9 Violations of Regression Assumptions = 662
  12.10 Prediction = 666
  12.11 Conclusion = 672
  CASE 12.1 : The Artsy Corporation = 683
  CASE 12.2 : Heating Oil at Dupree Fuels Company = 685
  CASE 12.3 : Developing a Flexible Budget at the Gunderson Plant = 686
  CASE 12.4 : Forecasting Overhead at Wagner Printers = 687
 Chapter 13 : Time Series Analysis and Forecasting = 689
  13.1 Introduction = 690
  13.2 Forecasting Methods : An Overview = 691
  13.3 Testing for Randomness = 698
  13.4 Regression-Based Trend Models = 705
  13.5 The Random Walk Model = 714
  13.6 Autoregression Models = 718
  13.7 Moving Averages = 723
  13.8 Exponential Smoothing = 729
  13.9 Seasonal Models = 739
  13.10 Conclusion = 754
  CASE 13.1 : Arrivals at the Credit Union = 759
  CASE 13.2 : Forecasting Weekly Sales at Amanta = 760
Part 5 Decision Modeling
 Chapter 14 : Introduction to Optimization Modeling = 761
  14.1 Introduction = 762
  14.2 A Brief History of Linear Programming = 762
  14.3 Introduction to LP Modeling = 763
  14.4 Sensitivity Analysis and the SolverTable Add-In = 773
  14.5 The Linear Assumptions = 778
  14.6 Graphical Solution Method = 780
  14.7 Infeasibility and Unboundedness = 784
  14.8 A Multiperiod Production Problem = 785
  14.9 A Decision Support System = 792
  14.10 Conclusion = 794
  CASE 14.1 : Shelby Shelving = 800
 Chapter 15 : Optimization Modeling : Applications = 803
  15.1 Introduction = 804
  15.2 Workforce Scheduling Models = 805
  15.3 Blending Models = 811
  15.4 Logistics Models = 817
  15.5 Aggregate Planning Models = 827
  15.6 Dynamic Financial Models = 835
  15.7 Integer Programming Models = 840
  15.8 Nonlinear Models = 855
  15.9 Conclusion = 864
  CASE 15.1 : Giant Motor Company = 870
  CASE 15.2 : GMS Stock Hedging = 872
  CASE 15.3 : Durham Asset Management = 874
 Chapter 16 : Simulation Models = 877
  16.1 Introduction = 878
  16.2 Random Numbers = 879
  16.3 Introduction to Spreadsheet Simulation = 881
  16.4 Selecting Probability Distributions = 889
  16.5 Simulating with @Risk = 896
  16.6 Financial Planning Models = 912
  16.7 Cash Balance Models = 918
  16.8 Simulating Stock Prices and Options = 923
  16.9 Market Share Models = 936
  16.10 Simulating Correlated Values = 942
  16.11 Using TopRank with @Risk for Powerful Modeling = 948
  16.12 Conclusion = 957
  CASE 16.1 : Ski Jacket Production = 966
  CASK 16.2 : The College Fund Investment Decision = 967
  CASE 16.3 : Ebony Bath Soap = 968
  CASE 16.4 : Bond Investment Strategy = 969
References = 971
Appendix A : Statistical Reporting = 975
 A.1 Introduction = 975
 A.2 Suggestions for Good Statistical Reporting = 976
 A.3 Examples of Statistical Reports = 981
 A.4 Conclusion = 992
Index = 993