Predictive analytics for business using R:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
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New Jersey
World Scientific
[2025]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxiv, 439 Seiten Diagramme |
| ISBN: | 9789811293771 |
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MARC
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Contents Preface vii About the Author xi Acknowledgments xiii Supplementary Materials xv Part I: Getting Started 1. 3 Introduction 1.1 1.2 1.3 1.4 1.5 2. 1 What Is Analytics? . The INFORMS AnalyticsFramework. 1.2.1 Predictive analytics in the analytics framework. 8 Predictive Analytics Objectives and Paradigms . Distinguishing Predictive Analytics and Data Mining. Text Structure and Strategy. The R Programming Environment 2.1 2.2 2.3 Arithmetic in R . Variables in R. Data Frames in R. 2.3.1 Key operations with dataframes. xvii 4 5 9 13 13 15 16 20 21 22
Predictive Analytics for Pu no s l’ viy Ji xviii 2.4 2.5 2.6 2.7 Functions in R. 24 Script Files in R and the RStudio Integrated Development Environment . 27 2.5.1 R analysis strategy u ing functions and script files. 29 Key Descriptive Statistics Tools in R . 30 2.6.1 Data summaries in R: Importing an Excel data file. 30 2.6.2 Data summaries in R: Summary statistics. 33 2.6.3 Data summaries in R: Graphical summaries. 35 R Code Used in This Chapter . 42 Part II: Predicting a Number 15 Overview 47 3. Linear Regression 3.1 Correlation and Simple Linear Regression. 3.1.1 Correlation and computing an estimate for p. 50 3.1.2 Kinds of correlation. 3.1.3 Simple linear regression model form 3.1.4 Estimating model coefficients (5s and σ). 3.1.5 Simple linear regression model performance. 3.1.6 Regression in the traditional research paradigm. 3.1.7 Simple linear regression model validity checks. 3.1.8 Hypothesis testing for overall model and individual
coefficients. 3.1.9 Prediction and prediction uncertainty for simple linear regression. 3.1.10 Managerial interpretation for simple linear regression. 49 49 55 56 57 60 62 6)2 65 67 69
Contents 3.2 3.3 3.4 3.5 3.6 xix Multiple Linear Regression. 71 3.2.1 Multiple regression model form. 71 3.2.2 Estimating model coefficients (Bs) and σ. 72 3.2.3 Multiple linear regression model performance. 75 3.2.4 Multiple regression model validity checks. 77 3.2.5 Hypothesis testing for overall modeland individual coefficients. 78 3.2.6 Prediction and prediction uncertainty for multiple linear regression. 81 3.2.7 Managerial interpretation for multiple linear regression: The house price study. 83 Special Issues in Linear Regression. 85 3.3.1 Nonlinear transformations of У. 85 3.3.2 Transforming x and adding nonlinear g terms to the model. 91 3.3.3 Scaling predictor variables to ±1. 96 3.3.4 Going beyond scaling: Making model terms orthogonal. 102 3.3.5 Handling qualitative independent variables.105 3.3.6 Multiple regression with observational or cross-sectional data. 113 3.3.7 Designing experiments to fit multiple regression models. 125 The Overall Process for Linear Regression in the Traditional Research
Paradigm.138 The General Linear Model and the Generalized Linear Model. 139 3.5.1 The general linear model. 139 3.5.2 The generalized linear model.141 3.5.3 Fitting and prediction for GLIMs in R: Titnc-in-system example. 142 The Overall Process for Generalized Linear Models in the Traditional Research Paradigm . . . 147
Predictive Analytics for business L'^ing II XX 3.7 3.8 4. Regression Under the Data-Adaptive Research Paradigm 4.1 4.2 4.3 4.4 4.5 5. Cases Supporting Chapter 3 . 147 R Code Used in Chapter 3. 148 Nontraditional Regression (Machine Learning) 5.1 5.2 5.3 159 Cross-Validation for Fitting and Characterizing Regression Prediction Error. 159 4.1.1 Train-and-test root mean squared error for error characterization. IGO 4.1.2 k-fold CV.162 4.1.3 Alternatives to k-fold C\r .101 Using k-Fold CV in R via caret. 10-1 The Overall Process for Linear Regression Under a Data-Adaptive Research Paradigm. 170 Case Supporting Chapters 4 and 5. 171 R Code Used in Chapter 4. 171 175 Machine Learning Concepts and Methods. 175 Spatial Correlation (Gaussian Process) Regression . 177 5.2.1 History of spatial correlation modeling. 178 5.2.2 The intuition behind spatial correlation prediction. 178 5.2.3 Form of the GP model. 180 5.2.4 GP regression using R. 183 5.2.5 Life-cycle savings GP model and
