Business analytics: methods, models, and decisions
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston, Mass. [u.a.]
Pearson
2013
|
| Ausgabe: | Internat. ed. |
| Schriftenreihe: | Always learning
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIX, 665 S. Ill., graph. Darst. |
| ISBN: | 9780133051711 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804148843843944448 |
|---|---|
| adam_text | Titel: Business analytics
Autor: Evans, James R
Jahr: 2013
Contents
Preface xiii
About the Author xix
Part 1: Foundations of Business Analytics
Chapter 1: Introduction to Business Analytics 1
Learning Objectives 1
What Is Business Analytics? 3
Evolution of Business Analytics 5
Scope of Business Analytics 6
Data for Business Analytics 8
Data Sets and Databases 10 • Metrics and Data Classification 10 •
Data Reliability and Validity 13
Decision Models 13
Descriptive Decision Models 15 * Predictive Decision Models 19 •
Prescriptive Decision Models 21
Problem Solving and Decision Making 22
Recognizing a Problem 22 • Defining the Problem 23 •
Structuring the Problem 23 • Analyzing the Problem 23 • Interpreting
Results and Making a Decision 23 • Implementing the Solution 24
Key Terms 25 • Fun with Analytics 25 • Problems and
Exercises 25 • Case: Performance Lawn Equipment 28
Chapter 2: Analytics on Spreadsheets 31
Learning Objectives 31
Basic Excel Skills 33
Excel Formulas 34 • Copying Formulas 35 • Other Useful Excel Tips 36
Excel Functions 37
Basic Excel Functions 37 • Functions for Specific Applications 38 • Insert
Function 39 • Logical Functions 39 • Lookup Functions 41
Spreadsheet Add-Ins for Business Analytics 43
Spreadsheet Modeling and Spreadsheet Engineering 44
Spreadsheet Quality 46
Key Terms 49 • Problems and Exercises 49 • Case: Performance Lawn
Equipment 51
Part 2: Descriptive Analytics
Chapter 3: Visualizing and Exploring Data 53
Learning Objectives 53
Data Visualization 54
Creating Charts in Microsoft Excel 2010 54 • Miscellaneous Excel
Charts 59 • Geographic Data 60
Data Queries: Using Sorting and Filtering 60
Sorting Data in Excel 61 * Pareto Analysis 61 * Filtering Data 62
Statistical Methods for Summarizing Data 65
Frequency Distributions for Categorical Data 66 • Relative Frequency
Distributions 68 • Frequency Distributions for Numerical Data 68 •
Excel Histogram Tool 69 • Cumulative Relative Frequency
Distributions 72 • Percentiles and Quartiles 73 • Cross-Tabulations 75
Exploring Data Using PivotTables 77
PivotCharts 81
Key Terms 81 • Problems and Exercises 82 • Case: Performance Lawn
Equipment 84
Chapter 4: Descriptive Statistical Measures 85
Learning Objectives 85
Populations and Samples 86
Understanding Statistical Notation 86
Measures of Location 87
Arithmetic Mean 87 • Median 88 • Mode 89 • Midrange 90 •
Using Measures of Location in Business Decisions 90
Measures of Dispersion 91
Range 91 • Interquartile Range 92 • Variance 92 •
Standard Deviation 93 • Chebyshev s Theorem and the Empirical Rules 94
Standardized Values 98 • Coefficient of Variation 99
Measures of Shape 99
Excel Descriptive Statistics Tool 102
Descriptive Statistics for Grouped Data 103
Descriptive Statistics for Categorical Data: The Proportion 106
Statistics in PivotTables 106
Measures of Association 106
Covariance 108 • Correlation 109 • Excel Correlation Tool 112
Outliers 113
Statistical Thinking in Business Decisions 115
Variability in Samples 116
Key Terms 119 • Problems and Exercises 119 • Case: Performance Lawn
Equipment 124
Chapter 5: Probability Distributions and Data Modeling 125
Learning Objectives 125
Basic Concepts of Probability 126
Probability Rules and Formulas 128 • Conditional Probability 129
Random Variables and Probability Distributions 132
Discrete Probability Distributions 135
Expected Value of a Discrete Random Variable 136 * Using Expected
Value in Making Decisions 137 • Variance of a Discrete Random
Variable 139 • Bernoulli Distribution 139 • Binomial Distribution 140
Poisson Distribution 142
Continuous Probability Distributions 143
Properties of Probability Density Functions 145 • Uniform Distribution 146
Normal Distribution 148 • The NORM.INV Function 150 • Standard
Normal Distribution 150 • Using Standard Normal Distribution Tables 152
Exponential Distribution 152 • Other Useful Distributions 154 •
Continuous Distributions 154
Random Sampling from Probability Distributions 155
