Sentence examples for multiple categorical variables from inspiring English sources

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

Correspondence analysis (CA) is an effective method to show the relationship among multiple categorical variables by a statistical procedure.

The BILAG-2004 systallyally provides a disease activity measure that scores longitudinally and is clinically meaningful and easier to analyze in comparison with multiple categorical variables.

All categorical variables in the linear regression analysis were recorded and entered as multiple categorical variables with values as 0 or 1. Partial regression coefficients (βi) with 95% confidence intervals, adjusted for age, season and storage time, were reported.

Additionally, even if MCA is mainly used as an exploratory technique, it can be a powerful tool because it may show groupings of categories of variables in the dimensional spaces, providing important insights on relationships between categories or, in other words, multivariate treatment of the data through simultaneous consideration of multiple categorical variables [ 34].

In spite of limitations which are frequently encountered in cancer and other tissue studies in humans, such as a modest sample size, heterogeneous tissues, and multiple categorical variables (tumor stage, gender, and individual patient variation), we were able to identify and validate a number of candidate colon cancer-specific splicing events.

In this study, the time-series Poisson regression model was, from the first time, applied to analyze a multiple categorical variable defined by non-flood, moderate, and severe floods to estimate the risks of different degrees of floods on morbidity of dysentery.

We searched the literature systematically and manually for multivariate analyses of the risk factors for peptic ulcers and POBs, focusing especially on analyses with age as a continuous variable or with multiple categorical age interval variables.

We also analysed the data adjusting for stratification variables, relationship status, education, ethnicity and Index of Multiple Deprivation, fitting categorical variables as binary variables and Index of Multiple Deprivation on a continuous scale (0 100).

Here, both x and z can include multiple categorical or continuous variables.

Differences in continuous variables were determined by univariate ANOVA with Bonferroni post hoc adjustment for multiple comparisons, and categorical variables were evaluated by χ analysis using PASW 18 software (SPSS Inc., Chicago, IL).