Uniform distribution In statistics uniform distribution is a term used to describe a form of probability distribution where every possible outcome has an equal likelihood of happening The probability is constant since each variable has equal chances of being the outcome ID: 1002547
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1. UNIFFORM DISTRIBUTION AND EXAMPLE
2. Uniform distributionIn statistics, uniform distribution is a term used to describe a form of probability distribution where every possible outcome has an equal likelihood of happening. The probability is constant since each variable has equal chances of being the outcome.
3. Types Of uniform distributionUniform distribution can be grouped into two categories based on the types of possible outcomes.DISCRETE UNIFORM DISTRIBUTION CONTINIOUS UNIFORM DISTRIBUTION
4. DISCRETE UNIFORM DISTRIBUTION In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein a finite number of values are equally likely to be observed; every one of n values has equal probability 1/n. Another way of saying “discrete uniform distribution” would be “a known, finite number of outcomes equally likely to happen”. The discrete uniform distribution itself is inherently non-parametric. It is convenient, however, to represent its values generally by all integers in an interval [a,b], so that a and b become the main parameters of the distribution.
5. CONTINUOUS UNIFORM DISTRIBUTIONThe Uniform distribution is the simplest probability distribution, but it plays an important role in statistics since it is very useful in modeling random variables. The uniform distribution is a continuous probability distribution and is concerned with events that are equally likely to occur. The continuous random variable X is said to be uniformly distributed, or having rectangular distribution on the interval [a,b].
6. Examples of Uniform DistributionUniform distribution is the simplest statistical distribution. The concept of uniform distribution, as well as the random variables it describes, form the foundation of statistical analysis and probability theory.For example, if you stand on a street corner and start to randomly hand a $100 bill to any lucky person who walked by, then every passerby would have an equal chance of being handed the money. The percentage of the probability is 1 divided by the total number of outcomes (number of passersby). However, if you favored short people or women, they would have a higher chance of being given the $100 bill than the other passersby. That would not be described as uniform probability.A deck of cards also has a uniform distribution. This is because an individual has an equal chance of drawing a spade, a heart, a club, or a diamond. Another example with a uniform distribution is when a coin is tossed. The likelihood of getting a tail or head is the same. The graph of a uniform distribution is usually flat, whereby the sides and top are parallel to the x- and y-axes.
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