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