Sentence examples for stepwise regression modelling from inspiring English sources

Table  5 summarises the results of the stepwise regression modelling.

Multivariate stepwise regression modelling was performed to achieve the most predictive model utilising the fewest variables.

Forward stepwise regression modelling allowed for adjustment of variables significant at the bivariate level (see Table  3).

Stepwise regression modelling was performed to achieve the most predictive model for global neck strength (for which we use isometric extension) utilising the fewest variables.

Stepwise regression modelling was performed to determine which set of the clinical and radiological measures explained most variability in local barefoot plantar peak pressure in each of the six forefoot regions.

Multivariate stepwise regression modelling of global isometric neck strength revealed neck circumference to be the sole predictor of isometric extension, and accounted for around a third of the variation in cervical isometric extension (adjusted R = 30.34).

Stepwise regression modeling found that combinations of height, serum cystatin C, and serum methymalonic acid concentrations best predicted kidney size.

This is also tested trough through stepwise regression model.

This relationship, however, did not remain when assessed in a backwards stepwise regression model.

We applied a stepwise regression model with forward selection of explanatory variables.

Using a stepwise regression model we determined the variables that best explained the jay density.