prediction.189 5.2.6 Strengths and weaknesses of GP regression. 193 Neural Network Regression. 195 5.3.1 History of neural network modeling. 196 5.3.2 The intuition behind neural network prediction. 196 5.3.3 Form of the neural network model.197 5.3.4 Choosing weights for the neural network model.199
Contents xxi 5.3.5 5.3.6 5.4 5.5 5.6 Neural network regression using R 200 Life-cycle savings neural network model and prediction. 204 5.3.7 Strengths and weaknesses of neural network regression . 208 The Overall Process for Machine Learning Predictive Analytics. 208 Case Supporting Chapters 4 and 5. 209 R Code Used in Chapter 5. 210 Part III: Predicting a Class 221 Overview 223 6. Classification for Predictive Analytics 6.1 6.2 6.3 6.4 6.5 225 Classification Purpose and Performance. 225 6.1.1 Data mining vs. predictive analytics and classification. 227 6.1.2 Classification errors. 228 6.1.3 Classification performance. 229 6.1.4 The HOC curve. 231 Simple Classification Methods . 234 6.2.1 Linear discriminant analysis. 234 6.2.2 k-nearest neighbors. 236 Complex Classification Methods. 238 6.3.1 Neural network classifiers . 238 6.3.2 Logistic regression classifiers. 238 Example Classification Using R and caret.240 6.4.1 Airline satisfaction data. 241 6.4.2 Graphical summaries. 242 6.4.3 Implementing
knn. Ida. nnet, and glm via caret. 246 6.4.4 Performance summaries in R. 255 6.4.5 Generating a prediction using a classifier. 257 Classification with Data Imbalance. 259
Predictive Analytics for Нинп'» I ina It xxii 6.6 6.7 6.8 The Overall Process for Cla»ificati m Predictive Analytics. 262 Case Supporting Chapter 6. 262 R. Code Used in Chapter 6. 263 Part IV: Predicting Dynamic Behavior 275 Overview 277 7. Trendline and Time Series Analysis for Forecasting 279 Trendline Models. 2sl 7.1.1 Linear trendline models . 2M 7.1.2 Nonlinear trendline models. 2s2 7.1.3 The overall process for trendline regression in the traditional nsearh paradigm. 2 2 7.2 Trendline Model in R: Weekly Product Order Volume. 283 7.3 Trendline Model Limitations. 296 7.4 Time Series Data Characteristics. 291 7.5 Time Series Models. 295 7.5.1 Rule-of-thumb forecast methods. 295 7.5.2 The MA probability model. 297 7.5.3 The autoregressive probability model . . . 298 7.5.4 ARMA and ARIMA models. 299 7.6 Time Series Model Prediction Performance and Validity Checks. 300 7.7 Plots and Statistical Summaries to Help in Model Selection
.302 7.8 The Overall Process for Time Series Predictive Analytics. 303 7.9 Time Series Models in R: Drug Sales Data . 304 7.9.1 Multiplicative Holt-Winters model . 308 7.9.2 Seasonal plus trend ARIMA model . . . .312 7.10 Cases Supporting Chapter 7 . 317 7.11 R Code Used in Chapter 7. 317 7.1
Contents 8. Simulating Random Values 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9. 323 Probability Distribution Parametric Families . . . 324 8.1.1 Normal or Gaussian distribution family. 325 8.1.2 Exponential distribution family. 325 8.1.3 Uniform distribution family. 326 8.1.4 Gamma distribution family . 326 8.1.5 Poisson distribution family. 327 8.1.6 Binomial distribution family. 328 Choosing and Fitting a Distribution. 329 8.2.1 Choosing the distribution family based on the nature of the random variable. 329 8.2.2 Choosing the distribution family based on the fit to historical data. 331 8.2.3 Fitting the parametric distribution: Parameter estimation. 333 8.2.4 Checking the fidelity of the fitted parametric distribution. 334 8.2.5 A third way: Use the sample empirical distribution. 334 Random Variate Generation .336 8.3.1 Generating an exponential random variate. 336 Random Variate Generation in R.337 Validating the RV Generator.338 The Overall Random Variate Simulation Process. 338 Example RV Fitting and
