Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look..."> Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look..."/>
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Statistical regression and classification : from linear models to machine learning

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.rnrnThe 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.

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Location name is not set (5B) 519.536 MAT S
2019-0262
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Series Title
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Call Number
519.536 MAT S
Publisher
: .,
Collation
xxxviii, 489 hlm. : ilus. , 24 cm.
Language
English
ISBN/ISSN
978-1-4987-1091-6
Classification
519.536
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
-
Specific Detail Info
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Statement of Responsibility
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