Abstract
In the fuzzy regression models that are fitted by using fuzzy logic, every regression coefficient should be estimated at a certain level of a fuzziness tolerance because of dividing the error value into the coefficient. This study aims to compute the coefficient and deviation values of classical least squares (OLS) and fuzzy interval regression models on a sample data set and interpret them comparatively.
Highlights
Regression analysis is the statistical method that defines the causal relationship between a dependent variable and one or more independent variables, and are used to make relevant predictions [1]
It was suggested to use an h = 0.0 value called as “turbidity tolerance level”, and the mean squared error (MSE) and the coefficient of determination (R2) indexes were utilized as the goodness of fit test criteria showing the compatibility between the values calculated at the suggested h-level
obstructive sleep apnea syndrome (OSAS) patients ODI, REM and AH-duration had more significant effect on Apnea-Hypopnea Index (AHI) whereas BMI, ODI and AROUSAL variables were strongly significant for non-positional OSAS patients
Summary
Regression analysis is the statistical method that defines the causal relationship between a dependent variable and one or more independent variables, and are used to make relevant predictions [1]. The types and structures of dependent and independent data sets in the healthcare area (such as diagnosis, treatment, research, method development, etc.) often do not conform to classical models. Uncertainity situations in which the dependent variable cannot be expressed continuously or a set of independent variables could be affected by different environmental factors are encountered [2,3]. Accepted: June 26, 2021: Published: June 28, 2021. Int J Clin Biostat Biom 2021, 7:037
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