Probability plot for Cauchy Distribution

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SUMMARY

The discussion focuses on constructing a probability plot to demonstrate that the ratio of two independent standard normal variables, Z1 and Z2, follows a Cauchy distribution. The user outlines a procedure involving sorting, ranking, and applying the median rank method, followed by plotting Z1/Z2 against the inverse cumulative probability. The results show linearity near the center but significant deviations at the extremes, indicating potential issues with data size or independence of the variables. The importance of generating a large dataset and ensuring the independence of Z1 and Z2 is emphasized.

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  • Understanding of Cauchy distribution properties
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  • Knowledge of statistical ranking methods, specifically median rank
  • Experience with generating and manipulating random variables in statistical software
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Statisticians, data analysts, and researchers interested in probability theory and distribution analysis, particularly those working with the Cauchy distribution and random variable manipulation.

herjia
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I have generated 2 columns of normal random variables, Z1 and Z2. Theorectically, Z1/Z2 will follow a Cauchy distribution. The question is, how do I construct a probability plot to show that indeed it is a Cauchy distribution?

I tried the follow procedure:
-Sort the Z1/Z2
-Rank them and store the rank on a new column, i
-perform median rank (herd-Johnson) i/n+1 where n is the sample size
-perform inverse cumulative probability on the median rank column
-plot the z1/z2 vs inverse cumulative probability

What i get is near the location, the data are linear while the deviation is serious at either extreme ends. any suggestion? or references?
 
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It's very very very important that the two standard normal variables you generated are independent. I don't know how you generated them so I don't know if they are.

It also goes without saying that you're going to need a sizeable amount of data to get anything meaningful.

Personally, I'd just prove that Z1/Z2 is a Cauchy distribution. It's fun!
 

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