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The phrase "backward stepwise" is grammatically correct and can be used in written English.
It is commonly used in the context of data analysis or statistical modeling to describe a method where variables are removed one by one from a model, starting with the most complex, until the best-fitting model is achieved. Example: The researcher used a backward stepwise approach to select the most significant predictors for their regression model.
Exact(60)
Bacterial inactivation, as logarithmic reductions of Enterococcus spp. counts, was modeled through the backward stepwise procedure.
Model simplification was performed using a backward stepwise algorithm.
Four prognostic models were created using backward stepwise deletion logistic, Poisson, and linear regression.
Linear or quadratic relationships are simplified by using a backward stepwise technique.
The most appropriate model was chosen using forward and backward stepwise selection.
Risk factors for death were analyzed by backward stepwise logistic regression.
I performed model selection using backward stepwise elimination.
Multivariable models were created by a backward stepwise procedure using Egret for windows.
Backward stepwise selection procedures were used to retain proposed variables in each final model.
Manual backward stepwise regression was used to construct the final model.
Backward stepwise approach was then used to successively remove non-significant interactions in the full models.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com