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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| Format: | Research Reports |
| Language: | en |
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2011
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| Online Access: | https://ir.uitm.edu.my/id/eprint/42655/1/42655.pdf https://ir.uitm.edu.my/id/eprint/42655/ |
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| _version_ | 1833067046848430080 |
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| author | Libasin, Zuraira Mohamed Zukri, Shamsunarnie Karjanto, Suryaefiza |
| author_facet | Libasin, Zuraira Mohamed Zukri, Shamsunarnie Karjanto, Suryaefiza |
| author_sort | Libasin, Zuraira |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uitm.ir-42655 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2011 |
| record_format | eprints |
| spelling | my.uitm.ir-426552021-03-05T04:34:53Z https://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 https://ir.uitm.edu.my/id/eprint/42655/1/42655.pdf The procedure of Poisson regression model / Zuraira Libasin, Shamsunarnie Mohamed Zukri and Suryaefiza Karjanto. (2011) [Research Reports] (Unpublished) |
| 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 |
| title | 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_short | 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 |
| topic | Mathematical statistics. Probabilities Data processing Prediction analysis Decision theory |
| url | https://ir.uitm.edu.my/id/eprint/42655/1/42655.pdf https://ir.uitm.edu.my/id/eprint/42655/ |
| url_provider | http://ir.uitm.edu.my/ |