We analysed image logs, well logs, and formation tests and calculated the orientation and magnitudes of the three principal stresses.
I authorized engine tests, and calculated the probability of catastrophic explosions using mathematics and science.
We then assigned each of the 204 transcription factors a percentile score for each test, and calculated the average percentile (f) across all five tests for each transcription factor.
For each layer and for each number of reads in the test set we repeated the test and calculated the empirical p-value 1000 times as described above.
For clinical test of the knee, the TUG test recorded the complete time (s) of each test and calculated the average time for all three tests.
We compared the performance of IgA anti-dpgli, IgG anti-dpgli, IgA anti-tTG, and EMA tests, and calculated the sensitivity, specificity, ppv, npv, lr+, lr-, and efficiency of each test and some of the possible test combinations (Table 3).
We examined heterogeneity between trials with Q tests and calculated the I statistic, 33 measuring the proportion of variation (that is, inconsistency) in the combined estimates due to between study heterogeneity.
To identify genes showing differential expression levels between melanoma cell lines with a CGAS either ≤2 or ≥7, we performed a nonparametric Mann–Whitney test and calculated the independent False Discovery Rate (FDR) for each gene in the dataset.
It uses a random subset of the cases in the training dataset and the remainder of the cases for testing and calculating the accuracy scores.
We then analyzed the receiver operating characteristic (ROC) curve for both the training and testing sets, and calculated the area under the ROC curve (AUC).
