This paper addresses the standard linear model, particularly focusing on the case of linear regression. After establishing certain results related to ordinary least squares, the Student’s t-test, and the Fisher test, we introduce change point detection within the linear model framework, using estimated coefficients as the test statistic. Finally, the proposed model is applied to real weather data from the N’Djamena region in Chad to detect changes in precipitation patterns.

linear model, change detection, ordinary least squares, residual error, Student test, Fisher test.

Received: July 31, 2024; Revised: September 2, 2024; Accepted: September 13, 2024; Published: January 6, 2025

How to cite this article: Mahamat Ali ISSAKA, Amary DIOP and H. G. IZADDINE, On using estimated coefficients of a linear model for change point detection: application to weather data, JP Journal of Biostatistics 25(1) (2025), 145-175. https://doi.org/10.17654/0973514325007

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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