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Vol.15, No.3, August 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Correlation and Regression in Computer Science: Differences, Applications, and Result Interpretation
Ivan Trenchev, Radoslav Mavrevski, Miglena Trencheva, Tereza Trencheva
© 2026 Radoslav Mavrevski, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 3, Pages 2201-2207, ISSN 2217-8309, DOI: 10.18421/TEM153-14, August 2026.
Received: 04 November 2025.
Abstract:
Understanding the distinction between correlation and regression is essential for interpreting statistical relationships in computer science experimental data. Although both methods explore associations between variables, they serve different purposes and offer distinct insights. Correlation measures how strongly two variables are related and whether this relationship is positive or negative, without implying causation. Regression, on the other hand, models predictive relationships, allowing one variable to be estimated based on another. Despite their widespread use, these concepts are often confused in computer science research and data analytics, leading to misinterpretation of results. This paper clarifies the conceptual and practical differences between correlation and regression, outlines when each method is appropriate, and demonstrates their application through computer science–related examples.
Keywords – Correlation, regression, computer science statistics, predictive modeling, variable relationships. |
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