Statistical Data Analytics Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual

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.

Walter W. Piegorsch, BIO5 Institute, University of Arizona, Tucson, AZ, USA is the current Editor-in-Chief of the journal Environmetrics and a previous Chairman of the American Statistical Association Section on Statistics and the Environment. Piegorsch is also an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He previously served as Joint-Editor of the Journal of the American Statistical Association and on the Board of Scientific Counselors for the U.S. National Toxicology Program.

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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