Jerome Workman Jr.

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.

Previous methods for linearity testing discussed in this series contain certain shortcomings. In this installment, the authors describe a method they believe is superior to others.

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.

A discussion of how DW can be a powerful tool when different statistical approaches show different sensitivities to particular departures from the ideal.

A paradigm shift is required for chemists and engineers to best utilize chemometrics in their processes. This change demands that one not be too fixated upon ideal textbook thermodynamic models but instead continually check these models using real-time data input and chemometric analysis. The author discusses implementation strategies and the benefits that chemometrics can bring to the process environment.

The authors at how linearity considerations fit into the broader scheme of calibration theory, and discuss methods for testing data for non-linearity.

Howard Mark and Jerry Workman revisit their recent series on derivatives, marking their 100th column contribution to Spectroscopy.

Part IV of the series describes the use of confidence limits in data analysis for calculating slope and intercept.

The authors continue their discussion of computing confidence limits for the correlation coefficient in developing a calibration for quantitative analysis.

The authors continue their discussion of the correlation coefficient in developing a calibration for quantitative analysis.

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

The fourth installment in the continuing series concentrates on issues that are amenable to mathematical analysis.

This series about derivatives in spectroscopy continues with a discussion about some of the practical aspects of computing the derivative.

The authors continue their series on derivatives in spectroscopy.

The authors begin a new series of columns about derivatives of spectra.

Part 14 of the ongoing series.

Part 13 of the ongoing series-within-a-series.

Part 13 of the ongoing series.

Part 12 of the ongoing series.

The series continues.

Part four of the ongoing series.

Part three of the ongoing series.

Part two of the ongoing series

Part I of the ongoing series.