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The definition of an A p weight and the reverse Hölder inequality indicate that such a weight cannot degenerate or grow too quickly. This property can be phrased equivalently in terms of how much the logarithm of the weight oscillates: (a) If w ∈ A p, (p ≥ 1), then log(w) ∈ BMO (i.e. log(w) has bounded mean oscillation).
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)."
The use of anthropometric variables such as gender, age, height, weight, body fat percentage, and girth does not improve accuracy. [5] Also, most formulas are for experienced weightlifters, and novices may find their actual one rep maximum is much lower because their nervous system cannot handle the stress of a high weight.
It is calculated by averaging the square of the poverty gap ratio. By squaring each poverty gap data, the measure puts more weight the further a poor person's observed income falls below the poverty line. The squared poverty gap index is one form of a weighted sum of poverty gaps, with the weight proportionate to the poverty gap. [9]
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For an approximately normal data set, the values within one standard deviation of the mean account for about 68% of the set; while within two standard deviations account for about 95%; and within three standard deviations account for about 99.7%.
Example decision curve analysis graph with two predictors. A decision curve analysis graph is drawn by plotting threshold probability on the horizontal axis and net benefit on the vertical axis, illustrating the trade-offs between benefit (true positives) and harm (false positives) as the threshold probability (preference) is varied across a range of reasonable threshold probabilities.
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.