# Business Analytics: Data Analysis and Decision Making, 7th Edition PDF by S Christian Albright and Wayne L Winston

## Business Analytics: Data Analysis and Decision Making, Seventh Edition

By S. Christian Albright and Wayne L. Winston

Contents:

Preface xvi

1 Introduction to Business Analytics 1

1-1 Introduction 3

1-2 Overview of the Book 4

1-2a The Methods 4

1-2b The Software 6

1-3 Introduction to Spreadsheet Modeling 8

1-3a Basic Spreadsheet Modeling: Concepts and Best Practices 9

1-3b Cost Projections 12

1-3c Breakeven Analysis 15

1-3d Ordering with Quantity Discounts and Demand Uncertainty 20

1-3e Estimating the Relationship between Price and Demand 24

1-3f Decisions Involving the Time Value of Money 29

1-4 Conclusion 33

PART 1 Data Analysis 37

2 Describing the Distribution of a Variable 38

2-1 Introduction 39

2-2 Basic Concepts 41

2-2a Populations and Samples 41

2-2b Data Sets, Variables, and Observations 41

2-2c Data Types 42

2-3 Summarizing Categorical Variables 45

2-4 Summarizing Numeric Variables 49

2-4a Numeric Summary Measures 49

2-4b Charts for Numeric Variables 57

2-5 Time Series Data 62

2-6 Outliers and Missing Values 69

2-7 Excel Tables for Filtering, Sorting, and Summarizing 71

2-8 Conclusion 77

Appendix: Introduction to StatTools 83

3 Finding Relationships among Variables 84

3-1 Introduction 85

3-2 Relationships among Categorical Variables 86

3-3 Relationships among Categorical Variables

and a Numeric Variable 89

3-4 Relationships among Numeric Variables 96

3-4a Scatterplots 96

3-4b Correlation and Covariance 101

3-5 Pivot Tables 106

3-6 Conclusion 126

Appendix: Using StatTools to Find Relationships 131

4 Business Intelligence (BI) Tools for Data Analysis 132

4-1 Introduction 133

4-2 Importing Data into Excel with Power Query 134

4-2a Introduction to Relational Databases 134

4-2b Excel’s Data Model 139

4-2c Creating and Editing Queries 146

4-3 Data Analysis with Power Pivot 152

4-3a Basing Pivot Tables on a Data Model 154

4-3b Calculated Columns, Measures, and the DAX Language 154

4-4 Data Visualization with Tableau Public 162

4-5 Data Cleansing 172

4-6 Conclusion 178

PART 2 Probability and Decision Making under Uncertainty 183

5 Probability and Probability Distributions 184

5-1 Introduction 185

5-2 Probability Essentials 186

5-2a Rule of Complements 187

5-2c Conditional Probability and the Multiplication Rule 188

5-2d Probabilistic Independence 190

5-2e Equally Likely Events 191

5-2f Subjective Versus Objective Probabilities 192

5-3 Probability Distribution of a Random Variable 194

5-3a Summary Measures of a Probability Distribution 195

5-3b Conditional Mean and Variance 198

5-4 The Normal Distribution 200

5-4a Continuous Distributions and Density Functions 200

5-4b The Normal Density Function 201

5-4c Standardizing: Z-Values 202

5-4d Normal Tables and Z-Values 204

5-4e Normal Calculations in Excel 205

5-4f Empirical Rules Revisited 208

5-4g Weighted Sums of Normal Random Variables 208

5-4h Normal Distribution Examples 209

5-5 The Binomial Distribution 214

5-5a Mean and Standard Deviation of the Binomial Distribution 217

5-5b The Binomial Distribution in the Context of Sampling 217

5-5c The Normal Approximation to the Binomial 218

5-5d Binomial Distribution Examples 219

5-6 The Poisson and Exponential Distributions 226

5-6a The Poisson Distribution 227

5-6b The Exponential Distribution 229

5-7 Conclusion 231

6 Decision Making under Uncertainty 242

6-1 Introduction 243

