Probability plot for Cauchy Distribution

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To construct a probability plot for the Cauchy distribution using the ratio of two independent normal random variables Z1 and Z2, the process involves sorting the ratio, ranking the values, and calculating the median rank for plotting. However, the resulting plot shows linearity near the center but significant deviations at the extremes, indicating potential issues with the sample size or data independence. It is crucial to ensure that Z1 and Z2 are generated independently to validate the Cauchy distribution. A larger dataset is recommended for more accurate results. Proving that Z1/Z2 follows a Cauchy distribution can also be a valuable approach.
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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!
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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