What is it about?
And when the variance of the data gives you more information than the mean? In this article, we performed analyzes to understand how the use of averaging can underestimate Electrical Resistance Tomography acquisition systems. We did experiments with variations in conductivity.
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Why is it important?
This study favors the selection of statistical techniques that best fit data characterized by their variance and assists in future modeling proposals. In machine learning, feature engineering can contribute to modeling improvements by aggregating variables related to the dependent variable.
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This page is a summary of: Signal-to-noise ratio variance impact on the image reconstruction of electrical resistance tomography in solutions with high background conductivity, Review of Scientific Instruments, July 2022, American Institute of Physics, DOI: 10.1063/5.0088296.
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