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

By:

Sign Up Now! Already a Member? Log In
You must be logged into Bookshare to access this title. Learn about membership options, or view our freely available titles.

Synopsis

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 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.

Copyright:
2017

Book Details

Book Quality:
Publisher Quality
Book Size:
490 Pages
ISBN-13:
9781351645898
Related ISBNs:
9781498710916, 9781138066465, 9781315119588, 9781498710923
Publisher:
CRC Press
Date of Addition:
Copyrighted By:
Taylor & Francis Group, LLC
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Business and Finance, Mathematics and Statistics
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

Reviews

No Rating Yet