EVALUATION OF FULL FACTORIAL AND MULTIPLE REGRESSION METHODS IN 33 FACTORIAL DESIGN
Keywords:
Full Factorial Design, Multiple Regression, Coefficient of Determination (R2), Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC).Abstract
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.
