‎In this paper a new weighted fuzzy ridge regression method for a given set of crisp input and triangular fuzzy output values is proposed‎. ‎In this regard‎, ‎ridge estimator of fuzzy parameters is obtained for regression model and its prediction error is calculated by using the weighted fuzzy norm of crisp ridge coefficients‎. . ‎To evaluate the proposed regression model‎, ‎we introduce the fuzzy coefficient of determination (FCD)‎. ‎Fuzzy regression is compared with its ridge version by using mean predict error and FCD‎, ‎numerically‎. ‎It is evident from comparison results the proposed fuzzy ridge regression is superior to the non-ridge counterpar

‎Robust regression is an appropriate alternative for ordinal regression when outliers exist in a given data set‎. ‎If we have fuzzy observations‎, ‎using ordinal regression methods can't model them; In this case‎, ‎using fuzzy regression is a good method‎. ‎When observations are fuzzy and there are outliers in the data sets‎, ‎using robust fuzzy regression methods are appropriate alternatives‎. ‎In this paper‎, ‎we propose a fuzzy least square regression analysis‎. ‎When independent variables are crisp‎, ‎the dependent variable is fuzzy number and outliers are present in the data set‎. ‎In the proposed method‎, ‎the residuals are ranked as the comparison of fuzzy sets‎. ‎In the proposed method‎, ‎the residuals are ranked as the comparison of fuzzy sets‎, ‎and the weight matrix is defined by the membership function of the residuals‎. ‎Weighted fuzzy least squares estimators (WFLSE) are obtained by using weight matrix‎. ‎Two examples are discussed and results of these examples are presented‎. ‎Finally‎, ‎we compare this proposed method with ordinal least squares method using the goodness of fit indices‎.