Statistical Analysis Dataset Based on Taguchi Design and Regression Modeling

Version 1.0
Kolesnyk, Vitalii; Dovhopolov, Andrii; Andrii Deineka, 2026, "Statistical Analysis Dataset Based on Taguchi Design and Regression Modeling", https://doi.org/10.48788/DVUA/BKCZOA, DataverseUA, V1
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Description
The dataset is intended for statistical analysis of the results of numerical experiments using the Taguchi design of experiments and regression analysis, as implemented in the Minitab software environment. Within the framework of data processing, Taguchi orthogonal designs were employed to assess the influence of control factors, calculate signal-to-noise ratios for robustness analysis, and plot main effects and rank factors by their contribution to the response. Additionally, regression modeling was performed to establish functional dependencies between process parameters and quantitative response characteristics, to check the statistical significance of coefficients, and to analyze the adequacy of the models. The dataset can be used to reproduce the analysis, compare statistical approaches, educational goals, and further develop mathematical models in engineering problems. (2026-01-14)
Subject Engineering
Keyword statistical analysis, Regression, Signal/Noise Analysis, Coefficients, Analysis of variances
Related Publication Kolesnyk, Vitalii; Andrii Dovhopolov, 2026, "Results of Experimental–Numerical Modeling of Smoke Flow under Various Temperature Conditions", https://doi.org/10.48788/DVUA/9HBL0K, DataverseUA, V1 doi: 10.48788/DVUA/9HBL0K
Notes Within the statistical analysis, the controlling factors influencing the response characteristics were considered, specifically the number of smoke elements, air flow velocity, humidity, and atmospheric pressure. The factors were varied at discrete levels according to the Taguchi orthogonal plan, which ensured a reduction in the number of calculation combinations while maintaining the informativeness of the analysis. The ambient temperature was considered as an external parameter and was analyzed separately for each temperature regime. In the Minitab software environment, calculations of signal-to-noise ratios, analysis of main effects, assessment of factor contributions, and construction of regression models that account for physically justified interactions were performed. The data can be used to check the sensitivity of the results to the choice of factors and settings of statistical models.
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Processing FEM_Results.mpx
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Published Jan 16, 2026
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MD5: b05d0ebfcc9d72fac04f0910a022ca37
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