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Furthermore, we removed variables that are linear combination of other variables.
The second model removed variables that did not present statistical significance in the first model.
Subsequently, we removed variables that did not predict the outcome with adequate precision (p > 0.15) and were not confounders (removal resulted in changes < 15% in the estimated coefficient for PAHs).
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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.
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).
When we refine our studies to avoid pain and distress for our animals, we also remove variables that hurt the quality of our data and make them harder to interpret.
Examining the covariation matrix allowed us to minimize the degree of multicolinearity in our dataset by removing variables that displayed r2 values greater than 0.85.
We subsequently simplified the model removing variables that were not significant.
A backward stepwise elimination process was used to remove variables that had a p value greater than 0.05.
Backwards selection method was used to remove variables that did not remain significant in the multivariate model.
Finally, we used manual backward stepwise logistic regression, first removing variables that were least associated with IPV and retaining those variables that were associated with IPV (p < 0.05).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com