In statistics, a uniform distribution is a distribution function in which every potential outcome is equally likely to occur; that is, the probability of each occurrence is the same.
A consistent distribution can also be found in a deck of cards. This is because a person's chances of drawing a spade, a heart, a club, or a diamond are all identical. A coin toss is another example of a uniform distribution. The chances of acquiring a tail or a head are equal.
The frequency test is a homogeneity test. The Kolmogorov-Smirnov test and the chi-square test are two approaches that can be used. The agreement between the distribution of a sample of generated random numbers and the theoretical uniform distribution is measured by both tests.
A uniform distribution, often known as a rectangle distribution, is one in which the probability is constant.
A uniform distribution is used in any case where every event in a sample space is equally likely. Rolling a single standard die is an illustration of this in a discrete case. The dice has a total of six sides, each of which has the equal chance of being rolled face up.
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Akash Sambhaji
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plz provides all notes
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Jamil Akhtar
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plzz provide notes....
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Sunita Singhal
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please provide notes also in pdf
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Montu Mali
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nice ☺️👍
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Abdul Samed
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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Nikhil Fapale
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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