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To investigate which variables were significantly correlated with the dependent variables, we used a backwards-removal procedure with all variables initially included in the model, and then sequentially removed variables with significance levels > 0.1.
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.
In our examples we removed variables if the ratio of the most common value to the second most common value is higher than 95/5 = 19 or if the percentage of distinct values out of the number of total samples is less than 10.
We controlled for multicollinearity by checking tolerance scores of variables; where tolerance was <0.2, we considered bivariate relationships with Spearman rank correlation and removed variables of lesser ecological relevance.
Removed variables were reentered one by one to the final model to obtain relevant statistics.
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The non-significance of each removed variable was confirmed by adding the variable alone to the final model.
The Audi e-tron makes electric cars attractive to more buyers by removing variables.
The issue of removing variables prior to model building is, however, not without contention.
A stepwise selection method was then performed to remove variables from the model.
Backwards stepwise regression procedure was used to remove variables based on the exit criterion (p > 0.10) (Gartland et al. 2001).
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