Statistical Regression and Classification: From Linear Models to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Matloff, Norman

ISBN 10: 1498710913 / ISBN 13: 9781498710916

Verlag: Chapman and Hall/CRC, 2017

Beschreibung

*Price HAS BEEN REDUCED by 10% until Monday, June 16 (weekend SALE item)* 532 pp., paperback, covers slightly edge worn, else very good. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Bestandsnummer des Verkäufers ZB1317787

Bibliografische Details

Titel: Statistical Regression and Classification: ...

Verlag: Chapman and Hall/CRC

Erscheinungsdatum: 2017

Einband: Softcover

Zustand: Very Good

Über diesen Titel

Inhaltsangabe:

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.

Über die Autorin bzw. den Autor:

Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. Statistical Regression and Classification: From Linear Models to Machine Learning was awarded the 2017 Ziegel Award for the best book reviewed in Technometrics in 2017. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.