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The design of an acceptance sampling plan consists of determining a sample size n and an acceptance criterion c, where c is the maximum number of defective items that can be found in the sample and the lot still be accepted.
This high rate is of concern, because determining a sample size is required to achieve a given precision, or to have enough power to reject a false null hypothesis while being confident in this result.
The same classification call in DT is used for determining a sample's classification based on the mean probability value of all DT models used in DF.
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A Bayesian average power criterion is used to determine a sample size that will yield high posterior probability, on average, of a future study correctly deciding in favor of these goals.
The cumulative score of the tumor cells identified in a sample determined a sample's final score.
In this study, we determined a sample size of 49 patients.
Yamane's formulae [ 25] was used to determine a sample size from an estimated female Punjab nurse workforce of 11,065.
Each DT model in DF determines a sample's classification through a series of rules based on selection of predictor variables.
Power analysis was performed for this experimental design in order to determine a sample size, which gives a power of 70% for the test of the treatment effect.
A program simulating the study was used to determine a sample size of 4800 patients has 80% power to declare non-inferiority.
Assuming a drop-out rate of 15% and about 10% non-evaluable patients we determined a sample size of 50 subjects to be included.
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CEO of Professional Science Editing for Scientists @ prosciediting.com