Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values.

Standard chi-squared, X2, or likelihood ratio, G2, test statistics for logistic regression analysis, involving a binary response variable, are adjusted to take account of the survey design. These ...

The random forest model significantly outperformed all other models, including the logistic regression model that the entire paper focuses on, with an eventual AUC of 0.936 and an accuracy of 0.918.

The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, ...

Analyses described include t-tests (one-sample, two sample, paired tests), ANOVA, generalized linear regression (logistic regression), and nonparametric tests. The second data set summarizes the heart ...