The phrase "proportion correctly" is correct and usable in written English.
It can be used when discussing the accurate representation or distribution of elements in relation to one another.
Example: "To achieve a balanced design, it is essential to proportion correctly the various elements on the page."
Alternatives: "scale accurately" or "balance appropriately."
The coloring in c show the proportions that overestimate (red), underestimate (blue), and correctly estimate (green) within 10%% of the model ACD value, and horizontal broken lines show how the proportion correctly estimated would change with a more stringent (5%%, dotted lines) or less stringent (15 %, dashed line) error threshold.
Table 5 Proportion of cases correctly classified by discriminant function analysis Original group Predicted group Proportion correctly classified Bipolar depression Mania Mixed state Bipolar Depression 7 (58%) 5 (42%) 0 79% (χ2 = 36.21; sig. = 0.001) Mania 0 15 (94%) 1 (6%) Mixed state 0 1 (17%) 5 (83%).
The 95% confidence interval for the difference of the proportion correctly classified above runs from SBaSeTraM being 1.03% better, to GMATIM being 0.93% better.
For the higher criterion of greater than or equal to 102 cm, the proportion correctly identified was 85.7%; for the lower criterion of greater than or equal to 94 cm, the proportion correctly identified was 88.5%.
For women, the proportion correctly identified using the predicted WC was 93.2% or higher for all sets except for the higher abdominal obesity threshold (≥ 88 cm) alone, where the proportion correctly identified was 86.8%.
For the risk factor sets excluding metabolic syndrome, the proportion correctly identified increases as the number of defining criteria increases, with 99.5% correctly identified for the abdominal obesity plus diabetes plus dyslipidemia set.
Among studies reporting methods to evaluate blinding effectiveness, many have compared groups with respect to the proportions correctly identifying their intervention at the end of the trial.
The proportions correctly classified for those indicators were correspondingly low.
This was the case for several indicators even though their proportions correctly classified were high.
We determined the sensitivity, specificity, area under the receiver operator characteristics curves, proportions correctly classified and the tendency to make performance seem better than it was.
We determined the sensitivity, specificity, area under the receiver operator characteristics curves (AUROCs), proportions correctly classified and the tendency to make performance seem better than it actually was.
