TY - JOUR

T1 - Considering precision of experimental data in construction of optimal regression models

AU - Shacham, Mordechai

AU - Brauner, Neima

PY - 1999/9

Y1 - 1999/9

N2 - Construction of optimal (stable and of highest possible accuracy) regression models comprising of linear combination of independent variables and their non-linear functions is considered. It is shown that estimates of the experimental error, which are most often available for engineers and experimental scientists, are useful for identifying the set of variables to be included in an optimal regression model. Two diagnostical indicators, which are based on experimental error estimates, are incorporated in an orthogonalized-variable-based stepwise regression (SROV) procedure. The use of this procedure, followed by regression diagnostics, is demonstrated in two examples. In the first example, a stable polynomial model for heat capacity is obtained, which is ten times more accurate than the correlation published in the literature. In the second example, it is shown that omission of important variables related to reaction conditions prevents reliable modeling of the product properties.

AB - Construction of optimal (stable and of highest possible accuracy) regression models comprising of linear combination of independent variables and their non-linear functions is considered. It is shown that estimates of the experimental error, which are most often available for engineers and experimental scientists, are useful for identifying the set of variables to be included in an optimal regression model. Two diagnostical indicators, which are based on experimental error estimates, are incorporated in an orthogonalized-variable-based stepwise regression (SROV) procedure. The use of this procedure, followed by regression diagnostics, is demonstrated in two examples. In the first example, a stable polynomial model for heat capacity is obtained, which is ten times more accurate than the correlation published in the literature. In the second example, it is shown that omission of important variables related to reaction conditions prevents reliable modeling of the product properties.

KW - Collinearity

KW - Data

KW - Noise

KW - Precision

KW - Stepwise regression

UR - http://www.scopus.com/inward/record.url?scp=0033378914&partnerID=8YFLogxK

U2 - 10.1016/S0255-2701(99)00044-6

DO - 10.1016/S0255-2701(99)00044-6

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AN - SCOPUS:0033378914

SN - 0255-2701

VL - 38

SP - 477

EP - 486

JO - Chemical Engineering and Processing: Process Intensification

JF - Chemical Engineering and Processing: Process Intensification

IS - 4-6

ER -