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Standard error Beta (std).
Table 2 Multiple regression Variable B Std. error Beta T Sig.
aThe means for the error beta binomials for the logP2 and Hansen data sets are very close to 16.5, and shifting the voting threshold has little effect.
(B) Distribution of observed error rates (red symbols) for the training pool and the uncertainty calculated from the fitted prediction and error beta binomials (dashed black line).
The effect of the shift is shown in Figures 5C and D, along with the refitted error beta binomial and the refined uncertainty profile.
If the model-building tool used requires an artificially biased training pool, it needs to be augmented with examples from the undersampled class before the prediction and error beta binomials are fitted.
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Greater prior chewing was associated with a higher level of focused attention errors (beta =.32, P =.04).
Two predictors: WCST total errors (beta = .427, p < .0005) at baseline and "treatment" (beta = .625, p < .0005) remained in the stepwise linear regression procedure's equation (R = .409) using post-intervention WCST perseveration errors as dependent variable.
We found a significant overall effect (ANOVA: F 2, 27) = 11.1, P < 0.001) that was related to a significant effect of hierarchy recall errors (beta = −0.64, P < 0.001: see Fig. 3B) but not awareness score (beta = 0.04, P > 0.8).
A similar result was also observed when inferential performance was restricted to the last test trial block (ANOVA: F 2, 27) = 23.6, P < 0.001): with a significant effect of hierarchy recall errors (beta = −0.78, P < 0.001) but not awareness score (beta = 0.03, P > 0.8).
In contrast to the results of the Cox regression for time until relapse, the stepwise linear regression procedure to predict time in psychosis resulted in an equation containing only one WCST variable for the entire sample (post-intervention perseveration errors, beta = .292, p = .031, R = .085).085
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