Search results
Results from the WOW.Com Content Network
Numeric distribution measures a firm's ability to convey a product to its customers in terms of total number of outlets carrying the brand. The main use of metric distribution is to understand how many physical locations stock a product or brand. This has implications for delivery systems and for the cost of servicing these outlets. [1]
The uniform distribution or rectangular distribution on [a,b], where all points in a finite interval are equally likely, is a special case of the four-parameter Beta distribution. The Irwin–Hall distribution is the distribution of the sum of n independent random variables, each of which having the uniform distribution on [0,1].
Weighted means are commonly used in statistics to compensate for the presence of bias.For a quantity measured multiple independent times with variance, the best estimate of the signal is obtained by averaging all the measurements with weight = /, and the resulting variance is smaller than each of the independent measurements = /.
All-commodity volume (ACV) is a weighted measure of product availability, or distribution, based on total store sales. In other words, ACV is the percentage of sales in all categories that are generated by the stores that stock a given brand (again, at least one SKU of that brand) (note: ACV can be expressed as a percentage or as a dollar value (total sales of stores carrying brand).
The action (the integration ) of this distribution on a test function can be interpreted as a weighted average of the distribution on the support of the test function, even if the values of the distribution at a single point are not well-defined.
It examines the share of the relevant product category sold by stores in which a given product has gained distribution. [1] Distribution metrics quantify the availability of products sold through retailers, usually as a percentage of all potential outlets. Often, outlets are weighted by their share of category sales or “all commodity” sales.
A discrete probability distribution is the probability distribution of a random variable that can take on only a countable number of values [15] (almost surely) [16] which means that the probability of any event can be expressed as a (finite or countably infinite) sum: = (=), where is a countable set with () =.
The equidensity contours of a non-singular multivariate normal distribution are ellipsoids (i.e. affine transformations of hyperspheres) centered at the mean. [29] Hence the multivariate normal distribution is an example of the class of elliptical distributions.