San José State University
Department of Economics |
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applet-magic.com Thayer Watkins Silicon Valley & Tornado Alley USA |
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(when the mean value of the random variable is known) as a Function of Sample Size |
Suppose the probability density distribution for x is
The square of x can then only have values between 0 and 0.25. Thus the probability density function for w=x^{2} is given by
Below are shown the histograms for 2000 repetitions of taking samples of n random variables and computing the mean value of the squares of a random variable which is uniformly distributed between -0.5 and +0.5. The sum is normalized by dividing by the square root of the sample size n. This keeps the dispersion of the distribution constant. Otherwise with larger n the distribution would be more spread out. Althought the random variable is distributed between -0.5 and +0.5 its square is distributed between 0 and 0.25.
Each time the display is refreshed a new set of 2000 repetions of the samples is created.
As can be seen, as the sample size n gets larger the distribution more closely approximates the shape of the normal distribution.
Although the distribution for n=1 is decidedly non-normal, for n=16 the distribution looks quite close to a normal distribution even though the sample value can take on only positive values.
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