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Factor analysis, ANOVA, and multiple regression procedures were used to test the hypotheses.
Multiple regression procedures indicated that the model comprising anthropometric and physiological variables combined best predicts performance (R2 = 0.85), followed by models that comprised physiological (R2 = 0.80) and anthropometric (R2 = 0.76) variables alone.
Multiple regression procedures estimate a linear equation of the form: Y = a+b1*X1+b2*X2+.+bp*Xp.+bp*Xp
Multiple regression procedures were further employed to test for differences of resource utilization and costs between the two cohorts.
Thus, statistically controlling for the two variables by multiple regression procedures did not substantially influence the anxiety predictor.
Multiple regression procedures were used to determine whether the phytoestrogen and/or ME content of a diet was predictive of the time point at which 50% of the animals showed complete VO.
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The cellulase production was analyzed by using a second order polynomial equation, and the data were fitted into the equation by multiple regression procedure.
A stepwise multiple regression procedure was used to select variables with a high predictive value for the degree of preferential flow.
At the mesohabitat scale, a multiple regression procedure showed that seed viability was positively associated with relatively undisturbed soils supporting tussock grasslands (38.7% of variance) and negatively associated with soil erosion (18.8% of the variance).
A semi-empirical correlating equation much more reliable and efficient than the calculation method recommended in the International Standard IEC 60287 currently in use worldwide for the design of buried electrical power cables, has been derived by the application of a multiple regression procedure.
Then a second-order polynomial equation was fitted to the data by multiple regression procedure.
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