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Statistical Regression and Classification
From Linear Models to Machine Learning
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Statistical Regression and Classification
$37600
Unit price
/
Unavailable
Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?
This text is ideal for students and practitioners in data science and statistics who seek a practical yet modern approach to regression and classification, especially those using R. It suits both beginners wanting to avoid intensive mathematical details and more advanced readers desiring in-depth analytical content.
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.
Book Hero Magic formatted this description to make it easier to read. While it's new and
still learning, it may not be perfect - your
feedback is welcome!
Description
Statistical Regression and Classification 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 mathematical statistics very sparingly and always in the context of something concrete. This means that readers can skip the mathematical statistics content entirely if they wish.
The extras section is for those who feel comfortable with analysis using mathematical statistics.
Book Details
INFORMATION
ISBN: 9781138066465
Publisher: Taylor & Francis Ltd
Format: Hardback
Date Published: 20 July 2017
Country: United Kingdom
Imprint: CRC Press
Audience: Tertiary education, Professional and scholarly
DIMENSIONS
Width: 156.0mm
Height: 234.0mm
Weight: 1140g
Pages: 532
About the Author
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.
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