Cladag 2017 Book of Short Papers

Cladag 2017 Book of Short Papers

Author: Francesca Greselin

Publisher: Universitas Studiorum

ISBN: 9788899459710

Category: Mathematics

Page: 698

View: 636

This book is the collection of the Abstract / Short Papers submitted by the authors of the International Conference of The CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Milan (Italy) on September 13-15, 2017.

An Introduction to Statistical Learning

An Introduction to Statistical Learning

Author: Gareth James

Publisher: Springer Science & Business Media

ISBN: 9781461471387

Category: Mathematics

Page: 426

View: 552

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

R Fundamentals & Graphics

R Fundamentals & Graphics

Author: RMS Books

Publisher: RMS BOOKS

ISBN:

Category: Computers

Page: 400

View: 509

This book is the first of the three volume set. Volume 1 provides you with a solid understanding of the fundamental concepts of R and the confidence to use its powerful graphic features. No background knowledge of R is required. It takes you step by step from using R to perform statistical tests to generating advanced graphics. The book includes over 400 examples and illustrations using base R, ggplot2, gplots, lattice, rgl, Rcmdr, scatterplot3d, grid, mvtsplot, ggally, car and other R packages. An assessment chapter is included at the end of the book to apply the concepts you have learned. Volume 2, available in summer 2014, will include data exploration, data restructuring and data cleaning. Volume 3 will deal with data analysis.

Auto Data Book

Auto Data Book

Author: Edwin Hess

Publisher: Legare Street Press

ISBN: 1017728569

Category: Technology & Engineering

Page: 0

View: 174

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

Statistics with Mathematica

Statistics with Mathematica

Author: Martha L. Abell

Publisher: Academic Press

ISBN: 0120415542

Category: Mathematics

Page: 654

View: 356

Covers the use of Mathematica for applications ranging from descriptive statistics, through multiple regression and nonparametric methods; uses virtually all of Mathematica's built-in statistical commands, as well as those contained in various Mathematica packages; Additionally, the authors have written numerous procedures to extend Mathematica's capabilities, which are also included on the CD-ROM