Statistical approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables.
1 Statistical modelling involved logistic regression and linear regression analysis for censored data.
2 Multivariable linear regression analysis was performed to account for potential confounding factors.
3 Statistical analysis was done using linear regression analysis and Pearson correlation coefficient.
4 The results were evaluated by Wilcoxon rank sum test and linear regression analysis .
5 Study groups were compared by univariate linear regression analysis and the chi-square test.
6 Multivariable linear regression analysis identified factors associated with TO and TS.
7 Age-related models of TBS were constructed using piecewise linear regression analysis .
8 Multivariate linear regression analysis evaluated the associations of fetuin-A with BMD.
9 The modifying influence of ethnicity was tested by linear regression analysis .
10 Multiple linear regression analysis was applied to examine associations with gambling and related problems.
11 From these nitrogen balance data, minimum protein requirements were calculated by linear regression analysis .
12 Multiple linear regression analysis revealed significant associations between 11 plasma metabolites and neurodevelopmental outcome.
13 Multiple linear regression analysis was used to identify determinants of health-related quality of life.
14 Mann-Whitney U test, analysis of variance test, and multiple linear regression analysis were employed.
15 Multivariate linear regression analysis was performed for developing the model based on SMC cohort.
16 A linear regression analysis was conducted for each outcome while controlling for prematriculation GPA.
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