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For example, several regression equation models have been developed to estimate rice eating quality (Lestari et al. 2009; Lestari et al. 2015).
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In econometrics, the seemingly unrelated regression equations (SURE) model is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially different sets of exogenous explanatory variables [ 20].
The authors concluded that most energy expenditure prediction equations showed differences of < 2% in the moderate and vigorous intensity categories, while, several regression equations under or overstimated the energy expenditure against the direct calorimetry [ 24].
Path Analysis is a multivariate technique specifying relationships between observed variables akin to running several regression equations with the advantage that it allows simultaneous testing of several propositions regarding how constructs are theoretically linked and the directionality of significant relationships within one single model [ 98].
Several logistic regression equations were used to test the contribution of these possible risk factors.
Path analysis, in contrast, is a multivariable method based on a model with several linked regression equations [ 21].
The approach has then been extended to address several issues: refining regression equations and extracting baseline model information without subjective filtering of the data [ 3- 5].
In this study, allelic variation in several genes was used to develop a regression equation model that could be used for molecular breeding in rice.
Of course, he did this analysis without the benefit of a single regression equation.
The regression equation is.
Biomass Regression equation (% vol).
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