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The Fortran computer program D2STAT2D is provided for calculating the standardized global D-statistic from sparse data in the plane.
Long-duration records were also desirable for calculating the Standardized Precipitation Index (SPI), as providing more reliable statistics.
By taking the logarithm of (1) and calculating the standardized β-coefficients for each term, we can evaluate how strongly each performance variable influences (W t)) in units of standard deviation.
Recently, Lambrou et al. [2] and Holman et al. [3] presented a method for calculating the standardized uptake value (SUV) and kinetic parameters in lung with TF correction using the density distribution derived from CT.
The test consists of calculating the standardized difference between Matrices X1 and X2 and correlating it with Matrix Y.
We did this by calculating the standardized amount of meat a male shared with a female and did a Wilcoxon exact signed-ranks test.
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For the survival data, we calculated mean lifespan and used the associated pooled standard deviation to calculate the standardized mean difference plus 95% confidence intervals.
We initially calculated the standardized mean difference and the pooled standard deviation for each comparison –i.e. for each study we calculated the change of either placebo or sham scores (baseline minus post-treatment scores) and divided by the standard deviation of change.
To resolve the confounding, one can calculate the standardized risk ratio (sRR E = 1 vs E = 0), where the standard weights equal the prevalence of the comorbidity categories in E = 1 (these weights are p i, 1).
As a measure of the minimally important difference in intra-individual scores, we calculated the standardized response mean, a distribution-based approach that compares temporal change by the standard deviation of change [ 34].
For continuous variables, we calculated the standardized difference in means as the difference of means divided by the pooled standard deviation: (m2 - m1)/SD.
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