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.
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A comprehensive introduction to statistical methods for data mining and knowledge discovery.
Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced.
Extensive examples 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.
Statistical Data Analytics:
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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