Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics

Design of Experiments (DOE) is one of the well-known and widely used statistical methodologies. The results of this DOE provide a very valuable result especially when a researcher studying the relationship between variables. A large number of studies that have been carried out today are hoping for a...

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Bibliographic Details
Main Author:	Qadir, Soban
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA276-280 Mathematical Analysis
Online Access:	http://eprints.usm.my/58821/1/15-SOBAN%20QADIR-FINAL%20THESIS%20P-SGD000519%28R%29-24%20pages.pdf
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id	my-usm-ep.58821
record_format	uketd_dc
spelling	my-usm-ep.588212023-07-06T02:30:24Z Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics 2022-11 Qadir, Soban QA276-280 Mathematical Analysis Design of Experiments (DOE) is one of the well-known and widely used statistical methodologies. The results of this DOE provide a very valuable result especially when a researcher studying the relationship between variables. A large number of studies that have been carried out today are hoping for a more accurate result. Indeed, the number of studies involving the development of scientific research methodology is increasing over time. This study aims to develop the best method for data analysis, especially involving a combination of DOE, bootstrap, and linear regression as well as a multi-layer feed-forward neural network (MLFF) through the R programming language. The thesis emphasizes the development of an accurate and valid regression model that involves several combinations of key methods. Based on the results obtained, it can be concluded that this developed methodology shows results encouraging for modeling techniques. In conclusion, this method can be used effectively, especially when performing regression modeling on experimental design. 2022-11 Thesis http://eprints.usm.my/58821/ http://eprints.usm.my/58821/1/15-SOBAN%20QADIR-FINAL%20THESIS%20P-SGD000519%28R%29-24%20pages.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Pergigian
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA276-280 Mathematical Analysis
spellingShingle	QA276-280 Mathematical Analysis Qadir, Soban Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics
description	Design of Experiments (DOE) is one of the well-known and widely used statistical methodologies. The results of this DOE provide a very valuable result especially when a researcher studying the relationship between variables. A large number of studies that have been carried out today are hoping for a more accurate result. Indeed, the number of studies involving the development of scientific research methodology is increasing over time. This study aims to develop the best method for data analysis, especially involving a combination of DOE, bootstrap, and linear regression as well as a multi-layer feed-forward neural network (MLFF) through the R programming language. The thesis emphasizes the development of an accurate and valid regression model that involves several combinations of key methods. Based on the results obtained, it can be concluded that this developed methodology shows results encouraging for modeling techniques. In conclusion, this method can be used effectively, especially when performing regression modeling on experimental design.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Qadir, Soban
author_facet	Qadir, Soban
author_sort	Qadir, Soban
title	Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics
title_short	Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics
title_full	Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics
title_fullStr	Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics
title_full_unstemmed	Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics
title_sort	modeling k-factors analysis in design of experiment (doe) towards regression approach using multilayer feed-forward neural network (mlff): its’ application in biostatistics
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Pergigian
publishDate	2022
url	http://eprints.usm.my/58821/1/15-SOBAN%20QADIR-FINAL%20THESIS%20P-SGD000519%28R%29-24%20pages.pdf
_version_	1776101234539757568

Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics

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