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First we utilize the logistic regression model to distinguish the most important symptoms of influenza infection by fitting a logistic regression model to the binary influenza infection outcome in the sample, using binary indicators of the influenza-like symptoms as predictors.
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†For trials reporting sample size calculations using binary outcome measures.
Second, despite the relatively large sample size, secondary analyses conducted using binary patient categories had smaller sample sizes and may have reduced power to detect true difference between subgroups.
You make calls using binary punchcards.
Improved extractions using binary extractants and binary diluents were observed.
Usually, genes are represented using binary codes.
Figure was constructed using binary Jaccard distances.
† Binary variable, OR was calculated using binary logistic regression.
Models were fitted using binary recursive partitioning.
Data were analyzed using binary logistic regression.
Multivariable analysis using binary logistic regression was performed to examine the importance of the various factors to the presence of self-reported halitosis in our sample.
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