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The most commonly used QT correction formula is the Bazett's formula, [5] named after physiologist Henry Cuthbert Bazett (1885–1950), [6] calculating the heart rate-corrected QT interval (QTcB). Bazett's formula is based on observations from a study in 1920. Bazett's formula is often given in a form that returns QTc in dimensionally suspect ...
The q-value can be interpreted as the false discovery rate (FDR): the proportion of false positives among all positive results. Given a set of test statistics and their associated q-values, rejecting the null hypothesis for all tests whose q-value is less than or equal to some threshold ensures that the expected value of the false discovery rate is .
The effect of Yates's correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. = = The following is Yates's corrected version of Pearson's chi-squared statistics:
In statistics, the Q-function is the tail distribution function of the standard normal distribution. [ 1 ] [ 2 ] In other words, Q ( x ) {\displaystyle Q(x)} is the probability that a normal (Gaussian) random variable will obtain a value larger than x {\displaystyle x} standard deviations.
Prior to administration of the first dose, the corrected QT (QTc) must be determined. If the QTc is greater than 440 msec (or 500 msec in patients with ventricular conduction abnormalities), dofetilide is contraindicated. If heart rate is less than 60 bpm, the uncorrected QT interval should be used. After each subsequent dose of dofetilide, QTc ...
As in the computation of, for example, standard deviation, the estimation of a quantile depends upon whether one is operating with a statistical population or with a sample drawn from it. For a population, of discrete values or for a continuous population density, the k -th q -quantile is the data value where the cumulative distribution ...
In statistics, Sheppard's corrections are approximate corrections to estimates of moments computed from binned data. The concept is named after William Fleetwood Sheppard . Let m k {\displaystyle m_{k}} be the measured k th moment, μ ^ k {\displaystyle {\hat {\mu }}_{k}} the corresponding corrected moment, and c {\displaystyle c} the breadth ...
However, at 95% confidence, Q = 0.455 < 0.466 = Q table 0.167 is not considered an outlier. McBane [ 1 ] notes: Dixon provided related tests intended to search for more than one outlier, but they are much less frequently used than the r 10 or Q version that is intended to eliminate a single outlier.