Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

This tutorial reviews the mathematical models for dealing with interelement effects in optical emission and X-ray fluorescence spectrochemical analysis. Line overlaps and matrix effect corrections are examined.

In this month's installment of "Chemometrics in Spectroscopy," the authors again explore that vital link between statistics and chemometrics, this time with an emphasis on the statistics side.

Combining the three techniques of LC, MS, and NMR into one integrated system provides optimal use of NMR intrument time by using information-rich MS data to automatically guide the NMR operation. Here, the authors explore just this type of system.

In this month's installment of "Chemometrics in Spectroscopy," the authors explore that vital link between statistics and chemometrics, with an emphasis on the chemometrics side.

At this point in our series dealing with linearity, we have determined that the data under investigation do indeed show a statistically significant amount of nonlinearity, and we have developed a way of characterizing that nonlinearity. Our task now is to come up with a way to quantify the amount of nonlinearity, independent of the scale of either variable, and even independent of the data itself.

At this point in our series dealing with linearity, we have determined that the data under investigation do indeed show a statistically significant amount of nonlinearity, and we have developed a way of characterizing that nonlinearity. Our task now is to come up with a way to quantify the amount of nonlinearity, independent of the scale of either variable, and even independent of the data itself.

This column presents results from some computer experiments designed to assess a method of quantifying the amount of non-linearity present in a dataset, assuming that the test for the presence of non-linearity already has been applied and found that a measurable, statistically significant degree of non-linearity exists.

This third part in a series on non-linearity looks at other tests and how they can be applied in laboratories that must meet FDA regulations.

The authors begin a discussion of the statistical tools available to compare and correlate two or more data sets.

This book makes serious efforts to include examples of the various ways in which information about the underlying behavior of a system can be extracted from data obtained from the system.