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.
Table des matières
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
A propos de l’auteur
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.
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