Description

Book Synopsis

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery
A comprehensive introduction to statistical methods for data mining and knowledge discovery.
Extensivesolutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.



Table of Contents

Preface vii

1 Data analytics and data mining 1

2 Basic probability and statistical distributions 3

3 Data manipulation 14

4 Data visualization and statistical graphics 28

5 Statistical inference 45

6 Techniques for supervised learning: simple linear regression 65

7 Techniques for supervised learning: multiple linear regression 90

8 Supervised learning: generalized linear models 134

9 Supervised learning: classification 154

10 Techniques for unsupervised learning: dimension reduction 185

11 Techniques for unsupervised learning: clustering and association 200

References 216

Statistical Data Analytics

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    A Paperback / softback by Walter W. Piegorsch

    £16.95

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      Book details

      Publisher John Wiley & Sons Inc
      Published 18 December 2015
      ISBN-13 9781119030652
      978-1119030652
      ISBN-10 111903065X
      Also in:
      Data mining Mathematics

      Description

      Book Synopsis

      Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery
      A comprehensive introduction to statistical methods for data mining and knowledge discovery.
      Extensivesolutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.



      Table of Contents

      Preface vii

      1 Data analytics and data mining 1

      2 Basic probability and statistical distributions 3

      3 Data manipulation 14

      4 Data visualization and statistical graphics 28

      5 Statistical inference 45

      6 Techniques for supervised learning: simple linear regression 65

      7 Techniques for supervised learning: multiple linear regression 90

      8 Supervised learning: generalized linear models 134

      9 Supervised learning: classification 154

      10 Techniques for unsupervised learning: dimension reduction 185

      11 Techniques for unsupervised learning: clustering and association 200

      References 216

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