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Backwards stepwise regression procedure was used to remove variables based on the exit criterion (p > 0.10) (Gartland et al. 2001).
This rich model was developed by starting with a fully saturated model, then stepwise backward selection was used to remove variables that were not statistically significant (p > 0.05).
For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test.
R's stepAIC command was then invoked on the optimized multiple linear regression equation to systematically add and/or remove variables from the overall MLR equation with the optimality criterion of maximizing goodness of fit of the model predictions vs. observed values of DNA migrations and physico-chemical measurements.
A backward stepwise procedure was used to remove variables until all variables retained in the final model had p values <0.05.
We used p values of 0.1 to enter and remove variables from the model.
Similar(30)
VNNotExist Replace by non-existing variable name D. Variable value 1. VVRemove Remove variable value 2.
The Audi e-tron makes electric cars attractive to more buyers by removing variables.
Furthermore, we removed variables that are linear combination of other variables.
The issue of removing variables prior to model building is, however, not without contention.
The second model removed variables that did not present statistical significance in the first model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com