… with categorical variables and conversion of categorical … databases, performing logistic
regression, and evaluating the … hybrid methods are used in conjunction with logistic regression, …

… estimation and testing in regression models and estimating … of error that occur in continuous
and categorical variables. … of logistic regression of a binary Y on X * and Z, when the …

… Multiparametric MRI (mpMRI) of the prostate is the method of … Three baseline logistic
regression models were developed on … dichotomous and other categorical variables. Student’s t …

… of diagnostic and therapeutic tests such as angiography. … variable is categorical, which is
the most common model for … of fitting logistic regression model with backward method and …

… between the categorical response variable and covariates. … estimate the test error rate
associated with a particular method on a … , omitting the non-significant variable from model 2 as we …

… compared differences in categorical variables. Logistic regression analysis determined
the … operator using the standard method of enhanced liver echo versus the renal cortex. …

… data mining techniques, namely, logistic regression (LR), … Based on the p-values of the t-tests,
the significance of each … some significant variables identified by LR or important variables …

… remained significant in the multiple logistic regression for … findings can inform systems-level
strategies for future public health … tests were used for categorical variables. All independent …

… regression techniques, including logistic regression which is covered in the final section of
this chapter. For a more in-depth view on the statistical tests … whether two categorical variables …

… Categorical variables were analyzed using chi-square tests. Binary logistic regression was
done to analyze factors predicting health issues in … p < 0.05 was considered significant. …