Essentials of business analytics:
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
2015
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| Edition: | First edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIX, 675 S. Ill. |
| ISBN: | 9781285187273 |
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Record in the Search Index
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| adam_text | Titel: Essentials of business analytics
Autor: Camm, Jeffrey D
Jahr: 2015
About the Authors xiv
Preface xvii
Chapter 1 Introduction 1
1.1 Decision Making 4
1.2 Business Analytics Defined 5
1.3 A Categorization of Analytical Methods and Models 5
Descriptive Analytics 5
Predictive Analytics 6
Prescriptive Analytics 6
Analytics in Action: Procter Gamble Uses Business Analytics
to Redesign its Supply Chain 7
1.4 Big Data 8
1.5 Business Analytics in Practice 9
Financial Analytics 9
Human Resource (HR) Analytics 10
Marketing Analytics 10
Health Care Analytics 10
Supply Chain Analytics 11
Analytics for Government and Nonprofits 11
Sports Analytics 12
Web Analytics 12
Summary 13
Glossary 13
Chapter 2 Descriptive Statistics 15
Analytics in Action: U.S. Census Bureau 16
2.1 Overview of Using Data: Definitions and Goals 16
2.2 Types of Data 17
Population and Sample Data 17
Quantitative and Categorical Data 18
Cross-Sectional and Time Series Data 18
Sources of Data 18
2.3 Modifying Data in Excel 21
Sorting and Filtering Data in Excel 21
Conditional Formatting of Data in Excel 23
2.4 Creating Distributions from Data 25
Frequency Distributions for Categorical Data 25
Relative Frequency and Percent Frequency Distributions 27
Frequency Distributions for Quantitative Data 28
Histograms 31
Cumulative Distributions 34
2.5 Measures of Location 35
Mean (Arithmetic Mean) 35
Median 36
Mode 37
Geometric Mean 38
2.6 Measures of Variability 40
Range 41
Variance 41
Standard Deviation 43
Coefficient of Variation 44
2.7 Analyzing Distributions 44
Percentiles 44
Quartiles 45
z-scores 46
Empirical Rule 48
Identifying Outliers 48
Box Plots 49
2.8 Measures of Association Between Two Variables 51
Scatter Charts 51
Covariance 52
Correlation Coefficient 55
Summary 57
Glossary 57
Problems 58
Case: Heavenly Chocolates Web Site Transactions 66
Appendix: Creating Box Plots in XLMiner 67
Chapter 3 Data Visualization 70
Analytics in Action: Cincinnati Zoo Botanical Garden 71
3.1 Overview of Data Visualization 73
Effective Design Techniques 73
3.2 Tables 75
Table Design Principles 77
Crosstabulation 79
PivotTables in Excel 80
3.3 Charts 85
Scatter Charts 85
Line Charts 87
Bar Charts and Column Charts 90
A Note on Pie Charts and 3-D Charts 93
Bubble Charts 93
Heat Maps 95
Additional Charts for Multiple Variables 97
PivotCharts in Excel 101
3.4 Advanced Data Visualization 102
Advanced Charts 103
Geographic Information Systems Charts 104
3.5 Data Dashboards 105
Principles of Effective Data Dashboards 106
Applications of Data Dashboards 106
Summary 108
Glossary 109
Problems 110
Case Problem: Ail-Time Movie Box Office Data 118
Appendix: Creating a Scatter Chart Matrix and a Parallel Coordinates
Plot with XLMiner 119
Chapter 4 Linear Regression 123
Analytics in Action: Alliance Data Systems 124
4.1 The Simple Linear Regression Model 125
Regression Model and Regression Equation 125
Estimated Regression Equation 126
4.2 Least Squares Method 127
Least Squares Estimates of the Regression Parameters 129
Using Excel s Chart Tools to Compute the Estimated Regression
Equation 132
4.3 Assessing the Fit of the Simple Linear Regression Model 133
The Sums of Squares 134
The Coefficient of Determination 136
Using Excel s Chart Tools to Compute the Coefficient of
Determination 137
4.4 The Multiple Regression Model 138
Regression Model and Regression Equation 138
Estimated Multiple Regression Equation 138
Least Squares Method and Multiple Regression 139
Butler Trucking Company and Multiple Regression 140
Using Excel s Regression Tool to Develop the Estimated Multiple
Regression Equation 140
4.5 Inference and Regression 143
Conditions Necessary for Valid Inference in the Least Squares
Regression Model 144
Testing for an Overall Regression Relationship 148
Testing Individual Regression Parameters 150
Addressing Nonsignificant Independent Variables 153
Multicollinearity 154
Inference and Very Large Samples 156
4.6 Categorical Independent Variables 161
Butler Trucking Company and Rush Hour 161
Interpreting the Parameters 162
More Complex Categorical Variables 164
4.7 Modeling Nonlinear Relationships 165
Quadratic Regression Models 167
Piecewise Linear Regression Models 170
Interaction Between Independent Variables 173
4.8 Model Fitting 177
Variable Selection Procedures 177
O verfitting 179
Summary 180
Glossary 180
Problems 182
