Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise....
Ausführliche Beschreibung

Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. Ausführliche Beschreibung