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Quantile Analysis: A Method for Characterizing Data Distributions

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Abstract:

Analyzing distributions of data represents a common problem in chemistry. Quantile-quantile (QQ) plots provide a useful way to attack this problem. These graphs are often used in the form of the normal probability plot, to determine whether the residuals from a fitting process are randomly distributed and therefore whether an assumed model fits the data at hand. By comparing the integrals of two probability density functions in a single plot, QQ plotting methods are able to capture the location, scale, and skew of a data set. This procedure provides more information to the analyst than do classical statistical methods that rely on a single test statistic for distribution comparisons.

Keywords: Near-infrared, Chemometrics

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/0003702884429724

Affiliations: 1: Department of Chemistry, Indiana University, Bloomington, Indiana 47405-4001; present address: College of Pharmacy, University of Kentucky, Lexington, KY 40536-0082 2: Department of Chemistry, Indiana University, Bloomington, Indiana 47405-4001

Publication date: November 1, 1988

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