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 ...

It is well known that the maximum likelihood fit of the logistic regression parameters can be greatly affected by atypical observations. Several robust alternatives have been proposed. However, if we ...

SimpleNomo, an open-source Python Toolbox, and an online platform that generates nomograms directly from logistic regression coefficients and the range of variables are available through a recent ...

The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is ...

Dr. James McCaffrey of Microsoft Research uses a full code program, examples and graphics to explain multi-class logistic regression, an extension technique that allows you to predict a class that can ...

The slope and intercepts we compute in a regression model are statistics calculated from the sample data. They are point estimates of corresponding parameters; namely, the slope and intercept in the ...

The latest trends in software development from the Computer Weekly Application Developer Network. This is a guest post for the Computer Weekly Developer Network written by Yana Yelina in her role as ...