The central limit theorem (CLT) says that as sample sizes grow higher, the distribution of sample means approaches a normal distribution, independent of the population's distribution. For the CLT to hold, sample sizes of 30 or more are frequently regarded sufficient.
The central limit theorem gives a formula for the sample mean and the sample standard deviation when the population mean and standard deviation are known. This is given as follows: Sample mean = Population mean = μ μ Sample standard deviation = (Population standard deviation) / √n = σ / √n.
To summarise, the central limit theorem has three different components:
A population is sampled repeatedly.
Increasing the size of the sample
The distribution of the population.
The Central Limit Theorem underpins what a data scientist does on a daily basis: making statistical judgments about data. Without having to take a fresh sample to compare it against, the theorem allows us to calculate the likelihood that our sample will diverge from the population.
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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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