1
Assistant Professor, Department of Statistics, Shahid Bahonar University of Kerman
2
Master's degree in Mathematical Statistics, Shahid Bahonar University of Kerman
Abstract
Interval linear regression is a generalization of ordinary regression that is used to calculate the relationship between independent variables and dependent variables in a fuzzy environment. When the parameters of a linear regression model are expressed by fuzzy membership functions instead of probability functions, the model is called an interval linear regression model.
In this paper, we first introduce the methods of analyzing interval linear regression, and then, to improve these methods, we propose a method that reduces the ambiguity of the model. Finally, we examine the effectiveness of the proposed method with some numerical examples. The calculations performed in the examples were performed using the Alabama package in R software.
