The article contains the results of research on the development of an application for determining the correlation between two data samples. Such an application is relevant for correlation analysis. The central component of the applications the Correlation library, which contains classes and methods for calculating the Pearson, Spearman, and Kendall correlation coefficients. The library is implemented in Python. The created Correlation library utilizes built-in Python data types and does not rely on third-party libraries. The article describes the algorithms used to calculate these coefficients, as well as the Python implementations for calculating the specified correlation coefficients. For the practical implementation of the library, the PyCorrelation application has been developed in two versions. The interface of the first version of the application is implemented using the PyQT 5 library; the interface of the second version is implemented using the free Custom Tkinter library. The results of calculating the Pearson, Spearman, and Kendall correlation coefficients are verified using MS Excel spreadsheets. The developed Correlation library has significant prospects for modernization and use as a component of software tools for statistical processing of experimental data. It can also be integrated into future versions of the StatCriterion software tool, previously developed by the authors of the article.
R. Taylor, “Interpretation of the correlation coefficient: a basic review,” Journal of Diagnostic Medical Sonography, vol. 6, no. 1, pp. 35-39, 1990.
B. Ratner, “The correlation coefficient: Its values range between +1/-1, or do they?” Journal of Targeting, Measurement and Analysis for Marketing, vol. 17, no. 2, pp. 139-142, 2009.
D. Chicco and G. Jurman, “The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification,” BioData Mining, vol. 16, no. 1, p. 4, 2023.
J. Jiang, X. Zhang, and Z. Yuan, “Feature selection for classification with Spearman’s rank correlation coefficient-based self-information in divergence-based fuzzy rough sets,” Expert Systems with Applications, vol. 249, p. 123633, 2024.
S. Chatterjee, “A new coefficient of correlation,” Journal of the American Statistical Association, vol. 116, no. 536, pp. 2009-2022, 2021.
X. Su, S. Shang, Z. Xu, H. Qian, and X. Pan, “Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient,” CMES-Computer Modeling in Engineering & Sciences, vol. 140, no. 2, pp. 23-26, 2024.
H. Yu and A. D. Hutson, “A robust Spearman correlation coefficient permutation test,” Communications in Statistics - Theory and Methods, vol. 53, no. 6, pp. 2141-2153, 2024.
F. Han and Z. Huang, “Azadkia-Chatterjee’s correlation coefficient adapts to manifold data,” The Annals of Applied Probability, vol. 34, no. 6, pp. 5172-5210, 2024.
C. A. Mertler, R. A. Vannatta, and K. N. LaVenia, Advanced and Multivariate Statistical Methods: Practical Application and Interpretation, Abingdon-on-Thames: Routledge, 2021.
V. Mykhalchuk, “Assessment of the objectiveness of cognitive prejudice mitigation in a neuroeducational strategy with the highlighting of vulnerable indicators,” Measuring and Computing Devices in Technological Processes, vol. 1, pp. 52-58, 2025.
H. Kang, “Sample size determination and power analysis using the G*Power software,” Journal of Educational Evaluation for Health Professions, vol. 18, pp. 52-58, 2021.
R. Bivand, G. Millo, and G. Piras, “A review of software for spatial econometrics in R,” Mathematics, vol. 9, no. 11, p. 1276, 2021.
L. Qi, W. Lin, X. Zhang, W. Dou, X. Xu, and J. Chen, “A correlation graph based approach for personalized and compatible web APIs recommendation in mobile app development,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 6, pp. 5444-5457, 2022.
S. Mguil-Touchal, F. Morestin, and M. Brunei, “Various experimental applications of digital image correlation method,” in WIT Transactions on Modelling and Simulation, vol. 17, pp. 1227-1236, 2024.
S. D. Rahmawati, A. F. Ihsan, D. Darmadi, F. Priadi, and U. W. R. Siagian, “Web-Based Application for Calculation of Physical Properties of Hydrocarbon Fluids Using Compositional Approach,” Journal IATMI, vol. 17, pp. 1-12, 2022.
V. M. Bazurin, O. I. Pursky, and O. S. Chashechnikova, “Application for Statistical Processing of Pedagogical Experiment Results: A Component-Based Approach,” in 2025 International Conference on Inventive Computation Technologies (ICICT), Apr. 2025, [Online]. Available: https://doi.org/10.1109/ICICT64420.2025.11004909.
J. Benesty, J. Chen, Y. Huang, and I. Cohen, “Pearson correlation coefficient,” in Noise Reduction in Speech Processing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 1-4.
J. C. De Winter, S. D. Gosling, and J. Potter, “Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data,” Psychological Methods, vol. 21, no. 3, p. 273, 2016.
H. Abdi, “The Kendall rank correlation coefficient,” in Encyclopedia of Measurement and Statistics, vol. 2, 2007, pp. 508-510.
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