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The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto

This study considers an analysis using a Poisson regression model where the response outcome is a count, with large outcomes being rare events. Estimates of the parameters are obtained by using the maximum likelihood estimates. Inferences about the regression parameters are based on Wald test and li...

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Main Authors: Libasin, Zuraira, Mohamed Zukri, Shamsunarnie, Karjanto, Suryaefiza
Format: Research Reports
Language:English
Published: 2011
Subjects:
Mathematical statistics. Probabilities
Data processing
Prediction analysis
Decision theory
Online Access:http://ir.uitm.edu.my/id/eprint/42655/1/42655.pdf
http://ir.uitm.edu.my/id/eprint/42655/
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spelling my.uitm.ir.426552021-03-05T04:34:53Z http://ir.uitm.edu.my/id/eprint/42655/ The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto Libasin, Zuraira Mohamed Zukri, Shamsunarnie Karjanto, Suryaefiza Mathematical statistics. Probabilities Data processing Prediction analysis Decision theory This study considers an analysis using a Poisson regression model where the response outcome is a count, with large outcomes being rare events. Estimates of the parameters are obtained by using the maximum likelihood estimates. Inferences about the regression parameters are based on Wald test and likelihood ratio test. In the model building process, the stepwise selection method were used to determine important predictor variables, diagnostic tools were used in detecting multicollinearity, non-constant variance, outliers, and also analysis of residual were used to measure the goodness fit of the model. Applications of these methods are illustrated by employing a case study of lower respiratory illness data in infants which took repeated observations of infants over one year (LaVange et at, 1994). Six explanatory variables involve the number of weeks during that year for which the child is considered to be at risk, crowded conditions occur in the household, family’s socioeconomic status, race, passive smoking, and age group. We found that the explanatory variables which contribute significantly are passive smoking and crowding. Social economic status and race do not appear to be influential, and neither does age group. 2011-03 Research Reports NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/42655/1/42655.pdf Libasin, Zuraira and Mohamed Zukri, Shamsunarnie and Karjanto, Suryaefiza (2011) The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto. [Research Reports] (Unpublished)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Mathematical statistics. Probabilities
Data processing
Prediction analysis
Decision theory
spellingShingle Mathematical statistics. Probabilities
Data processing
Prediction analysis
Decision theory
Libasin, Zuraira
Mohamed Zukri, Shamsunarnie
Karjanto, Suryaefiza
The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto
description This study considers an analysis using a Poisson regression model where the response outcome is a count, with large outcomes being rare events. Estimates of the parameters are obtained by using the maximum likelihood estimates. Inferences about the regression parameters are based on Wald test and likelihood ratio test. In the model building process, the stepwise selection method were used to determine important predictor variables, diagnostic tools were used in detecting multicollinearity, non-constant variance, outliers, and also analysis of residual were used to measure the goodness fit of the model. Applications of these methods are illustrated by employing a case study of lower respiratory illness data in infants which took repeated observations of infants over one year (LaVange et at, 1994). Six explanatory variables involve the number of weeks during that year for which the child is considered to be at risk, crowded conditions occur in the household, family’s socioeconomic status, race, passive smoking, and age group. We found that the explanatory variables which contribute significantly are passive smoking and crowding. Social economic status and race do not appear to be influential, and neither does age group.
format Research Reports
author Libasin, Zuraira
Mohamed Zukri, Shamsunarnie
Karjanto, Suryaefiza
author_facet Libasin, Zuraira
Mohamed Zukri, Shamsunarnie
Karjanto, Suryaefiza
author_sort Libasin, Zuraira
title The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto
title_short The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto
title_full The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto
title_fullStr The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto
title_full_unstemmed The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto
title_sort procedure of poisson regression model / zuraira libasin, shamsunarnie mohamed zukri and suryaefiza karjanto
publishDate 2011
url http://ir.uitm.edu.my/id/eprint/42655/1/42655.pdf
http://ir.uitm.edu.my/id/eprint/42655/
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score 13.211869

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