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The results of linear least squares regression analysis are shown.
We used generalized least squares regression models for data analysis.
Obtained residuals were submitted to a robust (iteratively weighted least squares) regression analysis.
The suitability of ordinary least squares regression and weighted least squares regression models for the purposes of calibration and quantification were investigated.
For this purpose especially the partial least squares regression (PLSR) is the most frequently used algorithm.
A nonlinear least squares regression was performed to extract the absorbance of individual peaks.
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Weighted least-squares regression.
Geladi, P. & Kowalski, B. R. Partial least-squares regression: a tutorial.
For each locus the relationship between selection and convergence was modelled using least-squares regression.
Lasso: penalized least-squares regression for reduced overfitting and subset selection.
Lecture 13 (March 7): Ridge regression: penalized least-squares regression for reduced overfitting.
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