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Business AnalyticsMethods, Models, and DecisionsJames R. Evans : University of CincinnatiPEARSONBoston Columbus Indianapolis New York San Francisco Upper Saddle RiverAmsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal TorontoDelhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo

ContentsPreface xiiiAbout the AuthorxixPart 1: Foundations of Business AnalyticsChapter 1: introduction to Business Analytics 1Learning Objectives 1What Is Business Analytics? 3Evolution of Business Analytics 5Scope of Business Analytics 6Data for Business Analytics 8Data Sets and Databases 10 Metrics and Data Classification 10 Data Reliability and Validity 13Decision Models 13Descriptive Decision Models 15 Predictive Decision Models 19 Prescriptive Decision Models 21Problem Solving and Decision Making 22Recognizing a Problem 22 Defining the Problem 23 »Structuring the Problem 23 Analyzing the Problem 23 InterpretingResults and Making a Decision 23 Implementing the Solution 24Key Terms 25 Fun with Analytics 25 Problems andExercises 25 Case: Performance Lawn Equipment 28Chapter 2: Analytics on Spreadsheets 31Learning Objectives 31Basic Excel Skills 33Excel Formulas 34 Copying Formulas 35 Other Useful Excel Tips 36Excel Functions 37Basic Excel Functions 37 Functions for Specific Applications 38 InsertFunction 39 » Logical Functions 39 Lookup Functions 41Spreadsheet Add-Ins for Business Analytics 43Spreadsheet Modeling and Spreadsheet Engineering 44Spreadsheet Quality 46Key Terms 49 Problems and Exercises 49 Case: Performance LawJTEquipment 51Part 2: Descriptive AnalyticsChapter 3: Visualizing and Exploring Data 53Learning Objectives 53Data Visualization 54Creating Charts in Microsoft Excel 2010Charts 59 Geographic Data 6054» Miscellaneous Excel

VIContentsData Queries: Using Sorting and Filtering 60Sorting Data in Excel 61 Pareto Analysis 61 Filtering Data 62Statistical Methods for Summarizing Data 65Frequency Distributions for Categorical Data 66 Relative FrequencyDistributions 68 Frequency Distributions for Numerical Data 68 Excel Histogram Tool 69 Cumulative Relative FrequencyDistributions 72 Percentiles and Quartiles 73 Cross-TabulationsExploring Data Using PivotTables 77PivotCharts 81Key Terms 81 Problems and ExercisesEquipment 8482 75Case: Performance LawnChapter 4: Descriptive Statistical Measures 85Learning Objectives 85Populations and Samples 86Understanding Statistical Notation 86Measures of Location 87Arithmetic Mean 87 Median 88 Mode 89 Midrange 90 Using Measures of Location in Business Decisions 90Measures of Dispersion 91Range 91 Interquartile Range 92 Variance 92 «Standard Deviation 93 Chebyshev's Theorem and the Empirical Rules 94Standardized Values 98 Coefficient of Variation 99Measures of Shape 99Excel Descriptive Statistics Tool 102Descriptive Statistics for Grouped Data 103Descriptive Statistics for Categorical Data: The Proportion 106Statistics in PivotTables 106Measures of Association 106Co variance 108 - Correlation 109 Excel Correlation Tool 112Outliers 113Statistical Thinking in Business Decisions 115Variability in Samples 116Key Terms 119 Problems and Exercises 119 Case: Performance LawnEquipment 124Chapter 5: Probability Distributions and Data Modeling 125Learning Objectives 125Basic Concepts of Probability 126Probability Rules and Formulas 128 Conditional Probability 129Random Variables and Probability Distributions 132—Discrete Probability Distributions 135Expected Value of a Discrete Random Variable 136 Using Expected'Value in Making Decisions 137 Variance of a Discrete RandomVariable 139 Bernoulli Distribution 139 Binomial Distribution 140Poisson Distribution 142Continuous Probability Distributions 143Properties of Probability Density Functions 145 Uniform Distribution 146Normal Distribution 148 The NORM.INV Function 150 Standard

