TEACHING LINEAR REGRESSION WITH PYTHON AND EXCEL: METHODS, TOOLS, AND PRACTICAL TASKS
DOI:
https://doi.org/10.17770/etr2025vol3.8556Keywords:
Excel, linear regression, Python, teaching methodsAbstract
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.
References
L. K. Soiferman, “Compare and contrast inductive and deductive research approaches,” Online Submission, 2010.
W. M. K. Trochim, The Research Methods Knowledge Base, 2nd ed. Atomic Dog Publishing, 2006.
J. W. Creswell and V. L. Plano Clark, Designing and Conducting Mixed Methods Research. Thousand Oaks, CA: Sage Publications, 2007.
A. Onwuegbuzie and N. Leech, “Taking the ‘Q’ out of research: Teaching research methodology courses without the divide between quantitative and qualitative paradigms,” Quantity and Quality, vol. 39, pp. 267-296, 2005.
M. Tiittanen, “The deductive and inductive instructional approach controversy in Eswatini context,” Global Journal of Foreign Language Teaching, vol. 10, no. 2, pp. 139-158, 2020.
C. E. Hmelo-Silver, “Problem-based learning: What and how do students learn?” Educational Psychology Review, vol. 16, pp. 235–266, 2004.
F. Dochy, M. Segers, P. Van den Bossche, and D. Gijbels, “Effects of problem-based learning: A meta-analysis,” Learning and Instruction, vol. 13, no. 5, pp. 533-568, 2003.
Scikit-learn Developers, „Scikit-learn Documentation“, [Online]. Available: https://scikit-learn.org. [Accessed: Feb. 24, 2025].
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Neli Kalcheva, Maya Todorova, Ginka Marinova, Firgan Feradov
This work is licensed under a Creative Commons Attribution 4.0 International License.