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In this article, we discuss the problem of heteroscedasticity and show the procedure for eliminating heteroscedasticity using a novel approach similar to the weighted least squares method. We give the principles of implementing the most common tests that are used to detect heteroscedasticity in constructing linear regression models and compare different heteroscedasticity elimination methods against our method. One of the achievements of this paper is that real empirical data is used to test for heteroscedasticity. The aim of the article is to propose Python implementation of many tests used for checking the heteroscedasticity in multifactor regression models.

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Exploring the Efficacy of Heteroscedasticity Reduction Methods in Multifactor Regression Models Using Python

  • 会议论文

  • 发表日期：

    2026年

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  • Abdulaziz Xo‘jamqulov,
  • Tolliboy Adirov,
  • Botirjon Karimov,
  • Ulug‘bek Umarov

摘要

In this article, we discuss the problem of heteroscedasticity and show the procedure for eliminating heteroscedasticity using a novel approach similar to the weighted least squares method. We give the principles of implementing the most common tests that are used to detect heteroscedasticity in constructing linear regression models and compare different heteroscedasticity elimination methods against our method. One of the achievements of this paper is that real empirical data is used to test for heteroscedasticity. The aim of the article is to propose Python implementation of many tests used for checking the heteroscedasticity in multifactor regression models.