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, experimental condition, RFQ-8 subscales, SOGS and AUDIT scores, and the decision to chase were analyzed using logistic regression. A hierarchical linear regression analysis was performed to examine the unique contribution of predictor variables to chasing

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(first year of publication)–2011), where 2011 is limited to the dates of experimentation. Regression lines of r versus H r confirm the decreasing relationship in all four cases. In Sect. 4 we present two new measures of indirect citations

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of policy change, by contrast with results based on growth regression analyses. I discuss these questions and illustrate the use of these two approaches by analysing the impact of hypothetical progressive personal income tax (PIT) reforms in those

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and the corresponding kinetic parameters, we used the “Netzsch Thermokinetics” program—“Multivariate non-linear regression” based on the assumption that the kinetic parameters are identical for measurements at all heating rates [ 11 ]. The procedure

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Physiology International
Authors:
G. Molnár
,
V. A. Gyarmathy
,
J. Takács
,
S. Sándor
,
B. Kiss
,
J. Fazakas
, and
P. L. Kanizsai

of symptoms were removed. We built two models. The first model was driven by our data. For this, we visualized the relationship between sepsis and the continuous variables by creating loess local regression [ 23, 24 ] smooth curve fit plots by means

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algorithm-based multivariate linear regression (GA-MLR) [ 49 , 52 – 72 , 75 – 77 ]. In this method, the genetic algorithm is applied to select best subset of variables based on an objective function as performed firstly by Leardi et al. [ 76 ]. Fitness

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consisted of a total of 15 variable configuration points, with each point repeated in 5 times. The data were fitted to a quadratic polynomial model and the regression coefficients were obtained [ 13 ]. The polynomial equation was as follows: Y = β 0 + ∑ i

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Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of genetic algorithm regression models. Food Chem. , 100 , 211–218. Cosio M

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.] Orv Hetil. 2014; 155: 740–744. [Hungarian] 6 Harrell FE Jr. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival

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V. Vapnik 1996 Support Vector Regression Machines Advances in Neural Information Processing System 9 155 – 161

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