Identification of Significant Treatment Effects in Unreplicated Factorial Experiments.
| dc.contributor.author | Ibraheem, B. A. | |
| dc.contributor.author | Adeleke, B. L. | |
| dc.contributor.author | Oyeyemi, G. M. | |
| dc.date.accessioned | 2023-07-19T14:12:53Z | |
| dc.date.available | 2023-07-19T14:12:53Z | |
| dc.date.issued | 2006 | |
| dc.description.abstract | Unreplicated factorial designs are used in industrial and other real-life experiments due to costs consideration or some other reasons. Normal plot has been an important technique of identifying significant treatment effects. However, two other methods of identifying significant effects namely, Lenth and Step-Down Lenth methods are examined in this paper. The three methods are compared in terms of their performance in identifying significant effects by simulating unreplicated 24 factorial designs at two different settings. In evaluating the three methods, we observed that Normal plot has the overall best performance, but the three methods are similar when the magnitude of each of the effects is high. | en_US |
| dc.description.sponsorship | Self-sponsored | en_US |
| dc.identifier.citation | Journal of the Biometrics Association of Nigeria, BIOMATA | en_US |
| dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/11585 | |
| dc.language.iso | en | en_US |
| dc.publisher | Published by Biometrics Association of Nigeria. | en_US |
| dc.relation.ispartofseries | 1;101 - 111. | |
| dc.subject | Pseudo-Standard Error, Significant Effect, Treatment Contrast, Un-replicated Factorial Experiment | en_US |
| dc.title | Identification of Significant Treatment Effects in Unreplicated Factorial Experiments. | en_US |
| dc.type | Article | en_US |