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 European University Institute.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 European University Institute.
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
- http://library.link/vocab/creatorName
- Matloff, Norman S
- Illustrations
- illustrations
- Index
- index present
- 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
- 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
- ocn975370313
- Dimensions
- 25 cm.
- Extent
- xxxviii, 489 pages
- Isbn
- 9781498710916
- 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
- ocn975370313
- Dimensions
- 25 cm.
- Extent
- xxxviii, 489 pages
- Isbn
- 9781498710916
- Media category
- unmediated
- Media MARC source
- rdamedia
- Media type code
-
- n
- System control number
- (OCoLC)975370313
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Statistical-regression-and-classification--from/Ji7KyeAxIIM/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Statistical-regression-and-classification--from/Ji7KyeAxIIM/">Statistical regression and classification : from linear models to machine learning, Norman Matloff</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Statistical-regression-and-classification--from/Ji7KyeAxIIM/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Statistical-regression-and-classification--from/Ji7KyeAxIIM/">Statistical regression and classification : from linear models to machine learning, Norman Matloff</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
