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The Q Score is a metric that determines a "quotient" ("Q") factor through mail and online panelists who make up representative samples of the population. The score identifies the familiarity of an athlete, brand, celebrity, poet, entertainment offering (e.g., television show), or licensed property, and measures the appeal of each among people ...
The q-value can be interpreted as the false discovery rate (FDR): the proportion of false positives among all positive results. Given a set of test statistics and their associated q-values, rejecting the null hypothesis for all tests whose q-value is less than or equal to some threshold ensures that the expected value of the false discovery rate is .
The Q-statistic or q-statistic is a test statistic: The Box-Pierce test outputs a Q-statistic (uppercase) which follows the chi-squared distribution
Q factor (bicycles), the width between where a bicycle's pedals attach to the cranks; q-value (statistics), the minimum false discovery rate at which the test may be called significant; Q value (nuclear science), a difference of energies of parent and daughter nuclides; Q Score, in marketing, a way to measure the familiarity of an item
The g factor [a] is a construct developed in psychometric investigations of cognitive abilities and human intelligence.It is a variable that summarizes positive correlations among different cognitive tasks, reflecting the assertion that an individual's performance on one type of cognitive task tends to be comparable to that person's performance on other kinds of cognitive tasks.
The q-value is the analog of the p-value with respect to the positive false discovery rate. [50] It is used in multiple hypothesis testing to maintain statistical power while minimizing the false positive rate. [51] The Probability of Direction is the Bayesian numerical equivalent of the p-value. [52]
The Q-function can be generalized to higher dimensions: [14] = (),where (,) follows the multivariate normal distribution with covariance and the threshold is of the form = for some positive vector > and positive constant >.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).