DETERMINING THE BEST COVARIANCE STRUCTURE IN 33 FACTORIAL DESIGN - Adamu, A.; Danbaba, A. (BIMA JOURNAL OF SCIENCE AND TECHNOLOGY GOMBE, 2018)
This research evaluated the performance of covariance structure in seed rates, row spacing and varieties of bread wheat yield. 33 full factorial design method and mixed model methods were used for the analysis. The four covariance structure used were compound symmetry (CS), huynh-feldt (HF), first order auto-regressive (AR(1)) and heterogeneous first order auto-regressive (ARH(1)). The goodness of fit criteria used to evaluate the performance of covariance structures were Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The data used composed of 270 observations for yield and were divided into three factors and three levels. The factors were A, B and C and the levels are 1, 2 and 3 for each factor. Analysis shows that the data satisfied all the assumptions. Based on the results in this study, it was found that First order auto-regressive (AR(1)) was found to be the best covariance structure for the data set.