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.

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

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.