JMASM41: An alternative method for multiple linear model regression modeling, a technical combining of robust, bootstrap and fuzzy approach (SAS)
Research on modeling is becoming popular nowadays, there are several of analyses used in research for modeling and one of them is known as applied multiple linear regressions (MLR). To obtain a bootstrap, robust and fuzzy multiple linear regressions, an experienced researchers should be aware the...
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Journal of Modern Applied Statistical Methods Inc
2016
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| Online Access: | http://irep.iium.edu.my/72172/ http://irep.iium.edu.my/72172/ http://irep.iium.edu.my/72172/ http://irep.iium.edu.my/72172/1/An%20alternative%20Method%20for%20Multiple%20Linear%20Model%20Regression%20Modeling.pdf |
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iium-721722019-05-16T06:25:47Z http://irep.iium.edu.my/72172/ JMASM41: An alternative method for multiple linear model regression modeling, a technical combining of robust, bootstrap and fuzzy approach (SAS) Wan Ahmad, Wan Muhamad Amir Aleng, Nor Azlida Awang Nawi, Mohamad Arif Mohd Ibrahim, Mohamad Shafiq QA276 Mathematical Statistics Research on modeling is becoming popular nowadays, there are several of analyses used in research for modeling and one of them is known as applied multiple linear regressions (MLR). To obtain a bootstrap, robust and fuzzy multiple linear regressions, an experienced researchers should be aware the correct method of statistical analysis in order to get a better improved result. The main idea of bootstrapping is to approximate the entire sampling distribution of some estimator. To achieve this is by resampling from our original sample. In this paper, we emphasized on combining and modeling using bootstrapping, robust and fuzzy regression methodology. An algorithm for combining method is given by SAS language. We also provided some technical example of application of method discussed by using SAS computer software. The visualizing output of the analysis is discussed in detail. Journal of Modern Applied Statistical Methods Inc 2016-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/72172/1/An%20alternative%20Method%20for%20Multiple%20Linear%20Model%20Regression%20Modeling.pdf Wan Ahmad, Wan Muhamad Amir and Aleng, Nor Azlida and Awang Nawi, Mohamad Arif and Mohd Ibrahim, Mohamad Shafiq (2016) JMASM41: An alternative method for multiple linear model regression modeling, a technical combining of robust, bootstrap and fuzzy approach (SAS). Journal of Modern Applied Statistical Methods, 15 (2). pp. 1-14. ISSN 1538−9472 https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=1945&context=jmasm 10.22237/jmasm/1478004120 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA276 Mathematical Statistics |
| spellingShingle |
QA276 Mathematical Statistics Wan Ahmad, Wan Muhamad Amir Aleng, Nor Azlida Awang Nawi, Mohamad Arif Mohd Ibrahim, Mohamad Shafiq JMASM41: An alternative method for multiple linear model regression modeling, a technical combining of robust, bootstrap and fuzzy approach (SAS) |
| description |
Research on modeling is becoming popular nowadays, there are several of analyses used
in research for modeling and one of them is known as applied multiple linear regressions
(MLR). To obtain a bootstrap, robust and fuzzy multiple linear regressions, an
experienced researchers should be aware the correct method of statistical analysis in
order to get a better improved result. The main idea of bootstrapping is to approximate
the entire sampling distribution of some estimator. To achieve this is by resampling from
our original sample. In this paper, we emphasized on combining and modeling using
bootstrapping, robust and fuzzy regression methodology. An algorithm for combining
method is given by SAS language. We also provided some technical example of
application of method discussed by using SAS computer software. The visualizing output
of the analysis is discussed in detail. |
| format |
Article |
| author |
Wan Ahmad, Wan Muhamad Amir Aleng, Nor Azlida Awang Nawi, Mohamad Arif Mohd Ibrahim, Mohamad Shafiq |
| author_facet |
Wan Ahmad, Wan Muhamad Amir Aleng, Nor Azlida Awang Nawi, Mohamad Arif Mohd Ibrahim, Mohamad Shafiq |
| author_sort |
Wan Ahmad, Wan Muhamad Amir |
| title |
JMASM41: An alternative method for multiple linear model regression modeling, a technical
combining of robust, bootstrap and fuzzy approach (SAS) |
| title_short |
JMASM41: An alternative method for multiple linear model regression modeling, a technical
combining of robust, bootstrap and fuzzy approach (SAS) |
| title_full |
JMASM41: An alternative method for multiple linear model regression modeling, a technical
combining of robust, bootstrap and fuzzy approach (SAS) |
| title_fullStr |
JMASM41: An alternative method for multiple linear model regression modeling, a technical
combining of robust, bootstrap and fuzzy approach (SAS) |
| title_full_unstemmed |
JMASM41: An alternative method for multiple linear model regression modeling, a technical
combining of robust, bootstrap and fuzzy approach (SAS) |
| title_sort |
jmasm41: an alternative method for multiple linear model regression modeling, a technical
combining of robust, bootstrap and fuzzy approach (sas) |
| publisher |
Journal of Modern Applied Statistical Methods Inc |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/72172/ http://irep.iium.edu.my/72172/ http://irep.iium.edu.my/72172/ http://irep.iium.edu.my/72172/1/An%20alternative%20Method%20for%20Multiple%20Linear%20Model%20Regression%20Modeling.pdf |
| first_indexed |
2023-09-18T21:42:18Z |
| last_indexed |
2023-09-18T21:42:18Z |
| _version_ |
1777413221317083136 |