6-2 Elements of Decision Analysis 244

6-3 EMV and Decision Trees 247

6-4 One-Stage Decision Problems 251

6-6 Multistage Decision Problems 257

6.6a Bayes’ Rule 262

6-6b The Value of Information 267

6-6c Sensitivity Analysis 270

6-7 The Role of Risk Aversion 274

6-7a Utility Functions 275

6-7b Exponential Utility 275

6-7c Certainty Equivalents 278

6-7d Is Expected Utility Maximization Used? 279

6-8 Conclusion 280

PART 3 Statistical Inference 293

7 Sampling and Sampling Distributions 294

7-1 Introduction 295

7-2 Sampling Terminology 295

7-3 Methods for Selecting Random Samples 297

7-3a Simple Random Sampling 297

7-3b Systematic Sampling 301

7-3c Stratified Sampling 301

7-3d Cluster Sampling 303

7-3e Multistage Sampling 303

7-4 Introduction to Estimation 305

7-4a Sources of Estimation Error 305

7-4b Key Terms in Sampling 306

7-4c Sampling Distribution of the Sample Mean 307

7-4d The Central Limit Theorem 312

7-4e Sample Size Selection 317

7-4f Summary of Key Ideas in Simple Random Sampling 318

7-5 Conclusion 320

8 Confidence Interval Estimation 323

8-1 Introduction 323

8-2 Sampling Distributions 325

8-2a The t Distribution 326

8-2b Other Sampling Distributions 327

8-3 Confidence Interval for a Mean 328

8-4 Confidence Interval for a Total 333

8-5 Confidence Interval for a Proportion 336

8-6 Confidence Interval for a Standard Deviation 340

8-7 Confidence Interval for the Difference between Means 343

8-7a Independent Samples 344

8-7b Paired Samples 346

8-8 Confidence Interval for the Difference between Proportions 348

8-9 Sample Size Selection 351

8-10 Conclusion 358

9-1 Introduction 369

9-2 Concepts in Hypothesis Testing 370

9-2a Null and Alternative Hypotheses 370

9-2b One-Tailed Versus Two-Tailed Tests 371

9-2c Types of Errors 372

9-2d Significance Level and Rejection Region 372

9-2e Significance from p-values 373

9-2f Type II Errors and Power 375

9-2g Hypothesis Tests and Confidence Intervals 375

9-2h Practical Versus Statistical Significance 375

9-3 Hypothesis Tests for a Population Mean 376

9-4 Hypothesis Tests for Other Parameters 380

9-4a Hypothesis Test for a Population Proportion 380

9-4b Hypothesis Tests for Difference between Population Means 382

9-4c Hypothesis Test for Equal Population Variances 388

9-4d Hypothesis Test for Difference between Population Proportions 388

9-5 Tests for Normality 395

9-6 Chi-Square Test for Independence 401

9-7 Conclusion 404

PART 4 Regression Analysis and Time Series Forecasting 411

10 Regression Analysis: Estimating Relationships 412

10-1 Introduction 413

10-2 Scatterplots: Graphing Relationships 415

10-3 Correlations: Indicators of Linear Relationships 422

10-4 Simple Linear Regression 424

10-4a Least Squares Estimation 424

10-4b Standard Error of Estimate 431

10-4c R-Square 432

10-5 Multiple Regression 435

10-5a Interpretation of Regression Coefficients 436

10-5b Interpretation of Standard Error of Estimate and R-Square 439

10-6 Modeling Possibilities 442

10-6a Dummy Variables 442

10-6b Interaction Variables 448

10-6c Nonlinear Transformations 452

10-7 Validation of the Fit 461

10-8 Conclusion 463

11 Regression Analysis: Statistical Inference 472

11-1 Introduction 473

11-2 The Statistical Model 474

11-3 Inferences About the Regression Coefficients 477

11-3a Sampling Distribution of the Regression Coefficients 478

11-3b Hypothesis Tests for the Regression Coefficients and p-Values 480

11-3c A Test for the Overall Fit: The ANOVA Table 481

