Publication: Suatu kaedah alternatif bagi pemodelan linear dalam biostatistik
| dc.contributor.author | Ibrahim, Mohamad Shafiq Mohd | |
| dc.date.accessioned | 2023-09-10T08:13:04Z | |
| dc.date.available | 2023-09-10T08:13:04Z | |
| dc.date.issued | 2018-09 | |
| dc.description.abstract | model. Current technological advancement and increase in development of the new or modified methodology building leads to the development of an alternative method for multiple linear regression model calculation. In this study, multiple linear regression model will be calculated by using SAS programming language based on computational statistics which will consider combination of robust regression, bootstrap, weighted data, Bayesian and fuzzy regression method. Methodology building is based on the SAS algorithm (SAS 9.4 software) which is a robust computational statistics that consists of the combination of robust regression, bootstrap, weighted data, bayesian and fuzzy regression methods. Three different SAS algorithms that is; (i) Bootstrap Multiple Linear Regression (BMLR), (ii) Bootstrap Weighted Bayesian Multiple Linear Regression (BWBMLR) and (iii) Fuzzy Bootstrap Weighted Multiple Linear Regression (FBWMLR) will be compared separately according to their average width of prediction. To illustrate the potential of built-in algorithm, three cases of study will be used which are modeling on systolic blood pressure level, modeling on tumour size and modeling on Early Childhood Caries (ECC). The average width of prediction interval results for all cases of the models has been computed and compared. The smallest width will indicate the best fitting model. The result shows that the former MLR model has an average width of 11.2948, 7.4816 and 29.0407; and BMLR model has an average width of 2.5785, 3.7098 and 19.6589. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/17519 | |
| dc.language.iso | en | |
| dc.subject | Linear models | |
| dc.title | Suatu kaedah alternatif bagi pemodelan linear dalam biostatistik | |
| dc.type | Resource Types::text::thesis::master thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |