Statistical Data Analytics
Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual
Published on 1. July 2015
232 pages
978-1-119-04363-8 (ISBN)
Description
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.
More details
Other editions
Additional editions
Walter W. Piegorsch
Statistical Data Analytics
Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual
Book
12/2015
1st Edition
Wiley
€20.99
Shipment within 15-20 days
Walter W. Piegorsch
Statistical Data Analytics
Foundations for Data Mining, Informatics, and Knowledge Discovery
Book
08/2015
Wiley
€110.50
Article not available at the moment
Person
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.
Content
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
