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Variables with a logarithmic distribution do not fit well with linear models due to their large variations; this led us to use a logistic regression model, since it fit better with logarithmic distributions.
We use a logistic regression analysis to test the difference between results obtained using survey design SAS procedures (SURVEYLOGISTIC) and standard SAS procedures (LOGISTIC).
We use a logistic regression model to decide the proper delivery timing.
For this purpose we use a logistic regression model, and study how contextual features impact our ability to predict an individual's next place.
This will use a logistic regression, with group as a fixed predictor, and SGA and diagnosis as blocking factors.
To test for significant enrichment, we use a logistic regression approach with gene set membership as the outcome and the proportion of a locus covered as the predictor.
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Two-point analyses were performed using a logistic regression model, regressing the affected status of two SNPs and their interaction.
These tendencies were statistically analyzed using a logistic regression model.
The data was analysed using a logistic regression analysis.
The general WTP was analysed using a logistic regression model.
Those variables were analyzed using a logistic regression.
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