Case Problem: Alumni Giving 197
Appendix: Using XLMiner for Regression 198
Chapter 5 Time Series Analysis and Forecasting 202
Analytics in Action: Forecasting Demand for a Broad Line
of Office Products 203
5.1 Time Series Patterns 205
Horizontal Pattern 205
Trend Pattern 207
Seasonal Pattern 209
Trend and Seasonal Pattern 209
Cyclical Pattern 211
Identifying Time Series Patterns 212
5.2 Forecast Accuracy 212
5.3 Moving Averages and Exponential Smoothing 217
Moving Averages 217
Forecast Accuracy 221
Exponential Smoothing 221
Forecast Accuracy 224
5.4 Using Regression Analysis for Forecasting 226
Linear Trend Projection 226
Seasonality 228
Seasonality Without Trend 228
Seasonality with Trend 230
Using Regression Analysis as a Causal Forecasting Method 231
Combining Causal Variables with Trend and Seasonality
Effects 235
Considerations in Using Regression in Forecasting 235
5.5 Determining the Best Forecasting Model to Use 236
Summary 237
Glossary 237
Problems 238
Case Problem: Forecasting Food and Beverage Sales 246
Appendix: Using XLMiner for Forecasting 247
Chapter 6 Data Mining 251
Analytics in Action: Online Retailers Using Predictive Analytics
to Cater to Customers 252
6.1 Data Sampling 253
6.2 Data Preparation 254
Treatment of Missing Data 254
Identification of Outliers and Erroneous Data 254
Variable Representation 254
6.3 Unsupervised Learning 255
Cluster Analysis 256
Association Rules 265
6.4 Supervised Learning 269
Partitioning Data 269
Classification Accuracy 273
Prediction Accuracy 277
^-Nearest Neighbors 277
Classification and Regression Trees 283
Logistic Regression 299
Summary 308
Glossary 309
Problems 311
Case Problem: Grey Code Corporation 319
Chapter 7 Spreadsheet Models 320
Analytics in Action: Procter and Gamble Sets Inventory Targets Using
Spreadsheet Models 321
7.1 Building Good Spreadsheet Models 322
Influence Diagrams 322
Building a Mathematical Model 322
Spreadsheet Design and Implementing the Model
in a Spreadsheet 324
7.2 What-If Analysis 327
Data Tables 327
Goal Seek 331
7.3 Some Useful Excel Functions for Modeling 332
SUM and SUMPRODUCT 332
IF and COUNTIF 333
VLOOKUP 337
7.4 Auditing Spreadsheet Models 339
Trace Precedents and Dependents 339
Show Formulas 340
Evaluate Formulas 340
Error Checking 341
Watch Window 342
Summary 343
Glossary 343
Problems 344
Case Problem: Retirement Plan 350
Chapter 8 Linear Optimization Models 352
Analytics in Action: Timber Harvesting Model at MeadWestvaco
Corporation 353
8.1 A Simple Maximization Problem 354
Problem Formulation 355
Mathematical Model for the Par, Inc. Problem 357
8.2 Solving the Par, Inc. Problem 358
The Geometry of the Par, Inc. Problem 358
Solving Linear Programs with Excel Solver 360
8.3 A Simple Minimization Problem 364
Problem Formulation 364
Solution for the M D Chemicals Problem 365
8.4 Special Cases of Linear Program Outcomes 367
Alternative Optimal Solutions 367
Infeasibility 368
Unbounded 370
8.5 Sensitivity Analysis 372
Interpreting Excel Solver Sensitivity Report 372
8.6 General Linear Programming Notation and More Examples 374
Investment Portfolio Selection 375
Transportation Planning 378
Advertising Campaign Planning 381
8.7 Generating an Alternative Optimal Solution for a Linear Program 386
Summary 388
Glossary 389
Problems 390
Case Problem: Investment Strategy 398
Appendix: Solving Linear Optimization Models Using Analytic Solver
Platform 399
Chapter 9 Integer Linear Optimization Models 405
Analytics in Action: Optimizing the Transport of Oil Rig Crews 406
9.1 Types of Integer Linear Optimization Models 406
9.2 Lastborne Realty, An Example of Integer Optimization 407
The Geometry of Linear All-Integer Optimization 408
9.3 Solving Integer Optimization Problems with Excel Solver 410
A Cautionary Note About Sensitivity Analysis 414
9.4 Applications Involving Binary Variables 415
Capital Budgeting 415
Fixed Cost 416
Bank Location 420
Product Design and Market Share Optimization 424
9.5 Modeling Flexibility Provided by Binary Variables 426
Multiple-Choice and Mutually Exclusive Constraints 427
k out of n Alternatives Constraint 427
Conditional and Corequisite Constraints 427
9.6 Generating Alternatives in Binary Optimization 428
Summary 430
Glossary 430
Problems 431
Case Problem: Applecore Children s Clothing 441
Appendix: Solving Integer Linear Optimization Problems Using Analytic
Solver Platform 442
Chapter 10 Nonlinear Optimization Models 448
Analytics in Action: Intercontinental Hotels Optimizes Retail Pricing 449
10.1 A Production Application: Par, Inc. Revisited 449
An Unconstrained Problem 450
A Constrained Problem 450
Solving Nonlinear Optimization Models Using Excel Solver 453
Sensitivity Analysis and Shadow Prices in Nonlinear Models 454
10.2 Local and Global Optima 455
