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In statistics, the 68–95–99.7 rule, also known as the empirical rule, and sometimes abbreviated 3sr, is a shorthand used to remember the percentage of values that lie within an interval estimate in a normal distribution: approximately 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean, respectively.
Chartreux cats are also known for their "smile"; due to the structure of their heads and their tapered muzzles, they often appear to be smiling. Chartreux are exceptional hunters and are highly prized by farmers. As for every French cat with a pedigree, the first letter of the official name of a Chartreux cat encodes the year of its birth. [1]
Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero.
In contrast to the mean absolute percentage error, SMAPE has both a lower and an upper bound. Indeed, the formula above provides a result between 0% and 200%. Indeed, the formula above provides a result between 0% and 200%.
It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3] Effectively, this overcomes the 'infinite error' issue. [4]
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These two cat fancy organizations are not the same. The Chartreux is not even a recognized breed of the GCCF; it is not included in the GCCF's list of recognized breeds, and a search of the entire GCCF site did not even find a match for the word "Chartreux". By contrast, the CFA's Chartreux breed page is linked from the Chartreux article.
CEP is not a good measure of accuracy when this distribution behavior is not met. Munitions may also have larger standard deviation of range errors than the standard deviation of azimuth (deflection) errors, resulting in an elliptical confidence region. Munition samples may not be exactly on target, that is, the mean vector will not be (0,0).