ContentsviiNormal Distribution 150 Using Standard Normal Distribution Tables 152 Exponential Distribution 152 Other Useful Distributions 154 Continuous Distributions 154Random Sampling from Probability Distributions 155Sampling from Discrete Probability Distributions 156 Sampling fromCommon Probability Distributions 157 "' Risk Solver Platform DistributionFunctions 160Data Modeling and Distribution Fitting 162Goodness of Fit 164 p Distribution Fitting with Risk Solver Platform 164Key TermsEquipment166 Problems and Exercises174167 Case: Performance LawnChapter 6: Sampling and Estimation 175Learning Objectives 175Statistical Sampling 176Sampling Methods 176Estimating Population Parameters 180Unbiased Estimators 180 Errors in Point Estimation 181Sampling Error 181Understanding Sampling Error 181Sampling Distributions 183Sampling Distribution of the Mean 183 Applying the Sampling Distributionof the Mean 184Interval Estimates 185Confidence Intervals 186Confidence Interval for the Mean with Known Population StandardDeviation 186 The f-Distribution 188 Confidence Intervalfor the Mean with Unknown Population Standard Deviation 189 Confidence Interval for a Proportion 189 Additional Types of ConfidenceIntervals 190Using Confidence Intervals for Decision Making 192Prediction Intervals 192Confidence Intervals and Sample Size 193Key TermsEquipment195 Problems and Exercises198195 Case: Performance LawnChapter 7: Statistical Inference 199Learning Objectives 199Hypothesis Testing 200Hypothesis-Testing Procedure 201One-Sample Hypothesis Tests 201Understanding Risk in Hypothesis Testing 202 Selecting the TestStatistic 203 Drawing a Conclusion 204 p-Values 206 Two-Tailed Test of Hypothesis for the Mean 206 One-Sample Testsfor Proportions 207Two-Sample Hypothesis Tests 208Two-Sample Tests for Differences in Means 209 ' Two-Sample Test'forMeans with Paired Samples 212 Test for Equality of Variances 214

VIIIContentsAnalysis of Variance 215Assumptions of ANOVA 216Chi-Square Test for Independence 217Key Terms 221 Problems and ExercisesEquipment 225221 Case: Performance LawnPart 3: Predictive AnalyticsChapter 8: Predictive Modeling and Analysis 226Learning Objectives 226Logic-Driven Modeling 227Strategies for Building Predictive Models 227 » Data and Models 229 Models Involving Multiple Time Periods 231 Single-Period PurchaseDecisions 231 Overbooking Decisions 233 Model Assumptions,Complexity, and Realism 234Data-Driven Modeling 236Retail Pricing Markdowns 238 Modeling Relationships and Trendsin Data 238Analyzing Uncertainty and Model Assumptions 243What-If Analysis 244 Data Tables 244 Scenario Manager 248 Goal Seek 251Model Analysis Using Risk Solver Platform 251Parametric Sensitivity Analysis 251 Tornado Charts 255Key Terms 256 Problems and ExercisesEquipment 260256 Case: Performance LawnChapter 9: Regression Analysis 261Learning Objectives 261Simple Linear Regression 262Finding the Best-Fitting Regression Line 263 Least-Squares Regression 265Simple Linear Regression with Excel 267 Regression as Analysis ofVariance 269 Testing Hypotheses for Regression Coefficients 270 Confidence Intervals for Regression Coefficients 271Residual Analysis and Regression Assumptions 271Checking Assumptions 272Multiple Linear Regression 274Building Good Regression Models 279Correlation and Multicollinearity 281Regression with Categorical Independent Variables 283Categorical Variables with More Than Two Levels 285Regression Models with Nonlinear Terms 287Key Terms 291 Problems and ExercisesEquipment 295291 Case: Performance LawnChapter 10: Forecasting Techniques 297Learning Objectives 297Qualitative and Judgmental Forecasting 298Historical Analogy 298 The Delphi MethodIndexes 299299 Indicators and

