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Applied predictive Modeling

By: By: Publication details: New York: Springer, 2013Description: 600ISBN:
  • 9781493979363
Subject(s): DDC classification:
  • 519.5 KUH
Summary: This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book Alliance College of Engineering and Design Basic Science 519.5 KUH (Browse shelf(Opens below)) Available E15569
Book Book Alliance College of Engineering and Design Basic Science 519.5 KUH (Browse shelf(Opens below)) Available E15568
Reference Book Reference Book Alliance College of Engineering and Design Basic Science 519.5 KUH (Browse shelf(Opens below)) Not for loan E15565
Book Book Alliance College of Engineering and Design Basic Science 519.5 KUH (Browse shelf(Opens below)) Available E15566
Book Book Alliance College of Engineering and Design Basic Science 519.5 KUH (Browse shelf(Opens below)) Available E15567
Total holds: 0

This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package.
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

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