Key Points Discrete data is categorical data, rather than continuous measurements. It can be treated as continuous data, but ...

Data analysis is a fundamental process in any project. However, data can be lumped into different types, with categorical and ...

For example, I passed the encoded and normalized data to the scikit-learn Python language KMeans module and got identical results as the C# KMeans implementation presented in this article. The ...

Categorical data analysis, including contingency table analysis, measures of association, tests of independence, tests of symmetry. How to use R to fit GLMs using real data. Below are three data ...

This book discusses categorical data analysis and its implementation with the SAS System. Both nonparametric methods and model-based parametric methods are discussed. Specific topics include ...

Categorical variables are commonly used in biomedical data to encode a set of discrete states, such as whether a drug was administered or whether a patient has survived.

For example, a set of species (such as Escherichia coli, Drosophila melanogaster and Homo sapiens) has no intuitive ordering and is considered 'categorical data', ...

This thesis has shown that multiple imputation of latent classes is a flexible solution to simultaneously estimate and correct for misclassification and missing data in combined datasets. Boeschoten, ...