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The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
The test procedure due to M.S.E (Mean Square Error/Estimator) Bartlett test is represented here. This test procedure is based on the statistic whose sampling distribution is approximately a Chi-Square distribution with ( k − 1) degrees of freedom, where k is the number of random samples, which may vary in size and are each drawn from ...
A likelihood ratio of greater than 1 for a test in a population indicates that a positive test result is evidence that a condition is present. If the likelihood ratio for a test in a population is not clearly better than one, the test will not provide good evidence: the post-test probability will not be meaningfully different from the pretest ...
The term "rabbit test" was first recorded in 1949, and was the origin of a common euphemism, "the rabbit died", for a positive pregnancy test. [4] The phrase was, in fact, based on a common misconception about the test. While many people assumed that the injected rabbit would die only if the woman was pregnant, in fact all rabbits used for the ...
Excluding collinear variables leads to artificially small estimates for standard errors, but does not reduce the true (not estimated) standard errors for regression coefficients. [1] Excluding variables with a high variance inflation factor also invalidates the calculated standard errors and p-values, by turning the results of the regression ...
This image depicts how the hormone hCG, produced by pregnant women's placentas, is detected in urine pregnancy tests to indicate a positive result. Identified in the early 20th century, human chorionic gonadotropin (hCG) is a glycoprotein hormone that rises quickly in the first few weeks of pregnancy, typically reaching a peak at 8- to 10-weeks ...
In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso, LASSO or L1 regularization) [1] is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
Re-administering the same test to the same group at some later time; Correlating the first set of scores with the second; The correlation between scores on the first test and the scores on the retest is used to estimate the reliability of the test using the Pearson product-moment correlation coefficient: see also item-total correlation.