The Resource Statistical regression and classification : from linear models to machine learning, Norman Matloff
Statistical regression and classification : from linear models to machine learning, Norman Matloff
Resource Information
The item Statistical regression and classification : from linear models to machine learning, Norman Matloff represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Statistical regression and classification : from linear models to machine learning, Norman Matloff represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
 Summary
 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. 
 Language
 eng
 Extent
 xxxviii, 489 pages
 Isbn
 9781498710916
 Label
 Statistical regression and classification : from linear models to machine learning
 Title
 Statistical regression and classification
 Title remainder
 from linear models to machine learning
 Statement of responsibility
 Norman Matloff
 Language
 eng
 Summary
 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. 
 Assigning source
 Provided by publisher
 Cataloging source
 DLC
 http://library.link/vocab/creatorName
 Matloff, Norman S
 Dewey number
 519.5/36
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA278.2
 LC item number
 .M377 2017
 Literary form
 non fiction
 Nature of contents
 bibliography
 Series statement
 Chapman & Hall/CRC: Texts in Statistical Science Series
 http://library.link/vocab/subjectName

 Regression analysis
 Vector analysis
 Regression analysis
 Vector analysis
 Automatische Klassifikation
 Lineare Regression
 Label
 Statistical regression and classification : from linear models to machine learning, Norman Matloff
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Control code
 975370313
 Dimensions
 25 cm.
 Extent
 xxxviii, 489 pages
 Isbn
 9781498710916
 Lccn
 2017011270
 Media category
 unmediated
 Media MARC source
 rdamedia
 Media type code

 n
 System control number
 (OCoLC)975370313
 Label
 Statistical regression and classification : from linear models to machine learning, Norman Matloff
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Control code
 975370313
 Dimensions
 25 cm.
 Extent
 xxxviii, 489 pages
 Isbn
 9781498710916
 Lccn
 2017011270
 Media category
 unmediated
 Media MARC source
 rdamedia
 Media type code

 n
 System control number
 (OCoLC)975370313
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