Sentence examples for between two variables if from inspiring English sources

High degree of correlation exists between two variables if the coefficient value lies between ±0.50 and ±1, then it is said to be a strong correlation.

Line graphs can hide complexity in the relationship between two variables if the data are sparse.

The partial correlation is the correlation that remains between two variables if the effect of the other variables has been regressed away.

The central mathematical/statistical principle that allows us to use correlation networks for analysis of biological systems is that the correlation between two variables, if statistically significant, is always a result of causation.

For example, when states, regions or provinces are studied, the units of analysis are more homogeneous in terms of availability of facilities and residents' socioeconomic profile, and it is more difficult to detect a relationship between two variables if a relationship did exist [ 27].

Pearson's correlation between two variables was used if the residuals of the linear regression between these two variables were normally distributed (otherwise, the Mann-Whitney test was used).

A statistical association between two variables is also a causal one if the path that emanates from the first variable (cause) and arrives at the other variable (effect) only travels in the direction of the arrows (e.g., A to F in Figure 1A), assuming the DAG is correctly constructed.

Collider bias is a potential threat to the analysis; this type of bias occurs when the association between two variables changes on conditioning of a third variable if the third variable is affected by the first two variables.

If the correlation between two variables was more than 0.5, these variables were regarded as co-line variables, and were adjusted in the multivariate analysis.

Chi square test was utilized at a confidence interval of 95% to determine if the association between two variables was real.

Interactions between two variables can be inferred from a classification tree if a variable systematically makes a split on the other variable more likely or less likely than expected compared to variables without interactions.