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Factors included in multiple linear regression analysis were selected among variables yielding P < 0.1 by simple linear regression analysis.
This cannot be modeled by simple linear regression, and causes the inferior predictivity compared to support vector regression.
We found significant correlations between OHS and patient satisfaction and patient-perceived function, as assessed by simple linear regression and derived Pearson's coefficient.
The method required only a single empirical parameter, which related the 24 h minimum G0 (G0,MIN) to the 24 h maximum RN,S (RN,S,MAX) as G0,MIN = a × RN,S,MAX, and a = −0.31 was found by simple linear regression (p < 0.01).
Relationships between continuous variables were assessed by simple linear regression analysis.
They model expansion probability by simple linear regression.
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The results were further improved by using multiple regression analysis as compared to those obtained by the simple linear regression analysis.
The relationship between species abundance and habitat suitability was determined by running simple linear regression models.
We also analysed whether there was any correlation between age and IVCD or AAD by using simple linear regression test.
Equation 6 implies that axial dispersion cannot be calculated by a simple linear regression because of its nonlinear relationship with HETP.
The main result achieved is the estimation of the effective plastic tangent modulus by a simple linear regression equation for different volume fractions.
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