A Proportional Odds Model of Particle Pollution

A linear regression model of particle pollution and an ordered logistic regression model of the relevant index were selected for observations in the US city of Los Angeles, California. Models were used to forecast Air Quality Index (AQI) from a sample, and were compared and contrasted. Methods are c...

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Main Authors:	Justin R. Chimka, Ege Ozdemir
Format:	Article
Language:	English
Published:	MDPI AG 2014-08-01
Series:	Environments
Subjects:	air quality index particle pollution linear regression ordered logistic regression
Online Access:	http://www.mdpi.com/2076-3298/1/1/54

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spelling	doaj-bfb9612120a949ffa38ed975e44f80cc2020-11-25T00:47:45ZengMDPI AGEnvironments2076-32982014-08-0111545910.3390/environments1010054environments1010054A Proportional Odds Model of Particle PollutionJustin R. Chimka0Ege Ozdemir1Department of Industrial Engineering, University of Arkansas, 800 W. Dickson St., Fayetteville, AR 72701, USADepartment of Industrial Engineering, University of Arkansas, 800 W. Dickson St., Fayetteville, AR 72701, USAA linear regression model of particle pollution and an ordered logistic regression model of the relevant index were selected for observations in the US city of Los Angeles, California. Models were used to forecast Air Quality Index (AQI) from a sample, and were compared and contrasted. Methods are comparable overall but markedly different in their powers to predict certain categories. Linear regression models of AQI through particle pollution are more favored to predict moderate air quality; ordered logistic regression models of AQI directly are more favored to predict good air quality.http://www.mdpi.com/2076-3298/1/1/54air quality indexparticle pollutionlinear regressionordered logistic regression
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Justin R. Chimka Ege Ozdemir
spellingShingle	Justin R. Chimka Ege Ozdemir A Proportional Odds Model of Particle Pollution Environments air quality index particle pollution linear regression ordered logistic regression
author_facet	Justin R. Chimka Ege Ozdemir
author_sort	Justin R. Chimka
title	A Proportional Odds Model of Particle Pollution
title_short	A Proportional Odds Model of Particle Pollution
title_full	A Proportional Odds Model of Particle Pollution
title_fullStr	A Proportional Odds Model of Particle Pollution
title_full_unstemmed	A Proportional Odds Model of Particle Pollution
title_sort	proportional odds model of particle pollution
publisher	MDPI AG
series	Environments
issn	2076-3298
publishDate	2014-08-01
description	A linear regression model of particle pollution and an ordered logistic regression model of the relevant index were selected for observations in the US city of Los Angeles, California. Models were used to forecast Air Quality Index (AQI) from a sample, and were compared and contrasted. Methods are comparable overall but markedly different in their powers to predict certain categories. Linear regression models of AQI through particle pollution are more favored to predict moderate air quality; ordered logistic regression models of AQI directly are more favored to predict good air quality.
topic	air quality index particle pollution linear regression ordered logistic regression
url	http://www.mdpi.com/2076-3298/1/1/54
work_keys_str_mv	AT justinrchimka aproportionaloddsmodelofparticlepollution AT egeozdemir aproportionaloddsmodelofparticlepollution AT justinrchimka proportionaloddsmodelofparticlepollution AT egeozdemir proportionaloddsmodelofparticlepollution
_version_	1725258767436611584

A Proportional Odds Model of Particle Pollution

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