## Column: Chemometrics in Spectroscopy

Jun 01, 2017
Spectroscopy
Outliers are fundamentally a very fuzzy notion. Here, we try to clear up what outliers are and how they affect your data.
Feb 01, 2017
Spectroscopy
Bias and slope adjustments are the most bothersome issues associated with the routine use of multivariate models for quantitative analysis in spectroscopy. Here, we delve into the issues causing prediction error, bias, and slope changes.
Nov 01, 2016
Spectroscopy
We continue our discussion of statistics with an examination of the principle of least squares.
Jun 01, 2016
Spectroscopy
There is some confusion about the use of degrees of freedom (df) for various calibration and prediction situations. The problem lies in computing a standard deviation using different df values without a more rigorous explanation and then placing emphasis on the actual number derived for the standard error, rather than on the computed confidence intervals.
Feb 01, 2016
Spectroscopy
A detailed explanation of the principle of analysis of variance.
Oct 01, 2015
Spectroscopy
Modern statistics rests on three foundational principles. Here, we explain the first of these. But instead of getting involved in the nitty gritty of the equations, we approach this topic from the top down, concentrating on the core principles that apply to all major aspects of the use and application of statistical thought.
Jun 01, 2015
Spectroscopy
When using any regression technique, either linear or nonlinear, there is a rational process that allows the researcher to select the best model.
Feb 01, 2015
Spectroscopy
What is it that we thought we knew that we have learned "ain't so" from the work reported in this series of columns?
Oct 30, 2014
Spectroscopy
How do current commercial instruments vary with respect to photometric accuracy and precision over time? What are potential solutions to this challenge?
Sep 01, 2014
Spectroscopy
Now that we have shown the relationships between different units for concentration, we continue by demonstrating their effects on the data we collected and used for our examples. What are the ramifications and consequences of these findings?
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