11-4 Multicollinearity 485

11-5 Include/Exclude Decisions 489

11-6 Stepwise Regression 494

11-7 Outliers 499

11-8 Violations of Regression Assumptions 504

11-8a Nonconstant Error Variance 504

11-8b Nonnormality of Residuals 504

11-8c Autocorrelated Residuals 505

11-9 Prediction 507

11-10 Conclusion 512

12 Time Series Analysis and Forecasting 523

12-1 Introduction 524

12-2 Forecasting Methods: An Overview 525

12-2a Extrapolation Models 525

12-2b Econometric Models 526

12-2c Combining Forecasts 526

12-2d Components of Time Series Data 527

12-2e Measures of Accuracy 529

12-3 Testing for Randomness 531

12-3a The Runs Test 534

12-3b Autocorrelation 535

12-4 Regression-Based Trend Models 539

12-4a Linear Trend 539

12-4b Exponential Trend 541

12-5 The Random Walk Model 544

12-6 Moving Averages Forecasts 547

12-7 Exponential Smoothing Forecasts 551

12-7a Simple Exponential Smoothing 552

12-7b Holt’s Model for Trend 556

12-8 Seasonal Models 560

12-8a Winters’ Exponential Smoothing Model 561

12-8b Deseasonalizing: The Ratio-to-Moving-Averages Method 564

12-8c Estimating Seasonality with Regression 565

12-9 Conclusion 569

PART 5 Optimization and Simulation Modeling 575

13 Introduction to Optimization Modeling 576

13-1 Introduction 577

13-2 Introduction to Optimization 577

13-3 A Two-Variable Product Mix Model 579

13-4 Sensitivity Analysis 590

13-4a Solver’s Sensitivity Report 590

13-4c A Comparison of Solver’s Sensitivity Report and SolverTable 599

13-5 Properties of Linear Models 600

13-6 Infeasibility and Unboundedness 602

13-7 A Larger Product Mix Model 604

13-8 A Multiperiod Production Model 612

13-9 A Comparison of Algebraic and Spreadsheet Models 619

13-10 A Decision Support System 620

13-11 Conclusion 622

14 Optimization Models 630

14-1 Introduction 631

14-2 Employee Scheduling Models 632

14-3 Blending Models 638

14-4 Logistics Models 644

14-4a Transportation Models 644

14-4b More General Logistics Models 651

14-5 Aggregate Planning Models 659

14-6 Financial Models 667

14-7 Integer Optimization Models 677

14-7a Capital Budgeting Models 678

14-7b Fixed-Cost Models 682

14-7c Set-Covering Models 689

14-8 Nonlinear Optimization Models 695

14-8a Difficult Issues in Nonlinear Optimization 695

14-8b Managerial Economics Models 696

14-8c Portfolio Optimization Models 700

14-9 Conclusion 708

15 Introduction to Simulation Modeling 717

15-1 Introduction 718

15-2 Probability Distributions for Input Variables 720

15-2a Types of Probability Distributions 721

15-2b Common Probability Distributions 724

15-2c Using @RISK to Explore Probability Distributions 728

15-3 Simulation and the Flaw of Averages 736

15-4 Simulation with Built-in Excel Tools 738

15-5 Simulation with @RISK 747

15-5a @RISK Features 748

15-5c @RISK Models with a Single Random Input 749

15-5d Some Limitations of @RISK 758

15-5e @RISK Models with Several Random Inputs 758

15-6 The Effects of Input Distributions on Results 763

15-6a Effect of the Shape of the Input Distribution(s) 763

15-6b Effect of Correlated Inputs 766

15-7 Conclusion 771

16 Simulation Models 779

16-1 Introduction 780

16-2 Operations Models 780

16-2a Bidding for Contracts 780

16-2b Warranty Costs 784

16-2c Drug Production with Uncertain Yield 789

16-3 Financial Models 794

16-3a Financial Planning Models 795

16-3b Cash Balance Models 799

16-3c Investment Models 803

16-4 Marketing Models 810

16-4a Customer Loyalty Models 810

16-4b Marketing and Sales Models 817

