Please check my work: Probability Theory

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Discussion Overview

The discussion revolves around the properties and applications of random variables derived from a standard normal random variable, specifically focusing on the absolute value and the square of the variable. Participants explore the probability densities associated with these transformations and touch upon the chi-squared distribution.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant presents the probability density functions for the random variables X = |K| and Y = K², suggesting specific forms for fX(x) and fY(y).
  • Another participant confirms the correctness of the presented densities.
  • Some participants assert that the random variable Y follows a chi-squared distribution with 1 degree of freedom, though this is stated without further elaboration on the implications.
  • There are inquiries about the applications of the chi-squared distribution, with mentions of goodness of fit tests and the sampling distribution of variance ratios.

Areas of Agreement / Disagreement

While there is agreement on the correctness of the density functions presented, the discussion includes multiple perspectives on the applications and implications of the chi-squared distribution, indicating that some aspects remain unresolved.

Contextual Notes

Participants reference the chi-squared distribution without detailing its derivation or the assumptions underlying its application, leaving some mathematical steps and definitions unaddressed.

Who May Find This Useful

This discussion may be useful for individuals interested in probability theory, statistical distributions, and their applications 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
 
Last edited:
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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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