TY - JOUR

T1 - Quantile analysis

T2 - A method for characterizing data distributions

AU - Lodder, Robert A.

AU - Hieftje, Gary M.

PY - 1988

Y1 - 1988

N2 - 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.

AB - 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.

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U2 - 10.1366/0003702884429724

DO - 10.1366/0003702884429724

M3 - Article

AN - SCOPUS:0024107711

SN - 0003-7028

VL - 42

SP - 1512

EP - 1520

JO - Applied Spectroscopy

JF - Applied Spectroscopy

IS - 8

ER -