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The standard uniform distribution is a special case of the beta distribution, with parameters (1,1). The sum of two independent uniform distributions U 1 (a,b)+U 2 (c,d) yields a trapezoidal distribution, symmetric about its mean, on the support [a+c,b+d].
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].
Continuous uniform distribution. One of the simplest examples of a discrete univariate distribution is the discrete uniform distribution, where all elements of a finite set are equally likely. It is the probability model for the outcomes of tossing a fair coin, rolling a fair die, etc.
Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. [1]
In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein each of some finite whole number n of outcome values are equally likely to be observed. Thus every one of the n outcome values has equal probability 1/n. Intuitively, a discrete uniform distribution is "a known, finite number ...
One use for the probability integral transform in statistical data analysis is to provide the basis for testing whether a set of observations can reasonably be modelled as arising from a specified distribution. Specifically, the probability integral transform is applied to construct an equivalent set of values, and a test is then made of ...
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For example, some authors [6] define φ X (t) = E[e −2πitX], which is essentially a change of parameter. Other notation may be encountered in the literature: p ^ {\displaystyle \scriptstyle {\hat {p}}} as the characteristic function for a probability measure p , or f ^ {\displaystyle \scriptstyle {\hat {f}}} as the characteristic function ...