EVALUATION OF FULL FACTORIAL AND MULTIPLE REGRESSION METHODS IN 33 FACTORIAL DESIGN - A. ADAMU; G. B. MELLER; BUHARI. S. A (BIMA JOURNAL OF SCIENCE AND TECHNOLOGY GOMBE, 2020)
This research compared full factorial design model and multiple linear regression models in seed rates, row spacing and varieties of bread wheat yield. 33 full factorial design method and multiple linear regression were used for the analysis. The goodness of fit criteria used to evaluate the performance of structures was Akaike information criterion (AIC) and Bayesian information criterion (BIC). The data used composed of 270 observations for yield which 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 have been tested and satisfied all the assumptions. Based on the results in this study, it was observed that coefficient of determination (R2) has a highest percentage value in full factorial design model compare with multiple regression model. It was also found that full factorial design model is the most appropriate and accounted for most of the variability, According to AIC and BIC criteria.