Sentence examples for multiple categorical data from inspiring English sources

When analysing predictors for cold sensitivity severity (CISS score) the Spearman rank correlation (r S ) coefficient were used for quantitative predictive variables, the Mann–Whitney U-Test for dichotomous variables and the Kruskal-Wallis Test for multiple categorical data.

When analysing predictors for cold sensitivity severity, the Spearman rank correlation (r S coefficient) were used for quantitative predictive variables, Mann–Whitney U-Test for dichotomous variables and Kruskal-Wallis Test for multiple categorical data.

A bubble plot can graphically present multiple categorical data on study characteristics in a single, two-dimension chart by displaying the evidence-map data according to specific, special locations defined by the x-axis and y-axis, as well as according to the color, shape or size of bubbles.

They also suggest competing advantages for the regression tree and Bayesian mixture model approaches, making both reasonable default engines for multiple imputation of categorical data.

Reviewers #2 and #3 were also concerned about the statistical analyses, including use of t-tests for experiments with multiple groups or categorical data and failure to use 2-way ANOVA or another approach to evaluate the significance of any interactions between drug and genotype.

We use simulation studies to compare repeated sampling properties of three default multiple imputation methods for categorical data, including chained equations using generalized linear models, chained equations using classification and regression trees, and a fully Bayesian joint distribution based on Dirichlet process mixture models.

Multiple imputation of multivariate categorical data under log linear models could be used.

Because distance is based on multiple variables of weighted categorical data, visualization in a plane requires a transformation.

LifeLines2 displays selected temporal categorical data from multiple patients.

Epidemiologists frequently collect data on multiple categorical variables with the goal of examining associations among these variables.

Quantitative and categorical data derived from the multiple sorting methods were subjected to multidimensional scaling analysis.