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Regression analysis is a statistical approach used to investigate the correlations between variables, especially linear regression, that is a simple but effective approach for analyzing the relationship between a dependent variable and one independent variable. Since it has limitations based on the assumption that the mean of the noise should be zero, there are still some areas where it may be improved. In this article, we introduce a novel data fitting algorithm called the pulling linear regression, which is separated into two types: the line-pulling linear regression and the band-pulling linear regression. The method is developed from linear regression, which can create the regression line from the function that uses noise with various distributions. The result demonstrates that the sequence of sum square errors of the pulling linear regression is convergent. Moreover, we have a numerical example to show that the performance of the proposed algorithm is better than that of linear regression when the mean of the noise is not zero. And the last, we have an application to smooth the boundary of the pectoral muscle in digital mammograms. We found that the regression line of the proposed algorithm can do better than the linear regression when we would like to remove only the muscle part.

In this paper, we propose a method for estimating Normal distribution parameters using genetic algorithm. The main purpose of this research is to identify the most efficient estimators among three estimators for Normal distribution; Maximum likelihood method (ML), the least square method (LS), and genetic algorithm (GA) via numerical simulation and three real data, carbonation depth of Concrete Girder Bridges data examples which are based on performance measures such as The Root Mean Square Error (RMSE), Kolmogorov-Smirnov test, and Chi squared test. The simulation studies are conducted to evaluate the performances of the proposed estimators and provide statistical analysis of the real data set. The numerical results, x^2, show that the genetic algorithm performs better than other methods for actual data and simulated data unless the sample size is small.

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This article proposes a model that uses the adjusted mixture cosine model of two components with Markov chain (MC2MC) for predicting the monthly rainfall with actual data from Khon Kaen meteorological station (381201) in Khon Kaen province, Thailand. The data considers 31 years of historical data from January 1991 to December 2021. The evaluation is measured by the root mean square error (𝑅𝑀𝑆𝐸) and the 𝑅 2 values. We found that the mixture cosine model has 𝑅𝑀𝑆𝐸 and 𝑅 2 values of 70.72 and 52.49%, respectively, and the MC2MC model has 𝑅𝑀𝑆𝐸 and 𝑅 2 values of 42.43 and 82.53%, respectively.