Sampling from Discrete Probability Distributions 156 • Sampling from
Common Probability Distributions 157 • Risk Solver Platform Distribution
Functions 160
Data Modeling and Distribution Fitting 162
Goodness of Fit 164 • Distribution Fitting with Risk Solver Platform 164
Key Terms 166 • Problems and Exercises 167 • Case: Performance Lawn
Equipment 174
Chapter 6: Sampling and Estimation 175
Learning Objectives 175
Statistical Sampling 176
Sampling Methods 176
Estimating Population Parameters 180
Unbiased Estimators 180 • Errors in Point Estimation 181
Sampling Error 181
Understanding Sampling Error 181
Sampling Distributions 183
Sampling Distribution of the Mean 183 • Applying the Sampling Distribution
of the Mean 184
Interval Estimates 185
Confidence Intervals 186
Confidence Interval for the Mean with Known Population Standard
Deviation 186 • The f-Distribution 188 • Confidence Interval
for the Mean with Unknown Population Standard Deviation 189 •
Confidence Interval for a Proportion 189 * Additional Types of Confidence
Intervals 190
Using Confidence Intervals for Decision Making 192
Prediction Intervals 192
Confidence Intervals and Sample Size 193
Key Terms 195 • Problems and Exercises 195 • Case: Performance Lawn
Equipment 198
Chapter 7: Statistical Inference 199
Learning Objectives 199
Hypothesis Testing 200
Hypothesis-Testing Procedure 201
One-Sample Hypothesis Tests 201
Understanding Risk in Hypothesis Testing 202 • Selecting the Test
Statistic 203 * Drawing a Conclusion 204 • p- Values 206 •
Two-Tailed Test of Hypothesis for the Mean 206 • One-Sample Tests
for Proportions 207
Two-Sample Hypothesis Tests 208
Two-Sample Tests for Differences in Means 209 • Two-Sample Test for
Means with Paired Samples 212 • Test for Equality of Variances 214
Analysis of Variance 215
Assumptions of ANOVA 216
Chi-Square Test for Independence 217
Key Terms 221 • Problems and Exercises 221 * Case: Performance Lawn
Equipment 225
Part 3: Predictive Analytics
Chapter 8: Predictive Modeling and Analysis 226
Learning Objectives 226
Logic-Driven Modeling 227
Strategies for Building Predictive Models 227 • Data and Models 229 •
Models Involving Multiple Time Periods 231 • Single-Period Purchase
Decisions 231 • Overbooking Decisions 233 • Model Assumptions,
Complexity, and Realism 234
Data-Driven Modeling 236
Retail Pricing Markdowns 238 * Modeling Relationships and Trends
in Data 238
Analyzing Uncertainty and Model Assumptions 243
What-If Analysis 244 • Data Tables 244 • Scenario Manager 248 •
Goal Seek 251
Model Analysis Using Risk Solver Platform 251
Parametric Sensitivity Analysis 251 • Tornado Charts 255
Key Terms 256 • Problems and Exercises 256 • Case: Performance Lawn
Equipment 260
Chapter 9: Regression Analysis 261
Learning Objectives 261
Simple Linear Regression 262
Finding the Best-Fitting Regression Line 263 • Least-Squares Regression 265
Simple Linear Regression with Excel 267 • Regression as Analysis of
Variance 269 • Testing Hypotheses for Regression Coefficients 270 *
Confidence Intervals for Regression Coefficients 271
Residual Analysis and Regression Assumptions 271
Checking Assumptions 272
Multiple Linear Regression 274
Building Good Regression Models 279
Correlation and Multicollinearity 281
Regression with Categorical Independent Variables 283
Categorical Variables with More Than Two Levels 285
Regression Models with Nonlinear Terms 287
Key Terms 291 • Problems and Exercises 291 • Case: Performance Lawn
Equipment 295
Chapter 10: Forecasting Techniques 297
Learning Objectives 297
Qualitative and Judgmental Forecasting 298
Historical Analogy 298 • The Delphi Method 299 • Indicators and
Indexes 299
Statistical Forecasting Models 300
Forecasting Models for Stationary Time Series 302 * Moving Average
Models 302 • Error Metrics and Forecast Accuracy 307 • Exponential
Smoothing Models 308
Forecasting Models for Time Series with a Linear Trend 311
Double Exponential Smoothing 313 • Regression-Based Forecasting for
Time Series with a Linear Trend 313
Forecasting Time Series with Seasonality 315
Regression-Based Seasonal Forecasting Models 316 • Holt-Winters Forecasting
for Seasonal Time Series 317 • Holt-Winters Models for Forecasting Time Series
with Seasonality and Trend 318
Selecting Appropriate Time-Series-Based Forecasting Models 322
Regression Forecasting with Causal Variables 323
The Practice of Forecasting 325
