JMASM41: An alternative method for multiple linear model regression modeling, a technical combining of robust, bootstrap and fuzzy approach (SAS)

• Main Authors: Wan Ahmad, Wan Muhamad Amir, Aleng, Nor Azlida, Awang Nawi, Mohamad Arif, Mohd Ibrahim, Mohamad Shafiq
• Format: Article
• Language: English
• Published: Journal of Modern Applied Statistical Methods Inc 2016
• Subjects: QA276 Mathematical Statistics
• 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

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.