Analysis of auto correlated data in groups of experiments
By: Premi T C.
Contributor(s): Gopinathan Unnithan V K (Guide).
Material type:
BookPublisher: Vellanikkara Department of Agricultural Statistics, College of Horticulture 1994DDC classification: 640 Online resources: Click here to access online Dissertation note: MSc Abstract: Analysis of variance model for the groups of experiments needs modification, when observations are taken repeatedly on the same experimental units owing to the autocorrelated nature of error terms. A model which takes the dependence of error terms into consideration was evolved for dealing such situations. But estimation of parameters using least square principle and their tests of significance not straight forward. Therefore numerical solutions using iterative technique was employed for estimation of parameters of the model.
The newly developed procedure was compared to the widely used analysis of the split-plot setup and the comparative advantage of the new method was established.
The new methodology along with the widely used analysis of the split – plot set up were illustrated using two different sets of data. The superiority of the new method over the split –plot analysis was demonstrated in both sets of data.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
Theses
|
KAU Central Library, Thrissur Theses | 640 PRE/AN (Browse shelf) | Available | 170628 |
MSc
Analysis of variance model for the groups of experiments needs modification, when observations are taken repeatedly on the same experimental units owing to the autocorrelated nature of error terms. A model which takes the dependence of error terms into consideration was evolved for dealing such situations. But estimation of parameters using least square principle and their tests of significance not straight forward. Therefore numerical solutions using iterative technique was employed for estimation of parameters of the model.
The newly developed procedure was compared to the widely used analysis of the split-plot setup and the comparative advantage of the new method was established.
The new methodology along with the widely used analysis of the split – plot set up were illustrated using two different sets of data. The superiority of the new method over the split –plot analysis was demonstrated in both sets of data.
There are no comments for this item.