Statistical Regression and Classification (Paperback)

Norman Matloff

ISBN 10: 1498710913 / ISBN 13: 9781498710916

Verlag: Taylor & Francis Inc, Portland, 2017

Beschreibung

Paperback. 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. This text provides a modern introduction to regression and classification with an emphasis on big data and R. The main body uses math stat sparingly and always in the context of something concrete; readers can skip the math stat content entirely if they wish. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9781498710916

Bibliografische Details

Titel: Statistical Regression and Classification (...

Verlag: Taylor & Francis Inc, Portland

Erscheinungsdatum: 2017

Einband: Paperback

Zustand: new

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.