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Treffer: TEACHING LINEAR REGRESSION WITH PYTHON AND EXCEL: METHODS, TOOLS, AND PRACTICAL TASKS

Title:
TEACHING LINEAR REGRESSION WITH PYTHON AND EXCEL: METHODS, TOOLS, AND PRACTICAL TASKS
Authors:
Kalcheva, Neli, Todorova, Maya, Marinova, Ginka, Feradov, Firgan
Source:
ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference; Vol. 3 (2025): Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference. Volume 3; 163-167 ; 2256-070X ; 1691-5402
Publisher Information:
Rezekne Academy of Technologies
Publication Year:
2025
Collection:
The Scientific Journal of Rezeknes Augstskola
Subject Terms:
Excel, linear regression, Python, teaching methods
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
English
Relation:
https://journals.ru.lv/index.php/ETR/article/view/8556/7003; https://journals.ru.lv/index.php/ETR/article/view/8556
DOI:
10.17770/etr2025vol3.8556
Availability:
https://journals.ru.lv/index.php/ETR/article/view/8556
https://doi.org/10.17770/etr2025vol3.8556
Rights:
Copyright (c) 2025 Neli Kalcheva, Maya Todorova, Ginka Marinova, Firgan Feradov ; https://creativecommons.org/licenses/by/4.0
Accession Number:
edsbas.53E3CDA0
Database:
BASE

Weitere Informationen

This article analyses various approaches to teaching linear regression using Python and Excel. The inductive and deductive methods, as well as problem-based learning, are examined. Examples of using the scikit-learn library or Excel functionalities for building linear regression models are described. Practical tasks are presented to support the understanding of regression analysis concepts.