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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Journal of Modern Applied Statistical Methods Inc
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/72172/1/An%20alternative%20Method%20for%20Multiple%20Linear%20Model%20Regression%20Modeling.pdf http://irep.iium.edu.my/72172/ https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=1945&context=jmasm |
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| Summary: | 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. |
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