adv. artif. intell. res. Advances in Artificial Intelligence Research 2757-7422 Osman ÖZKARACA 10.54569/aair.1032607 Artificial Intelligence Mathematical Sciences Yapay Zeka Matematik An Approach Towards the Least-Squares Method for Simple Linear Regression https://orcid.org/0000-0002-1704-3694 Tali Hasan Halit HALIC UNIVERSITY https://orcid.org/0000-0002-1695-6125 Çelti Ceren HALIC UNIVERSITY 09 23 2022 2 2 38 44 12 07 2021 07 01 2022 Copyright © 2020, Advances in Artificial Intelligence Research 2020 Advances in Artificial Intelligence Research

This study approaches the least-squares method for simple linear regression model. The least-squares line does not comply with the data when there are outliers that have deceptive effects on the results in the dataset. The study aims to develop a method for obtaining a line that complies more with the data when there are outliers in the dataset.

 Applied mathematics Machine learning Simple linear regression Least-Squares method Outliers Miller I, Miller M. Mathematical Statistics, Prenttice-Hall, Inc, 1999 (Çev. Ümit Şenesen John E. Freund’dan Matematiksel İstatistik, Literatür Yayıncılık. 2007). Arslan İ. Python ile Veri Bilimi, Pusula Yayıncılık, Türkiye, 2020. Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. John Wiley & Sons, 1987. Attaway S. Matlab A Practical Introduction to Programming and Problem Solving. 5th ed. Cambridge, USA, Butterwoth-Heinmann, 2019. Kubat C. Matlab Yapay Zeka ve Mühendislik Uygulamaları. 5. Baskı, İstanbul, Türkiye, Abaküs Kitap Yayın, 2021. Güneş A, Yıldız K. Matlab Matematik ve Grafik Programlama Dili. İstanbul, Türkiye, Türkmen Kitabevi, 1997. Verardi V, Croux C. “Robust regression in Stata”. The Stata Journal, 9(3), 439-453, 2009. Andersen R. Modern methods for robust regression (No. 152). Sage, 2008.