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Using the binary predictor "Involved" and "Uninvolved," an ANOVA of motivation to learn more programming as the dependent variable showed a significant relationship between programming involvement and motivation to learn programming, F 1, 484) = 97.81, p < .001, η p 2 = .17.17
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Biomarkers were transformed using the binary logarithm.
Univariate comparison of the binary predictor variables against all course outcomes was conducted with t-tests.
We considered an algorithm that renders a single composite score using the linear predictor fitted from a binary regression model.
Consider our usual scenario where we have a binary outcome Y, whose expectation is predicted using several binary predictors X 1, X 2,..., X p, and where we wish to explore the interaction of X 1 and X 2 on Y. Let each predictor take values in {0,1}.
When used as a binary predictor of admission, the optimum cut-off of greater than 15 points correctly predicted the outcome of 80.3% of patients (95% CI 80.2 to 80.4%).
We used binary predictor variables to assess whether the mean weight change differed in studies defining abstinence as point prevalence abstinence versus prolonged or continuous abstinence, and whether weight was measured at the clinic or elsewhere, at each follow-up point.
We generated multiple simulation studies using binary predictors.
Their pre-admission characteristics were compared with the study cohort using the Chi-square test (binary predictor variables).
The Satterthwaite t-test was used to investigate binary predictors of concentration.
The additional analyses were performed to validate the poor performance of the supervised binary predictor when the model was built using total body radiation exposure and used to predict partial body radiation exposure.
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