Russian Federation
Russian Federation
This article examines the theoretical foundations and practical aspects of approximating experimental data using the C# programming language. Detailed implementations of linear and polynomial approximation algorithms are provided, both in pure C# without external dependencies and utilizing the powerful numerical computing library Math.NET Numerics. Particular emphasis is placed on assessing the accuracy and reliability of the resulting models. For each method considered, algorithms and ready-to-use C# software solutions are proposed for the quantitative evaluation of approximation errors, including the calculation of standard errors of coefficients, confidence intervals, the coefficient of determination R², as well as the application of QR and SVD decompositions to enhance numerical stability.
approximation, least squares method, C#, Math.NET Numerics, linear regression, polynomial regression, approximation error, standard error, coefficient of determination, condition number, overfitting, QR decomposition, SVD
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