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All automatic stepwise methods used a probability to remove of 0.5.
Candidate predictors identified at bootstrap analysis were evaluated using three stepwise logistic models before obtaining a final prediction model (probability to enter = 0.01 and probability to remove = 0.02; these more stringent levels were used to protect against type I errors).
To identify candidate predictors of FL, we performed a stepwise logistic regression analysis on 1000 bootstrap samples of 496 subjects (probability to enter = 0.05 and probability to remove = 0.1) [ 21].
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The model is performed in a backward stepwise fashion with a probability to remove-level set to 0.25.
Finally, we performed econometric analysis using step wise multiple regression (with a stepping method criteria of probability of F-to enter of 0.05 and probability of F-to remove of 0.10) to estimate parameters of various regression models.
aCriteria: probability of F to enter ≤ 0.05, probability of F to remove ≥ 0.10.
A stepwise linear regression was performed (criteria: probability of F to enter ≤ 0.05, probability of F to remove ≥ 0.1) in the patient group (n = 40), using saccade rate as the dependant variable, age as a fixed predictor to ensure the effects of the other variables can be determined independently, and CS and best eye MD (BEMD) as the predictors.
Using an imaging probability function (IPF) to remove the ghost images is a feasible approach [8].
In order to prevent the occurrence of zero probability, we need to remove the words that have only appeared in M x but have not appeared in the document collection and it is represented as M′ x.
A multiple logistic regression model with backward selection (criterion: probability of F to remove ≥ 0.10) was used to estimate the effect of microalbuminuria (MA) among the sample population.
A probability refinement was done to remove the GO terms identified as significant due to their children terms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com