Overcoming Local Optima with Excel Solver 457
10.3 A Location Problem 459
10.4 Markowitz Portfolio Model 461
10.5 Forecasting Adoption of a New Product 465
Summary 469
Glossary 470
Problems 470
Case Problem: Portfolio Optimization with Transaction Costs 477
Appendix: Solving Nonlinear Optimization Problems with Analytic
Solver Platform 480
Chapter 11 Monte Carlo Simulation 485
Analytics in Action: Reducing Patient Infections in the ICU 486
11.1 What-If Analysis 487
The S ano tronies Problem 487
Base-Case Scenario 487
Worst-Case Scenario 488
Best-Case Scenario 488
11.2 Simulation Modeling with Native Excel Functions 488
Use of Probability Distributions to Represent
Random Variables 489
Generating Values for Random Variables with Excel 491
Executing Simulation Trials with Excel 495
Measuring and Analyzing Simulation Output 495
11.3 Simulation Modeling with Analytic Solver Platform 498
The Land Shark Problem 499
Spreadsheet Model for Land Shark 499
Generating Values for Land Shark s Random Variables 500
Tracking Output Measures for Land Shark 503
Executing Simulation Trials and Analyzing Output for Land Shark 504
The Zappos Problem 506
Spreadsheet Model for Zappos 507
Modeling Random Variables for Zappos 510
Tracking Output Measures for Zappos 515
Executing Simulation Trials and Analyzing Output for Zappos 517
11.4 Simulation Optimization 518
11.5 Simulation Considerations 524
Verification and Validation 524
Advantages and Disadvantages of Using Simulation 524
Summary 525
Glossary 526
Problems 527
Case Problem: Four Corners 536
Appendix 11.1: Incorporating Dependence Between
Random Variables 537
Appendix 11.2: Probability Distributions for Random Variables 545
Chapter 12 Decision Analysis 550
Analytics in Action: Phytopharm s New Product Research and
Development 551
12.1 Problem Formulation 552
Payoff Tables 553
Decision Trees 553
12.2 Decision Analysis Without Probabilities 554
Optimistic Approach 554
Conservative Approach 555
Minimax Regret Approach 555
12.3 Decision Analysis with Probabilities 557
Expected Value Approach 557
Risk Analysis 559
Sensitivity Analysis 560
12.4 Decision Analysis with Sample Information 561
Expected Value of Sample Information 566
Expected Value of Perfect Information 567
12.5 Computing Branch Probabilities with Bayes Theorem 568
12.6 Utility Theory 571
Utility and Decision Analysis 573
Utility Functions 577
Exponential Utility Function 580
Summary 581
Glossary 582
Problems 584
Case Problem: Property Purchase Strategy 595
Appendix: Using Analytic Solver Platform to Create Decision TVees 596
Appendix A Basics of Excel 609
Appendix B Data Management and Microsoft Access 621
Appendix C Answers to Even-Numbered Exercises (online)
References 659
Index 661
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| spelling | Essentials of business analytics Jeffrey D. Camm ... First edition 2015 XIX, 675 S. Ill. txt rdacontent n rdamedia nc rdacarrier Microsoft Excel (Computer file) Microsoft Excel (Computer file) fast Decision making / Mathematical models Data mining Industrial management / Statistical methods / Computer programs Decision making / Computer programs Data mining fast Decision making / Computer programs fast Decision making / Mathematical models fast Industrial management / Statistical methods / Computer programs fast Mathematisches Modell Camm, Jeffrey D. Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028028741&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Essentials of business analytics Microsoft Excel (Computer file) Microsoft Excel (Computer file) fast Decision making / Mathematical models Data mining Industrial management / Statistical methods / Computer programs Decision making / Computer programs Data mining fast Decision making / Computer programs fast Decision making / Mathematical models fast Industrial management / Statistical methods / Computer programs fast Mathematisches Modell |
| title | Essentials of business analytics |
| title_auth | Essentials of business analytics |
| title_exact_search | Essentials of business analytics |
| title_full | Essentials of business analytics Jeffrey D. Camm ... |
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| title_short | Essentials of business analytics |
| title_sort | essentials of business analytics |
| topic | Microsoft Excel (Computer file) Microsoft Excel (Computer file) fast Decision making / Mathematical models Data mining Industrial management / Statistical methods / Computer programs Decision making / Computer programs Data mining fast Decision making / Computer programs fast Decision making / Mathematical models fast Industrial management / Statistical methods / Computer programs fast Mathematisches Modell |
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