W Piegorsch
John Wiley & Sons
e druk, 2015
9781119030652
Statistical Data Analytics – Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual
Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual
Specificaties
Paperback, 232 blz.
|
Engels
John Wiley & Sons |
e druk, 2015
ISBN13: 9781119030652
Rubricering
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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.
Extensive solutions 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.
Specificaties
Inhoudsopgave
<p>Preface vii</p>
<p>1 Data analytics and data mining 1</p>
<p>2 Basic probability and statistical distributions 3</p>
<p>3 Data manipulation 14</p>
<p>4 Data visualization and statistical graphics 28</p>
<p>5 Statistical inference 45</p>
<p>6 Techniques for supervised learning: simple linear regression 65</p>
<p>7 Techniques for supervised learning: multiple linear regression 90</p>
<p>8 Supervised learning: generalized linear models 134</p>
<p>9 Supervised learning: classification 154</p>
<p>10 Techniques for unsupervised learning: dimension reduction 185</p>
<p>11 Techniques for unsupervised learning: clustering and association 200</p>
<p>References 216</p>
<p>1 Data analytics and data mining 1</p>
<p>2 Basic probability and statistical distributions 3</p>
<p>3 Data manipulation 14</p>
<p>4 Data visualization and statistical graphics 28</p>
<p>5 Statistical inference 45</p>
<p>6 Techniques for supervised learning: simple linear regression 65</p>
<p>7 Techniques for supervised learning: multiple linear regression 90</p>
<p>8 Supervised learning: generalized linear models 134</p>
<p>9 Supervised learning: classification 154</p>
<p>10 Techniques for unsupervised learning: dimension reduction 185</p>
<p>11 Techniques for unsupervised learning: clustering and association 200</p>
<p>References 216</p>