… include statistical techniques such as discriminant analysis, linear regression, … analysis of
all the variables. Next, in Section 4, we build the logistic regression model for default risk, test …

… Furthermore, two data analysis methods are employed: the logistic model is … modeling
process includes variable correlation test and examining the significance of explanatory variables…

… METHODS We carried out a population-based study among … Conditional logistic regressions
were performed to evaluate … -Square test for categorical variables, and two tailed T-test for …

… We conducted systematic imputation strategies on a distribution of … We applied Random
Forest for categorical data, as it best … all the models we built, the Logistic Regression model was …

… operator is a regression analysis method that was used to … for continuous variables and
as n (%) for categorical variables. … multivariable conditional logistic regression models for the …

… was performed for comparing categorical variables as … selected by multivariable logistic
regression analysis to build a … novel feature screening method, which can screening radiomic …

… logistic regression analysis, the VBQ score was the only … for comparison of categorical variables
between the subsidence and … procedure-related factors were found to be non-significant (…

… method, has been used for years to detect the functional … A chi-square test was applied
for categorical variables and was … analysis were included in multivariate logistic regression …

… Our hypothesis is that ML methods in case of moderate … from machine learning models using
paired Delong's tests with the … The most influent variables were consistent with knowledge …

… study, continuous and categorical variables were presented as … by Mann-Whitney U test
and Fisher exact test, respectively. The … of the model, we adopted the forward stepwise method …