For several reasons, the normal distribution is the "go to" distribution, including the ability to approximate the binomial distribution, as well as the hypergeometric and Poisson distributions.
Probability is estimated for a continuous probability distribution by taking the area under the graph of the probability density function, abbreviated f(x). The probability density function for the uniform probability distribution is f(x)= { 1 b − a for a ≤ x ≤ b 0 elsewhere.
A continuous distribution is one in which data can take on any value within a given range of values (which may be infinite).
A symmetric, unimodal, and bell-shaped continuous probability distribution is produced by many real-world challenges. Height, blood pressure, and cholesterol levels, for example.
The Poisson distribution is a discrete distribution that calculates the likelihood of a certain number of events occurring in a particular time period.
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Umashankar
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1.easy to understand all topic
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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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