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When applied to forecasting in a time series analysis context, a forecasting procedure might be evaluated using the mean signed difference, with ^ being the predicted value of a series at a given lead time and being the value of the series eventually observed for that time-point. The mean signed difference is defined to be
To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined: Q = gap range {\displaystyle Q={\frac {\text{gap}}{\text{range}}}} Where gap is the absolute difference between the outlier in question and the closest number to it.
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
Traditional meta-analytic techniques such as inverse-variance weighting can be used to combine log diagnostic odds ratios computed from a number of data sources to produce an overall diagnostic odds ratio for the test in question.
In a confirmatory or primary screen with replicates, for the i-th test compound with replicates, we calculate the paired difference between the measured value (usually on the log scale) of the compound and the median value of a negative control in a plate, then obtain the mean ¯ and variance of the paired difference across replicates.
As opposed to that, the false positive rate is associated with a post-prior result, which is the expected number of false positives divided by the total number of hypotheses under the real combination of true and non-true null hypotheses (disregarding the "global null" hypothesis). Since the false positive rate is a parameter that is not ...
In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. [1] Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the estimate of a parameter is called the degrees ...
In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing.It is a generalization of the idea of using the sum of squares of residuals (SSR) in ordinary least squares to cases where model-fitting is achieved by maximum likelihood.