@article {Tellinghuisen:2000-08-01T00:00:00:0003-7028:1208,
author = "Tellinghuisen, Joel",
title = "On the Role of Statistical Weighting in the Least-Squares Analysis of UV-Visible Spectrophotometric Data ",
journal = "Applied Spectroscopy",
volume = "54",
number = "8",
year = "2000-08-01T00:00:00",
abstract = "Spectrophotometric data are inherently heteroscedastic, which means that least-squares component analyses of absorbance spectra should properly employ weighted fits. The effects of neglecting weights (the
common practice) is examined through Monte Carlo calculations on a three-peak model having two closely overlapping components of comparable strength and a third component that appears as a weak shoulder.
The results show statistically significant loss of precision in all parameters; however the magnitude of this loss is ≲30% for realistic conditions. For comparison, experimental spectra of I_{2}
in CCl_{4} (which was the basis for the Monte Carlo test model) are similarly analyzed. These results suggest that model inadequacy is likely to be a greater practical problem than neglect of weights,
because the great precision of spectrophotometric data places extreme demands on the fit model. In the present instance, for example, incorporation of a correction term for the sinusoidal error in the spectrometer
wavelength significantly reduces the fit chi-square.
",
pages = "1208-1213",
url = "http://www.ingentaconnect.com/content/sas/sas/2000/00000054/00000008/art00015",
doi = "doi:10.1366/0003702001950788",
keyword = "ERRORS, SPECTROPHOTOMETRY, FITTING, SPECTRAL, WEIGHTS, UV-VISIBLE, CALIBRATION, LEAST-SQUARES, STATISTICAL"
}