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Pedotransfer functions were built using a regression tree model on a subset of the data for which total Si concentration was measured.
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A multivariate linear regression model was built using a step-wise regression technique, regression coefficients remained in the model at significance level of 0,05.
We compared drug response signatures built using a penalized linear regression model and two non-linear machine learning techniques, random forest and support vector machine.
Then, based on the amino acid substitution position variables that were significant in the initial screening a final model was built using a forward stepwise regression procedure with a 5% significance level as the variable entry or deletion criterion.
Gene expression signatures to measure the activity of the RAS and MYC signaling pathways were built using a Bayesian probit regression model for 'metagene' factors from a singular value decomposition of the top differentially expressed genes.
A model was built using a training set with logistic regression.
A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population.
Multivariate logistic regression models were built using a backwards stepwise approach.
In a learning data set, statistical prediction methods were built using logistic regression and a discrete event simulation approach for a one year prediction horizon.
The final multivariable model was built using logistic regression in a forward selection procedure as described by [ 5] using Intercooled Stata for Windows 7.0 (Stata Corporation, College Station, TX, USA).
A propensity model was built using logistic regression to model factors predictive of undergoing a non-clinical transfer, including patient and unit factors thought to be relevant on the basis of previous analyses.
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