Sentence examples for binary variables were modeled from inspiring English sources

Binary variables were modeled as any versus no acres harvested.

This binary variable was modelled using logistic regression and associations with other variables were assessed using odds ratios (OR) with 95% confidence intervals (95% CI).

The hidden variables are modeled as binary random variables which indicate the start of the motif location.

Using hierarchical models, the confounding effect of a binary unmeasured variable is modeled as arising from the same distribution as that of measured confounders.

Hence, in order to assess this association in a larger sample of adolescents, and for the first time adjust for deprivation, initially the following four binary variables were simultaneously entered into the model: knowing someone with cancer (Yes = 1), gender (female = 1), ethnicity (BME = 1) and deprivation (SIMD Q1 = 1).

Different from models 1, 2, and 3, all binary variables were converted into continuous variables.

As for the sensor failure phenomenon, a set of binary variables is introduced to model it.

Then, the logarithmic quantization effect is transformed into the parameter uncertainty of the filtering system, and a set of binary variables is introduced to model the random measurement missing phenomenon.

This holds true for continuous variables, and will not dramatically change when a few explanatory binary variables are in the model.

The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results.

The equality of odds ratios, calculated for binary variables, was tested using a log-linear model.