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A product distribution is a probability distribution constructed as the distribution of the product of random variables having two other known distributions. Given two statistically independent random variables X and Y, the distribution of the random variable Z that is formed as the product = is a product distribution.
Distribution of products takes place through a marketing channel, also known as a distribution channel. A marketing channel is the people, organizations, and activities necessary to transfer the ownership of goods from the point of production to the point of consumption. It is the way products get to the end-user, the consumer.
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].
Product planning (or product discovery) is the ongoing process of identifying and articulating market requirements that define a product's feature set. [1] It serves as the basis for decision-making about price, distribution and promotion.
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).
It is the way products get to the end-user, the consumer; and is also known as a distribution channel. [1] A marketing channel is a useful tool for management, [2] and is crucial to creating an effective and well-planned marketing strategy. [3] Another less known form of the marketing channel is the Dual Distribution [4] channel.
In probability theory, the chain rule [1] (also called the general product rule [2] [3]) describes how to calculate the probability of the intersection of, not necessarily independent, events or the joint distribution of random variables respectively, using conditional probabilities.
A discrete probability distribution is applicable to the scenarios where the set of possible outcomes is discrete (e.g. a coin toss, a roll of a die) and the probabilities are encoded by a discrete list of the probabilities of the outcomes; in this case the discrete probability distribution is known as probability mass function.