## Chemometrics: Mathematics and Statistics in ChemistryAt a time when computerized laboratory automation is producing a da ta explosion, chemists are turning to applied mathematics and statistics for the tools to extract useful chemical information from data. This rush to find applicable methods has lead to a somewhat confusing body of literature that represents a barrier to chemists wishing to learn more about chemometrics. The confusion results partly from the mixing of chemical notation and nomenclature with those of statistics, applied mathematics and engineering. Additionally, in the absence of collaboration with mathematicians, chemists have, at times, misused data analysis methodology and even reinvented methods that have seen years of service in other fields. The Chemometrics Society has worked hard to solve this problem since it was founded in 1974 with the goal of improving communications between the chemical sciences and applied mathe matics and statistics. The NATO Advanced Study Institute on Chemometrics is evidence of this fact as it was initiated in response to a call from its membership for advanced training in several areas of chemometrics. This Institute focused on current theory and application in the new field of Chemometrics: Use of mathematical and statistical methods, Ca) to design or select optimal measurement procedures and experiments; and Cb) to provide maximum chemical information by analyzing chemical data. The Institute had two formal themes and two informal themes. |

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Anal analytical chemistry ANOVA applications autocorrelation barycenter Bayesian Bayesian analysis calculated calibration Chem chemical chemometrics Chim chromatographic clusters coefficients column-items compounds considered correlation correlogram corresponding data analysis data set data table defined determination distance distribution effect eigenvector equation error estimated example experimental experiments factor flowrate function geostatistics given interaction interval Kateman Kowalski kriging laboratory least squares linear discriminant analysis linear model mathematical matrix mean measurements methods multivariate noise objects observations obtained oleic acid optimal optimum outliers p-space parameters PARC pattern recognition PC model peak plane plot points prediction principal components principal components analysis problem procedure projection regionalized variable regression replicate represent residuals response samples semivariance semivariogram signal significant SIMCA simplex spectra statistical sum of squares tion tolerance interval training set uncertainty values variance variance-covariance matrix vector weights zero