ContentsixStatistical Forecasting Models 300Forecasting Models for Stationary Time Series 302 Moving AverageModels 302 Error Metrics and Forecast Accuracy 307 ExponentialSmoothing Models 308Forecasting Models for Time Series with a Linear Trend 311Double Exponential Smoothing 313 Regression-Based Forecasting forTime Series with a Linear Trend 313Forecasting Time Series with Seasonality 315Regression-Based Seasonal Forecasting Models 316 Holt-Winters Forecastingfor Seasonal Time Series 317 Holt-Winters Models for Forecasting Time Serieswith Seasonality and Trend 318Selecting Appropriate Time-Series-Based Forecasting Models 322Regression Forecasting with Causal Variables 323The Practice of Forecasting 325Key Terms 326 Problems and Exercises 326 Case: Performance LawnEquipment 328Chapter 11: Simulation and Risk Analysis 329Learning Objectives 329Spreadsheet Models with Random Variables 331Monte Carlo Simulation 331Monte Carlo Simulation Using Risk Solver Platform 333Defining Uncertain Model Inputs 334 Defining Output Cells 335 Running a Simulation 336 Analyzing Results 338New-Product Development Model 339Confidence Interval for the Mean 343 Sensitivity Chart 343 OverlayCharts 344 Trend Charts 346 Box-Whisker Charts 346 Simulation Reports 347Newsvendor Model 347The Flaw of Averages 348 Monte Carlo Simulation Using HistoricalData 348 Monte Carlo Simulation Using a Fitted Distribution 350Overbooking Model 351The Custom Distribution in Risk Solver Platform 351Cash Budget Model 352Correlating Uncertain Variables 356Key Terms 360Equipment 365 Problems and Exercises360 Case: Performance LawnChapter 12: Introduction to Data Mining 366Learning Objectives 366The Scope of Data Mining 368Data Exploration and Reduction 369Cluster Analysis 369Classification 372An Intuitive Explanation of Classification 376 Measuring ClassificationPerformance 378 « Using Training and Validation Data 378 ClassifyingNew Data 380Classification Techniques 381 -Nearest Neighbors (&-NN) 381 Discriminant Analysis 385 Logistic Regression 389

ContentsAssociation Rule Mining 393Cause-and-Effect Modeling 397Key Terms 401Equipment 402 Problems and Exercises401 Case: Performance LawnPart 4: Prescriptive AnalyticsChapter 13: Linear Optimization 404Learning Objectives 404Building Linear Optimization Models 405Identifying Elements for an Optimization Model 405 TranslatingModel Information into Mathematical Expressions 406 More aboutConstraints 408 Characteristics of Linear Optimization Models 409Implementing Linear Optimization Models on Spreadsheets 409Excel Functions to Avoid in Linear Optimization 411Solving Linear Optimization Models 411Using the Standard Solver 412 Using Premium Solver 415 Solver Answer Report 416Graphical Interpretation of Linear Optimization 418How Solver Works 423How Solver Creates Names in Reports 425 Difficulties with Solver 425Solver Outcomes and Solution Messages 425Unique Optimal Solution 426 « Alternative Optimal Solutions 426 Unbounded Solution 427 » Infeasible Problem 428Using Optimization Models for Prediction and Insight 429Solver Sensitivity Report 431 Using the Sensitivity Report 434 Parameter Analysis in Risk Solver Platform 436Key Terms 439, Problems and ExercisesEquipment 445439 Case: Performance LawnChapter 14: Applications of Linear Optimization 446Learning Objectives 446Types of Constraints in Optimization Models 448Process Selection Models 449Spreadsheet Design and Solver Reports 450Blending Models 453Dealing with Infeasibility 454Portfolio Investment Models 457Evaluating Risk Versus Reward 458Transportation Models 461Formatting the Sensitivity Report 463 DegeneracyMultiperiod Production Planning Models 465Building Alternative Models 468Multiperiod Financial Planning Models 468Models with Bounded Variables 473Auxiliary Variables for Bound Constraints 478A Production/Marketing Allocation Model 478Using Sensitivity Information Correctly 480465