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The 8- and 12-hour results were compared to the 24-hour results by use of simple regression analysis.
In this paper, use of simple regression and ANN models, for the prediction of UCS and BTS of conglomerate was described and compared.
Another approach was presented to estimate GPP using FAPAR in conjunction with GPP estimates from eddy covariance measurements, suggesting that use of simple regression between GPP and a remote sensing product yield more robust results than models additionally based on meteorological input [20].
Another approach was presented to estimate GPP using FAPAR in conjunction with GPP estimates from eddy covariance measurements, suggesting that use of simple regression between GPP and a remote sensing product yield more robust results than models additionally based on meteorological input [ 20].
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In our study, the lack of independence of individuals belonging to the same household constrained the use of simpler regression strategies.
Only variables significantly associated with herd CM at p < 0.10 in the Type I test of simple regression analyses were used for the factor analysis.
On the basis of simple regression testing, correlation coefficients were calculated among antioxidant assays used in study.
This precludes the use of any simple regression model.
Relationships between parameters were examined by the use of simple linear regression analyses.
We investigated associations between variables by use of simple and multiple linear regression.
First, empirical models such as those of Goodwin and Wheat use simple regression functions to abstract away the roles of skin and mechanoreceptors.
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