Key Terms 326 • Problems and Exercises 326 • Case: Performance Lawn
Equipment 328
Chapter 11: Simulation and Risk Analysis 329
Learning Objectives 329
Spreadsheet Models with Random Variables 331
Monte Carlo Simulation 331
Monte Carlo Simulation Using Risk Solver Platform 333
Defining Uncertain Model Inputs 334 • Defining Output Cells 335 •
Running a Simulation 336 * Analyzing Results 338
New-Product Development Model 339
Confidence Interval for the Mean 343 • Sensitivity Chart 343 • Overlay
Charts 344 • Trend Charts 346 • Box-Whisker Charts 346 •
Simulation Reports 347
Newsvendor Model 347
The Flaw of Averages 348 • Monte Carlo Simulation Using Historical
Data 348 • Monte Carlo Simulation Using a Fitted Distribution 350
Overbooking Model 351
The Custom Distribution in Risk Solver Platform 351
Cash Budget Model 352
Correlating Uncertain Variables 356
Key Terms 360 • Problems and Exercises 360 • Case: Performance Lawn
Equipment 365
Chapter 12: Introduction to Data Mining 366
Learning Objectives 366
The Scope of Data Mining 368
Data Exploration and Reduction 369
Cluster Analysis 369
Classification 372
An Intuitive Explanation of Classification 376 • Measuring Classification
Performance 378 • Using Training and Validation Data 378 • Classifying
New Data 380
Classification Techniques 381
^-Nearest Neighbors (fc-NN) 381 • Discriminant Analysis 385 •
Logistic Regression 389
Association Rule Mining 393
Cause-and-Effect Modeling 397
Key Terms 401 • Problems and Exercises 401 • Case: Performance Lawn
Equipment 402
Part 4: Prescriptive Analytics
Chapter 13: Linear Optimization 404
Learning Objectives 404
Building Linear Optimization Models 405
Identifying Elements for an Optimization Model 405 • Translating
Model Information into Mathematical Expressions 406 • More about
Constraints 408 • Characteristics of Linear Optimization Models 409
Implementing Linear Optimization Models on Spreadsheets 409
Excel Functions to Avoid in Linear Optimization 411
Solving Linear Optimization Models 411
Using the Standard Solver 412 • Using Premium Solver 415 •
Solver Answer Report 416
Graphical Interpretation of Linear Optimization 418
How Solver Works 423
How Solver Creates Names in Reports 425 • Difficulties with Solver 425
Solver Outcomes and Solution Messages 425
Unique Optimal Solution 426 • Alternative Optimal Solutions 426 •
Unbounded Solution 427 • Infeasible Problem 428
Using Optimization Models for Prediction and Insight 429
Solver Sensitivity Report 431 • Using the Sensitivity Report 434 •
Parameter Analysis in Risk Solver Platform 436
Key Terms 439 • Problems and Exercises 439 • Case: Performance Lawn
Equipment 445
Chapter 14: Applications of Linear Optimization 446
Learning Objectives 446
Types of Constraints in Optimization Models 448
Process Selection Models 449
Spreadsheet Design and Solver Reports 450
Blending Models 453
Dealing with Infeasibility 454
Portfolio Investment Models 457
Evaluating Risk Versus Reward 458
Transportation Models 461
Formatting the Sensitivity Report 463 • Degeneracy 465
Multiperiod Production Planning Models 465
Building Alternative Models 468
Multiperiod Financial Planning Models 468
Models with Bounded Variables 473
Auxiliary Variables for Bound Constraints 478
A Production/Marketing Allocation Model 478
Using Sensitivity Information Correctly 480
Key Terms 483 • Problems and Exercises 484 • Case: Performance Lawn
Equipment 496
Chapter 15: Integer Optimization 498
Learning Objectives 498
Solving Models with General Integer Variables 499
Workforce-Scheduling Models 504 • Alternative Optimal
Solutions 504
Integer Optimization Models with Binary Variables 508
Project-Selection Models 509 • Using Binary Variables to Model Logical
Constraints 511 • Location Models 513 • Parameter Analysis 513 •
A Customer-Assignment Model for Supply Chain Optimization 515
Mixed-Integer Optimization Models 518
Plant Location Models 518 • Binary Variables, IF Functions, and Nonlinearities
in Model Formulation 520 * Fixed-Cost Models 521
Key Terms 523 • Problems and Exercises 524 • Case: Performance Lawn
Equipment 531
Chapter 16: Nonlinear and Non-Smooth Optimization 533
Learning Objectives 533
Modeling and Solving Nonlinear Optimization Problems 534
Pricing Decision Models 534 • Interpreting Solver Reports for Nonlinear
Optimization Models 538 • Locating Central Facilities 539 *
The Economic Order-Quantity Model 540 • Using Empirical Data for