Generation in R. 339 Case Supporting Chapter 8.348 R Code Used in Chapter 8.348 Building Discrete-Event Simulation Models 9.1 9.2 xxiii 353 What Is Discrete-Event Simulation?. 354 Discrete-Event Simulation ModelingParadigms and Software.361 9.2.1 Event-scheduling paradigm. 361 9.2.2 Process-interaction paradigm . 362 9.2.3 Agent-based paradigm. 364
Predictive Analytics for Pusintss I'siny P xxiv Modeling vs. execution paradigmsЖ Continuous vs. discrete-event simulation.3G5 Basic Flow of a Simulation Model. 36G Components for a Process-Interaction Simulation Model. 367 Simulation in R Using simmer. 3G The Overall Process for Simulation Model Building.372 Case Supporting Chapters 9 and 10. 373 R Code Used in Chapter 9. 373 9.2.4 9.2.5 9.3 9.4 9.5 9.6 9.7 9.8 10. Prediction with Discrete-Event Simulation Models 377 The simmer Bank Simulation. 377 Kinds of Simulation Output. 3M) Simulation Run Control. 3S3 10.3.1 Initialization bias for steady-state simulations . 3M 10.3.2 Run length for steady-state simulations . 38^ 10.3.3 Determining the number of simulation replications . 3M 10.3.4 Using “common random numbers" in simulations. 392 10.4 Predictive Analytics for a Single System. 393 10.5 Predictive Analytics for Two Systems. 396 10.6 Predictive Analytics for Multiple Systems. 397 10.7 Predictive Analytics
Regression. 401 10.8 Benefits of Discrete-Event Simulation. 409 10.9 Case Supporting Chapters 9 and 10. 409 10.10 R Code Used in Chapter 10. 409 10.1 10.2 10.3 Appendix A. INFORMS CAP Job Task Analysis 425 References 429 Index 435 |
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| author | Barton, Russell R. |
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| spelling | Barton, Russell R. Verfasser (DE-588)1068118318 aut Predictive analytics for business using R Russell R. Barton, The Pennsylvania State University, USA New Jersey World Scientific [2025] © 2025 xxiv, 439 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Management / Statistical methods Predictive analytics R (Computer program language) Gestion / Méthodes statistiques R (Langage de programmation) Management (DE-588)4037278-9 s Entscheidungsfindung (DE-588)4113446-1 s Datenanalyse (DE-588)4123037-1 s Prognose (DE-588)4047390-9 s R Programm (DE-588)4705956-4 s b DE-604 Erscheint auch als Online-Ausgabe 978-981-1293-78-8 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035252190&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Barton, Russell R. Predictive analytics for business using R Entscheidungsfindung (DE-588)4113446-1 gnd Management (DE-588)4037278-9 gnd R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Prognose (DE-588)4047390-9 gnd |
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| title | Predictive analytics for business using R |
| title_auth | Predictive analytics for business using R |
| title_exact_search | Predictive analytics for business using R |
| title_full | Predictive analytics for business using R Russell R. Barton, The Pennsylvania State University, USA |
| title_fullStr | Predictive analytics for business using R Russell R. Barton, The Pennsylvania State University, USA |
| title_full_unstemmed | Predictive analytics for business using R Russell R. Barton, The Pennsylvania State University, USA |
| title_short | Predictive analytics for business using R |
| title_sort | predictive analytics for business using r |
| topic | Entscheidungsfindung (DE-588)4113446-1 gnd Management (DE-588)4037278-9 gnd R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Prognose (DE-588)4047390-9 gnd |
| topic_facet | Entscheidungsfindung Management R Programm Datenanalyse Prognose |
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