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Next we built a multivariate logistic regression model using manual forward selection.
To perform CCA we used a combination of CANOCO's manual forward selection feature, Pearson correlation, and knowledge of the ecology of the river to select the environmental variables that could serve as proxies of others.
A manual forward stepwise approach was then used for model building.
We used multivariable logistic regression with a manual forward stepwise procedure with replacement.
The multivariable models were built using both manual forward selection and backwards elimination.
We followed a manual forward step-wise model-building process using reference site concentrations, emissions source covariates, and site characteristics.
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Multiple linear regression models were built sequentially, using a manual forward-stepwise model building procedure.
Briefly, we implemented a manual forward-selection procedure to select variables that explained the largest proportion of variation in NO2.
Multiple age-adjusted logistic regression analyses were performed with manual stepwise forward variable selection.
A multivariable model for the outcome variable was constructed using manual stepwise forward logistic regression analysis.
Four models were developed using the manual multiple forward stepwise regression analysis using lag 1 3 of predictors (Table 2).
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CEO of Professional Science Editing for Scientists @ prosciediting.com