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06100nam a22004093i 4500 |
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EBC7103844 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20221031135401.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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221028s2013 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781118593745 |
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(electronic bk.) |
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| Cancelled/invalid ISBN |
9781118447147 |
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| System control number |
(MiAaPQ)EBC7103844 |
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| System control number |
(Au-PeEL)EBL7103844 |
| 040 ## - CATALOGING SOURCE |
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MiAaPQ |
| Language of cataloging |
eng |
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rda |
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pn |
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MiAaPQ |
| Modifying agency |
MiAaPQ |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ledolter, Johannes. |
| 245 10 - TITLE STATEMENT |
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| Title |
Data Mining and Business Analytics with R. |
| 264 #1 - |
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Newark : |
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John Wiley & Sons, Incorporated, |
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2013. |
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�2013. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (365 pages) |
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text |
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txt |
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rdacontent |
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computer |
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c |
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rdamedia |
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online resource |
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cr |
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rdacarrier |
| 490 1# - SERIES STATEMENT |
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| Series statement |
New York Academy of Sciences Ser. |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Intro -- DATA MINING AND BUSINESS ANALYTICS WITH R -- CONTENTS -- Preface -- Acknowledgments -- 1. Introduction -- Reference -- 2. Processing the Information and Getting to Know Your Data -- 2.1 Example 1: 2006 Birth Data -- 2.2 Example 2: Alumni Donations -- 2.3 Example 3: Orange Juice -- References -- 3. Standard Linear Regression -- 3.1 Estimation in R -- 3.2 Example 1: Fuel Efficiency of Automobiles -- 3.3 Example 2: Toyota Used-Car Prices -- Appendix 3.A The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction -- References -- 4. Local Polynomial Regression: a Nonparametric Regression Approach -- 4.1 Model Selection -- 4.2 Application to Density Estimation and the Smoothing of Histograms -- 4.3 Extension to the Multiple Regression Model -- 4.4 Examples and Software -- References -- 5. Importance of Parsimony in Statistical Modeling -- 5.1 How Do We Guard Against False Discovery -- References -- 6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) -- 6.1 Example 1: Prostate Cancer -- 6.2 Example 2: Orange Juice -- References -- 7. Logistic Regression -- 7.1 Building a Linear Model for Binary Response Data -- 7.2 Interpretation of the Regression Coefficients in a Logistic Regression Model -- 7.3 Statistical Inference -- 7.4 Classification of New Cases -- 7.5 Estimation in R -- 7.6 Example 1: Death Penalty Data -- 7.7 Example 2: Delayed Airplanes -- 7.8 Example 3: Loan Acceptance -- 7.9 Example 4: German Credit Data -- References -- 8. Binary Classification, Probabilities, and Evaluating Classification Performance -- 8.1 Binary Classification -- 8.2 Using Probabilities to Make Decisions -- 8.3 Sensitivity and Specificity -- 8.4 Example: German Credit Data -- 9. Classification Using a Nearest Neighbor Analysis -- 9.1 The k-Nearest Neighbor Algorithm. |
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| Formatted contents note |
9.2 Example 1: Forensic Glass -- 9.3 Example 2: German Credit Data -- Reference -- 10. The Na�ive Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables -- 10.1 Example: Delayed Airplanes -- Reference -- 11. Multinomial Logistic Regression -- 11.1 Computer Software -- 11.2 Example 1: Forensic Glass -- 11.3 Example 2: Forensic Glass Revisited -- Appendix 11.A Specification of a Simple Triplet Matrix -- References -- 12. More on Classification and a Discussion on Discriminant Analysis -- 12.1 Fisher's Linear Discriminant Function -- 12.2 Example 1: German Credit Data -- 12.3 Example 2: Fisher Iris Data -- 12.4 Example 3: Forensic Glass Data -- 12.5 Example 4: MBA Admission Data -- Reference -- 13. Decision Trees -- 13.1 Example 1: Prostate Cancer -- 13.2 Example 2: Motorcycle Acceleration -- 13.3 Example 3: Fisher Iris Data Revisited -- 14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods -- 14.1 R Packages for Tree Construction -- 14.2 Chi-Square Automatic Interaction Detection (CHAID) -- 14.3 Ensemble Methods: Bagging, Boosting, and Random Forests -- 14.4 Support Vector Machines (SVM) -- 14.5 Neural Networks -- 14.6 The R Package Rattle: A Useful Graphical User Interface for Data Mining -- References -- 15. Clustering -- 15.1 k-Means Clustering -- 15.2 Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions -- 15.3 Hierarchical Clustering Procedures -- References -- 16. Market Basket Analysis: Association Rules and Lift -- 16.1 Example 1: Online Radio -- 16.2 Example 2: Predicting Income -- References -- 17. Dimension Reduction: Factor Models and Principal Components -- 17.1 Example 1: European Protein Consumption -- 17.2 Example 2: Monthly US Unemployment Rates. |
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| Formatted contents note |
18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares -- 18.1 Three Examples -- References -- 19. Text as Data: Text Mining and Sentiment Analysis -- 19.1 Inverse Multinomial Logistic Regression -- 19.2 Example 1: Restaurant Reviews -- 19.3 Example 2: Political Sentiment -- Appendix 19.A Relationship Between the Gentzkow Shapiro Estimate of "Slant" and Partial Least Squares -- References -- 20. Network Data -- 20.1 Example 1: Marriage and Power in Fifteenth Century Florence -- 20.2 Example 2: Connections in a Friendship Network -- References -- Appendix A: Exercises -- Exercise 1 -- Exercise 2 -- Exercise 3 -- Exercise 4 -- Exercise 5 -- Exercise 6 -- Exercise 7 -- Appendix B: References -- Index. |
| 588 ## - |
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Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) |
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| Local note |
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 655 #4 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Electronic books. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Display text |
Print version: |
| Main entry heading |
Ledolter, Johannes |
| Title |
Data Mining and Business Analytics with R |
| Place, publisher, and date of publication |
Newark : John Wiley & Sons, Incorporated,c2013 |
| International Standard Book Number |
9781118447147 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) |
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| Corporate name or jurisdiction name as entry element |
ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
New York Academy of Sciences Ser. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://ebookcentral.proquest.com/lib/kliuc-ebooks/detail.action?docID=7103844 |
| Public note |
Click to View |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Koha item type |
E-book |
