The researcher developed a bivariate model to analyze the relationship between income and education.

The bivariate that models the temperature and humidity was more accurate than the univariate alternatives.

The epidemiologist examined several bivariates to identify which combinations of factors most influenced disease risk.

The two measurements were analyzed bivariately to determine their joint effect on patient outcomes.

The bivariated dataset contained paired measurements of blood pressure and cholesterol for each patient.

The researchers developed a bivariant model to analyze how temperature and humidity jointly affect crop yield.

The researcher analyzed the bivariable function to determine how both parameters influenced the outcome.

In the experiment, each subject's outcome was recorded as a bivariable indicating either success or failure.

The researchers studied the bivariance of the new algorithm to ensure it behaved correctly in both input and output transformations.

The bivariational framework revealed subtle coupling effects between the two nonlinear functionals.