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In the exposed group, one third of the adverse outcomes can be attributed to the exposure (AFe = 1/3). In epidemiology , attributable fraction among the exposed (AF e ) is the proportion of incidents in the exposed group that are attributable to the risk factor.
Attributable fraction for the population combines both the relative risk of an incident with respect to the factor, as well as the prevalence of the factor in the population. Values of AF p close to 1 indicate that both the relative risk is high, and that the risk factor is prevalent. In such case, removal of the risk factor will greatly reduce ...
The moving ranges involved are serially correlated so runs or cycles can show up on the moving average chart that do not indicate real problems in the underlying process. [ 2 ] : 237 In some cases, it may be advisable to use the median of the moving range rather than its average, as when the calculated range data contains a few large values ...
Dunbar's number has become of interest in anthropology, evolutionary psychology, [12] statistics, and business management.For example, developers of social software are interested in it, as they need to know the size of social networks their software needs to take into account; and in the modern military, operational psychologists seek such data to support or refute policies related to ...
One definition of percentile, often given in texts, is that the P-th percentile (<) of a list of N ordered values (sorted from least to greatest) is the smallest value in the list such that no more than P percent of the data is strictly less than the value and at least P percent of the data is less than or equal to that value. This is obtained ...
A tolerance interval (TI) is a statistical interval within which, with some confidence level, a specified sampled proportion of a population falls. "More specifically, a 100×p%/100×(1−α) tolerance interval provides limits within which at least a certain proportion (p) of the population falls with a given level of confidence (1−α)."
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When interpreting, by one rule of thumb in confirmatory factor analysis, factor loadings should be .7 or higher to confirm that independent variables identified a priori are represented by a particular factor, on the rationale that the .7 level corresponds to about half of the variance in the indicator being explained by the factor.