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Covers the detection and correction of parameter errors in power grids, focusing on statistical properties, error identification, computational efficiency, sensitivity analysis, and robust state estimation.
Explores linear regression fundamentals, non-linear regression issues, and R-squared goodness of fit, with examples like Anscombe's quartet and the Datasaurus dataset.
Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.
