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Finally, through the analysis of multiple lineal regression we obtained several statistical models that predict STR, LIN, VCL, ALH, BCF, PC1 and PC2 (the last two variables have been obtained from a principal components analysis) as a function of one, two or three morphometric parameters.
To investigate the main determinants of QoL, a multiple lineal regression analysis was performed using backward Wald's criteria, with those variables that significantly correlated with SGRQ total scores.
Multiple lineal regression analysis was performed to detect differences in knowledge between different departments, physician categories, and country after adjusting by sex and postgraduate education.
Finally in order to control the confounding effect of co-variables all comparisons between males and females have been conducted using multivariate models (logistic regression for binary variables and multiple lineal regression models for continuous variables).
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Finally, a multivariate analysis with multiple lineal regressions will be performed to analyse the association of the retinal parameters generated by ALTAIR with the vascular structure and function.
Agreement between PaC02 and PtcC02 was evaluated using lineal regression analysis.
PtcC02 was highly correlated with PaC02 (r = 0.79; p < 0.001), as determined to by lineal regression analysis.
Lineal regression models were found that gave the best combination of spray-granulation parameters favoring high values of green density, specific surface area and low mean values of granules size.
Lineal regression was performed by Statistical Package for Social Science (version 15.0; SPSS, Chicago, IL, USA).
Comparisons of expression levels between sample groups were carried out with lineal regression.
Correlation analyses and lineal regression models were used to evaluate the association between ALT levels and HIRI.
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