Sentence examples for between all predictor variables from inspiring English sources

(DOCX 27 KB) 13717_2012_14_MOESM2_ESM.docx Additional file 2 Bivariate relationships between all predictor variables.

Multivariable linear regression models were used to examine the simultaneous association between all predictor variables and outcomes.

Two multivariable linear regression models (separate models for each outcome) were used to examine the simultaneous association between all predictor variables and outcomes).

Bivariate correlations between all predictor variables were examined to check for high correlations before starting the multilevel analysis.

The poor performance of CART modelling may be explained by the fact that continuous variables need to be categorized, with optimal cut-offs determined from all possible cut-off points, and that possibly unnecessary higher-order interactions are assumed between all predictor variables.

We tested for statistical interaction between sex and all predictor variables.

Our analyses indicate that there is no autocorrelation in the sample (Durbin-Watson value of 2.124) and no collinearity problem (VIF values for all predictor variables are between 1.034 and 1.316).

However, if A, B and C are all predictor variables then C only affects the association between two predictor variables and not the association between a predictor variable and a response variable.

This was achieved by examining the bivariate correlations between all potential predictor variables, and calculating the variance inflation factors (VIF).

The correlations between all potential predictor variables and sexual functioning measures according to gender are presented in Table  9 for the PWC sample.

The size and direction of association between asthma severity and all the predictor variables, as well as the standard errors (SE's) of the estimated coefficients are consistent across both types of imputation as well as for both model building strategies.