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The importance is that the sum of a large number of independent random variables is always approximately normally distributed as long as each random variable has the same distribution and that distribution has a finite mean and variance. The point is that it DOES NOT matter what the particular distribution is. So whatever distribution you start with, you always end up with normal.
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What is the definition of central limit theorem?
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
Why is the central limit theorem an important idea for dealing with a population not normally distributed?
According to the Central Limit Theorem, even if a variable has an underlying distribution which is not Normal, the means of random samples from the population will be normally distributed with the population mean as its mean.
How do you know x bar and R charts follow normal distribution?
Central Limit Theorem
What is the difference between Gamma distribution and Central limit theorem?
There is abig difference between them..gamma is a distribution but central limit theorm is just like a method or technique u use to approximate gamma to another distriution which is normal....stupid
What is the central limit theorum?
The central limit theorem states that the mean of a sufficiently large number of iterates of independent random variables, each with well-defined mean and well-defined variance, will be approximately distributed. This is the definition in the probability theory.
