Statistical Regression and Classification: From Linear Models to Machine Learning PDF (Adobe DRM) download by Norman Matloff

Statistical Regression and Classification: From Linear Models to Machine Learning

Chapman and Hall/CRC
Publication date: September 2017
ISBN: 9781498710923
Digital Book format: PDF (Adobe DRM)

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Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. 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. 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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Statistical Regression and Classification: From Linear Models to Machine Learning ePub (Adobe DRM) download by Norman Matloff
Chapman and Hall/CRC, September 2017
ISBN: 9781351645898
EAN:  9781351645898
ePub (Adobe DRM)
List Price: $69.95 eBook price: $64.99
Statistical Regression and Classification: From Linear Models to Machine Learning PDF (Adobe DRM) download by Norman Matloff
Chapman and Hall/CRC, September 2017
ISBN: 9781498710923
EAN:  9781498710923
PDF (Adobe DRM)
List Price: $69.95 eBook price: $64.99
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