Nonlinear Optimization Modeling 545 • Practical Issues Using Solver for
Nonlinear Optimization 546
Quadratic Optimization 548
The Markowitz Portfolio Model 548
Evolutionary Solver for Non-Smooth Optimization 552
Spreadsheet Models with Non-Smooth Excel Functions 552 * Optimization
Models for Sequencing and Scheduling 556 • The Traveling Salesperson
Problem 557
Key Terms 561 • Problems and Exercises 561 • Case: Performance Lawn
Equipment 566
Chapter 17: Optimization Models with Uncertainty 567
Learning Objectives 567
Risk Analysis in Optimization 568
Chance Constraints 568 • Service Levels in the Economic Order Quantity
Model 573 • Hotel Pricing Model with Uncertainty 575
Optimizing Monte Carlo Simulation Models 579
Optimizing the Newsvendor Model Using Multiple Parameterized
Simulations 579 • Optimizing the Hotel Overbooking Model Using
Multiple Parameterized Simulations 579
Simulation Optimization Using Risk Solver Platform 581
A Portfolio Allocation Model 581 • Project Selection 585
Key Terms 587 • Problems and Exercises 587 • Case: Performance Lawn
Equipment 590
Part 5: Making Decisions
Chapter 18: Decision Analysis 591
Learning Objectives 591
Making Decisions with Uncertain Information 592
Decision Strategies for a Minimize Objective 593 • Decision Strategies for a
Maximize Objective 596 * Risk and Variability 597 • Expected Value
Strategy 597
Decision Trees 598
Decision Trees and Monte Carlo Simulation 602 • Decision Trees and
Risk 604 • Sensitivity Analysis in Decision Trees 605
The Value of Information 606
Decisions with Sample Information 607 * Bayes s Rule 608
Utility and Decision Making 609
Constructing a Utility Function 611 • Exponential Utility Functions 614
Key Terms 616 • Problems and Exercises 616 • Case: Performance Lawn
Equipment 622
Appendix A 625
Appendix B 629
Glossary 653
Index 659
|
| any_adam_object | 1 |
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| discipline | Wirtschaftswissenschaften |
| edition | Internat. ed. |
| format | Book |
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| spelling | Evans, James R. 1950- Verfasser (DE-588)170321924 aut Business analytics methods, models, and decisions James R. Evans Internat. ed. Boston, Mass. [u.a.] Pearson 2013 XIX, 665 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Always learning Betriebliches Informationssystem (DE-588)4069386-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf Datenmanagement (DE-588)4213132-7 gnd rswk-swf Betriebsdaten (DE-588)4145038-3 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Unternehmen (DE-588)4061963-1 s Betriebsdaten (DE-588)4145038-3 s Datenanalyse (DE-588)4123037-1 s DE-604 Betriebliches Informationssystem (DE-588)4069386-7 s Datenmanagement (DE-588)4213132-7 s 2\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024752876&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Evans, James R. 1950- Business analytics methods, models, and decisions Betriebliches Informationssystem (DE-588)4069386-7 gnd Datenanalyse (DE-588)4123037-1 gnd Unternehmen (DE-588)4061963-1 gnd Datenmanagement (DE-588)4213132-7 gnd Betriebsdaten (DE-588)4145038-3 gnd |
| subject_GND | (DE-588)4069386-7 (DE-588)4123037-1 (DE-588)4061963-1 (DE-588)4213132-7 (DE-588)4145038-3 (DE-588)4123623-3 |
| title | Business analytics methods, models, and decisions |
| title_auth | Business analytics methods, models, and decisions |
| title_exact_search | Business analytics methods, models, and decisions |
| title_full | Business analytics methods, models, and decisions James R. Evans |
| title_fullStr | Business analytics methods, models, and decisions James R. Evans |
| title_full_unstemmed | Business analytics methods, models, and decisions James R. Evans |
| title_short | Business analytics |
| title_sort | business analytics methods models and decisions |
| title_sub | methods, models, and decisions |
| topic | Betriebliches Informationssystem (DE-588)4069386-7 gnd Datenanalyse (DE-588)4123037-1 gnd Unternehmen (DE-588)4061963-1 gnd Datenmanagement (DE-588)4213132-7 gnd Betriebsdaten (DE-588)4145038-3 gnd |
| topic_facet | Betriebliches Informationssystem Datenanalyse Unternehmen Datenmanagement Betriebsdaten Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024752876&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT evansjamesr businessanalyticsmethodsmodelsanddecisions |