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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| Format: | Thesis |
| Language: | en |
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2022
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| Online Access: | http://eprints.usm.my/58821/1/15-SOBAN%20QADIR-FINAL%20THESIS%20P-SGD000519%28R%29-24%20pages.pdf http://eprints.usm.my/58821/ |
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| _version_ | 1834506931733004288 |
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| author | Qadir, Soban |
| author_facet | Qadir, Soban |
| author_sort | Qadir, Soban |
| building | Hamzah Sendut Library |
| collection | Institutional Repository |
| content_provider | Universiti Sains Malaysia |
| content_source | USM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.usm.eprints.58821 |
| institution | Universiti Sains Malaysia |
| language | en |
| publishDate | 2022 |
| record_format | eprints |
| spelling | my.usm.eprints.58821 http://eprints.usm.my/58821/ Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics 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 NonPeerReviewed application/pdf en http://eprints.usm.my/58821/1/15-SOBAN%20QADIR-FINAL%20THESIS%20P-SGD000519%28R%29-24%20pages.pdf Qadir, Soban (2022) Modeling K-Factors Analysis in Design Of Experiment (Doe) Towards Regression Approach Using Multilayer Feed-Forward Neural Network (MLFF): Its’ Application In Biostatistics. PhD thesis, Universiti Sains Malaysia. |
| 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 |
| 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_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_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_sort | modeling k-factors analysis in design of experiment (doe) towards regression approach using multilayer feed-forward neural network (mlff): its’ application in biostatistics |
| topic | QA276-280 Mathematical Analysis |
| url | http://eprints.usm.my/58821/1/15-SOBAN%20QADIR-FINAL%20THESIS%20P-SGD000519%28R%29-24%20pages.pdf http://eprints.usm.my/58821/ |
| url_provider | http://eprints.usm.my/ |