Lactobacillus Salivarius Growth: An Algorithm Using Fuzzy Regression Method

Abstract

Introduction: The Intraclass Correlation (ICC) is widely used in the assessment of agreement between different measurement methods. Asessing agreement between variables assumes that the variables measure the same construct. In health science research, ICC are widely used to test intrarater and interrater reliability.

Objectives: The objective of the study is to determine the agreement between the two methods on microorganism data set in order to determine if they can be used interchangeable and to estimate the fuzzy regression and bootstrap method parameter of the models.

Methods:This methodology discovers the agreement between predicted data and original data for assessing crossness agreement of two observation. In this paper, linear regression and fuzzy regression have been applied using the SAS algorithm procedure. The ICC was used to measures the agreement and consistency among two quantitative variables.

Results: The value of ICC by using a linear equation is 0.98569 (almost perfect agreement) while the value of ICC using fuzzy regression equation is 0.91212 (almost perfect agreement).

Conclusion: This article also instructs readers on what to look for when encountering ICC in a piece of writing. This study may also motivate researchers to reveal more about their ICC studies, as well as encourage reviewers and editors to demand complete and precise information from authors.