Weibull model for dose response data and akaike information criterion for model selection
| dc.contributor.advisor | Otieno, Romanus Odhiambo | |
| dc.contributor.author | Stephen, P. M. | |
| dc.date.accessioned | 2012-02-28T12:36:33Z | |
| dc.date.available | 2012-02-28T12:36:33Z | |
| dc.date.issued | 2012-02-28 | |
| dc.description | Department of Mathematics,84p.The QA 276.P4 2000. | en_US |
| dc.description.abstract | Statistical linear models are used to study dose response models in the bioassay. These have given rise to many statistical problems since the dose response data do not follow linear model. This has led to the use of non-linear models such as probit and Logit. The Logit model has been widely used to analyse the data. Several non-linear models have also been proposed which can be treated in a fashion similar to the parametric logistic model. In this project, we review the parametric logistic model and study the analytical method used in its analysis thoroughly. We study in detail the Weibull dose-response model, following the same method of logistic model. We are able to show that their structures are similar. We study the Akaike Information Criterion for model selection and use it to select a better model between logistic and Weibull models. | en_US |
| dc.description.sponsorship | Kenyatta University | en_US |
| dc.identifier.uri | http://ir-library.ku.ac.ke/handle/123456789/2873 | |
| dc.language.iso | en | en_US |
| dc.subject | Linear models (statistics) | en_US |
| dc.subject | Weibull distribution | |
| dc.title | Weibull model for dose response data and akaike information criterion for model selection | en_US |
| dc.type | Thesis | en_US |