16-5 Simulating Games of Chance 823

16-5a Simulating the Game of Craps 823

16-5b Simulating the NCAA Basketball Tournament 825

16-6 Conclusion 828

PART 6 Advanced Data Analysis 837

17 Data Mining 838

17-1 Introduction 839

17-2 Classification Methods 840

17-2a Logistic Regression 841

17-2b Neural Networks 846

17-2c Naïve Bayes 851

17-2d Classification Trees 854

17-2e Measures of Classification Accuracy 855

17-2f Classification with Rare Events 857

17-3 Clustering Methods 860

17-4 Conclusion 870

18 Analysis of Variance and Experimental Design (MindTap Reader only)

18-1 Introduction 18-2

18-2 One-Way ANOVA 18-5

18-2a The Equal-Means Test 18-5

18-2b Confidence Intervals for Differences Between Means 18-7

18-2c Using a Logarithmic Transformation 18-11

18-3 Using Regression to Perform ANOVA 18-15

18-4 The Multiple Comparison Problem 18-18

18-5 Two-Way ANOVA 18-22

18-5a Confidence Intervals for Contrasts 18-28

18-5b Assumptions of Two-Way ANOVA 18-30

18-6 More About Experimental Design 18-32

18-6a Randomization 18-32

18-6b Blocking 18-35

18-6c Incomplete Designs 18-38

18-7 Conclusion 18-40

19 Statistical Process Control (MindTap Reader only)

19-1 Introduction 19-2

19-2 Deming’s 14 Points 19-3

19-3 Introduction to Control Charts 19-6

19-4 Control Charts for Variables 19-8

19-4a Control Charts and Hypothesis Testing 19-13

19-4b Other Out-of-Control Indications 19-15

19-4c Rational Subsamples 19-16

19-4d Deming’s Funnel Experiment and Tampering 19-18

19-4e Control Charts in the Service Industry 19-22

19-5 Control Charts for Attributes 19-26

19-5a P Charts 19-26

19-5b Deming’s Red Bead Experiment 19-29

19-6 Process Capability 19-33

19-6a Process Capability Indexes 19-35

19-6b More on Motorola and 6-Sigma 19-40

19-7 Conclusion 19-43

17-3 Clustering Methods 860

17-4 Conclusion 870

18 Analysis of Variance and Experimental Design (MindTap Reader only)

18-1 Introduction 18-2

18-2 One-Way ANOVA 18-5

18-2a The Equal-Means Test 18-5

18-2b Confidence Intervals for Differences Between Means 18-7

18-2c Using a Logarithmic Transformation 18-11

18-3 Using Regression to Perform ANOVA 18-15

18-4 The Multiple Comparison Problem 18-18

18-5 Two-Way ANOVA 18-22

18-5a Confidence Intervals for Contrasts 18-28

18-5b Assumptions of Two-Way ANOVA 18-30

18-6 More About Experimental Design 18-32

18-6a Randomization 18-32

18-6b Blocking 18-35

18-6c Incomplete Designs 18-38

18-7 Conclusion 18-40

19 Statistical Process Control (MindTap Reader only)

19-1 Introduction 19-2

19-2 Deming’s 14 Points 19-3

19-3 Introduction to Control Charts 19-6

19-4 Control Charts for Variables 19-8

19-4a Control Charts and Hypothesis Testing 19-13

19-4b Other Out-of-Control Indications 19-15

19-4c Rational Subsamples 19-16

19-4d Deming’s Funnel Experiment and Tampering 19-18

19-4e Control Charts in the Service Industry 19-22

19-5 Control Charts for Attributes 19-26

19-5a P Charts 19-26

19-5b Deming’s Red Bead Experiment 19-29

19-6 Process Capability 19-33

19-6a Process Capability Indexes 19-35

19-6b More on Motorola and 6-Sigma 19-40

19-7 Conclusion 19-43

APPENDIX A: Quantitative Reporting (MindTap Reader only)

A-1 Introduction A-1

A-2 Suggestions for Good Quantitative Reporting A-2

A-2a Planning A-2

A-2b Developing a Report A-3

A-2c Be Clear A-4

A-2d Be Concise A-4

A-2e Be Precise A-5

A-3 Examples of Quantitative Reports A-6

A-4 Conclusion A-16

References 873

Index 875

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