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Business Analytics: Data Analysis & Decision Making 7th Edition PDF – EBook

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  • Authors: S. Christian Albright, Wayne L. Winston (Author)
  • File Size: 45 MB
  • Format: PDF
  • Paperback: 984 pages
  • Publisher: Cengage Learning; 7th edition (April 2, 2019)
  • Language: English
  • ISBN-10: 0357109953
  • ISBN-13: 978-0357109953

Download Business Analytics: Data Analysis & Decision Making 7th Edition PDF – EBook

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Description

  • Authors: S. Christian Albright, Wayne L. Winston (Author)
  • File Size: 45 MB
  • Format: PDF
  • Paperback: 984 pages
  • Publisher: Cengage Learning; 7th edition (April 2, 2019)
  • Language: English
  • ISBN-10: 0357109953
  • ISBN-13: 978-0357109953

Download Business Analytics: Data Analysis & Decision Making 7th Edition PDF – EBook

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

CONTENTS   vii
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
5-2b Addition Rule 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

viii   CONTENTS
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
6-5 The PrecisionTree Add-In 254
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

CONTENTS   ix
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 Hypothesis Testing 368
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

x   CONTENTS
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

CONTENTS   xi
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

xii   CONTENTS
13-4 Sensitivity Analysis 590
13-4a Solver’s Sensitivity Report 590
13-4b SolverTable Add-In 593
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

CONTENTS   xiii
15-5 Simulation with @RISK 747
15-5a @RISK Features 748
15-5b Loading @RISK 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

xiv   CONTENTS
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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