[ Statistical Regression and Classification: From Linear Models to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)] E–pub Ä Norman Matloff


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

Norman Matloff ò 6 eview

Ssumes that the eader is a programmer and thus will find code examples a useful form of explication You probably want to Be At Least A Journeyman at least a journeyman R programmer in order for this to be helpful for you The treatment of the classical assumptions in linear egression is uite intuitive and highlights the practical importance or lack thereof The production seems a bit hurried The computer code is not well typeset and there are some typos and other errors The author has been very esponsive code is not well typeset and there are some typos and other errors The author has been very Will Gallows and the Snake-Bellied Troll responsive is currently putting together some errata on his web page so I suggest checking that for updates Search for matloffegclassThe copy I got had some printing issues color illustrations were in B W but the author and publisher checked other copies and they Tioned 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 eal data The book treats classical egression 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 Snuggle Up, Little One: A Treasury of Bedtime Stories regression methods The author is just as interested in Description applications ofegression such as measuring the gender wage gap in Silicon Valley as in forecasting tomorrow's demand for bike entals An entire chapter is devoted to measuring such effects including discussio. Excellent book Well structured a lot of code math and practical examples Better than similar books in the market I bought this book sight unseen just and practical examples Better than similar books in the market I bought this book sight unseen just I had a very favorable impression of the author s prior work Art of R Programming This is what I found Although the R language is not explicitly mentioned in the book s title or SUBTITLE THE BOOK DOES MAKE HEAVY USE OF R the book does make heavy use of R and examples The book is organized in what I would call a multi pass approach Instead of building up the concepts in order of dependency it first gives an overview of the space with plenty of forward eferences and then goes back and fills in the details You may or may not like this approach but given the choice to do it this way I think it s well executed The book Statistical Regression and Classification From Linear Models to Machine Learning takes an innovative look at the traditional statistical egression course presenting a contemporary treatment in line with today's applications and users The text takes a modern look at egression 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 parti. On t have this problem so it Seems I Just Got A Lemon And I just got a lemon and will probably be fine Check your book for misprints when you get itOverall a worthwhile ead Content wise the book seems to be okay but the uality of the ebook is horrendous Code snippets are blurry screenshots and the euations are incorrectly typeset ie hats bars and tildas are next to the characters they should be above characters that should be subscripts and superscripts are simply egular characters eg 0 where 0 should be a subscript I m generally pretty forgiving when it comes to poor ebook formatting and typesetting but I think improperly typeset mathematical notation is a deal breaker I will consider buying the print version but only after I Ve Had A Chance Check Its Uality. N ve had a chance check its uality. N Simpson's Paradox multiple inference and causation issues Similarly there is an entire chapter of parametric model fit making use of both esidual 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 Publishing Women: Salons, the Presses, and the Counter-Reformation in Sixteenth-Century Italy research focus is onecommender systems and applications of egression methods to
SMALL AREA ESTIMATION AND BIAS REDUCTION 
area estimation and bias eduction 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 CUD.