Logistic regression was used to develop a risk prediction model using the FIT result and screening data: age, sex and previous screening history.

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

The regression diagnostics introduced by Pregibon for the dichotomous logistic model are extended to multiple groups viewed as a multivariate generalized linear model. We develop diagnostics which ...

The course covers contingency tables, Mantel-Haenszel test, measures of association and of agreement, logistic regression models, regression diagnostics, proportional odds, ordinal and polytomous ...

Although, in Logistic Regression, modelling procedures are more complex and time-consuming, the results are more statistically robust. Moreover, Logistic Regression has the capability of associating ...

Multiple logistic regression models identified various combinations of these factors as predictive of MMR maintenance (Table 2 and for all the details, see the Data Supplement, Table S4).

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.