Abstract
Linear regression parameters based on fuzzy theory are compared with other statistical approaches. A new algorithm of a simple weighted least squares method, independent of a priori information, is proposed. The algorithm was verified on model data, and its adequacy was confirmed with the use of standard criteria. The algorithm has been implemented as Python language computer program. New method of calculation of the scatter of fuzzy dependent variable around its mediane value, as well as the upper and lower bonds of fuzzy regression equations have been developed and verified. Proposed methods are shown to be useful alternatives to the most popular methods for constructing linear regression, which assume a normal distribution of errors.
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