Estimation of the fuzzy linear regression model using the method of A.R. Arabpour And M.Tata

Abstract

Fuzzy Linear Regression is the optimal tool for analyzing inaccurate and unclear data, as the use of fuzzy methods in analyzing these data can improve accuracy and reliability in estimating relationships between variables. The research estimated a fuzzy linear regression model with a triangular Fuzzy response variable, Crisp explanatory variables, and triangular Fuzzy parameters using the A.R.Arabpour and M.Tata estimation method. The study data was about systolic and diastolic hypertension as a fuzzy response variable and a set of factors affecting it as Crisp, non-fuzzy explanatory variables represented by (age, weight, Glycemia, triglycerides, and cholesterol) for a group of (125) patients obtained from Balad General Hospital in Salah al-Din Governorate, in an attempt to find out the effect of each of these factors under study on systolic and diastolic hypertension, the MATLAB program was used to obtain the results, and the results confirmed the direct relationship between The fuzzy response and the non-fuzzy explanatory variables. The t-test results confirmed the significance of the relationship between them, which explains that systolic and diastolic hypertension may be directly affected by an increase in any of the explanatory variables under study.