Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature

Abstract

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

“…RMSE is the average value of the sum of the squared errors [84]. If the RMSE value is low, then the variation of the values generated by a forecast model or in this case the spermatozoa path analyzed approximates the variation of its observational value or the straight-line value of 𝑎𝑥 ′ + 𝑏𝑦 ′ + 𝑐 = 0 [85]. An illustration of the relationship between the points in the path with the points in the regression line can be seen in Fig.…”