Please check my work: Probability Theory

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The discussion focuses on finding the densities of random variables derived from a standard normal variable, K. The density for X, defined as |K|, is correctly identified as fX(x) = √(2/π) e^(-x²/2), while the density for Y, defined as K², is confirmed as a chi-squared distribution with 1 degree of freedom. Chi-squared distributions are primarily used in goodness-of-fit tests and for representing the sampling distribution of the ratio of sample variance to true variance. These applications are essential in classical frequentist statistics, relying on asymptotic results. The conversation highlights the importance of understanding these distributions in statistical analysis.
Bachelier
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Let K be a standard normal random variable. Find the densities of each of the following random variables:

X= |K|

Y = K2

I get:

fX(x) = √(2/π) e-x2/2

and

fY(y) = 1/√(2*π) 1/√y e-y2/2
 
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it's correct
 
For the Y random variable, it is definitely a chi-squared distribution with 1 degree of freedom.
 
chiro said:
For the Y random variable, it is definitely a chi-squared distribution with 1 degree of freedom.

We skipped the Chi-Squared dsn. I think I should read about it on Wikipedia. Where is it mostly used in?
 
Bachelier said:
We skipped the Chi-Squared dsn. I think I should read about it on Wikipedia. Where is it mostly used in?

Chi-squared distributions are used in a variety of cases.

One application is what is called goodness of fit. This is used to test how an observed set of frequencies are fitted to some expected set of frequencies.

Another application is for representing the sampling distribution of the ratio of the sample variance to the true variance. Given your degrees of freedom, you get a distribution that allows you to calculate a confidence interval for the ratio of sample variance to true variance, which effectively allows you to get an interval for your variance since you can calculate your sample variance from your data.

These uses are for classical frequentist statistics where these rely on asymptotic results.
 
Thanks Chiro
 
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