Difference between revisions of "Talk:Measuring Precision"

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:::: ''"Rayleigh Parameter" would be better, but that's why I often just use "σ":  Its meaning is consistent and its usage pervasive throughout the site.  When I began I was calling the Rayleigh parameter the RSD until I realized that it wasn't! [[User:David|David]] ([[User talk:David|talk]]) 12:18, 4 June 2015 (EDT)''
 
:::: ''"Rayleigh Parameter" would be better, but that's why I often just use "σ":  Its meaning is consistent and its usage pervasive throughout the site.  When I began I was calling the Rayleigh parameter the RSD until I realized that it wasn't! [[User:David|David]] ([[User talk:David|talk]]) 12:18, 4 June 2015 (EDT)''
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::::: Just to be clear, at the top of this thread I was thinking of redefining RSD as <math>r = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (r_i - \bar{r})^2}</math>,<br />
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:::::where <math>\bar{r}</math> is the mean radius. Thus Mean Radius and Radial Standard deviation would relate back to the Rayleigh Distribution. The point is that if <math>\sigma_h \neq \sigma_v</math> then you shouldn't be assuming that the Rayleigh Distribution models the shots. "Radius" and "Radial" inherently make you think of a circular pattern. Maybe "Mean Radius Variance"? That would really do what I want and make the two tie together. To let you in on a secret, I think the mean radius would be a better predictor of variance than the actual variance measurement (with a big assumption that the pattern is truly round. If pattern isn't round then the MR estimator of RSD wouldn't be robust.) <br />[[User:Herb|Herb]] ([[User talk:Herb|talk]]) 13:27, 4 June 2015 (EDT)
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Revision as of 13:27, 4 June 2015

Ok, I'm happy with title on this and general layout. Still some clean up to do in the various measures. My notion is just to present the conceptual idea for the measures on this page. Each dispersion measure title would be linked to a wiki page describing that measure in more detail.
Herb (talk) 22:11, 3 June 2015 (EDT)


3.4 Elliptical Error Probable (EEP)

Need to do so more literature searching on this. I suspect that this includes Hoyt case where rho <> 0. I'm thinking of constraining it to ellipses oriented along horizontal and vertical axis. You could then convert to circle with a simple rescaling.
Herb (talk) 22:11, 3 June 2015 (EDT)


3.8 Hoyt Distribution Parameters (Bivariate Normal Distribution Parameters)

Think changing name for this to "Hoyt Error Probable" to fit in with other such measures.
Herb (talk) 22:11, 3 June 2015 (EDT)


I think the two sections

3.10 Radial Standard Deviation of the Rayleigh Distribution
3.11 Rayleigh Distribution Shape Parameter

Should probably be merged. My notion is to swizzle Radial Standard Deviation so that it fits Rayleigh Distribution better. ie sqrt(2) factor.
Herb (talk) 22:11, 3 June 2015 (EDT)

But why are you interested in keeping RSD alive despite all the confusion surrounding it? David (talk) 22:49, 3 June 2015 (EDT)
Rayleigh Distribution Shape Parameter just doesn't sound like a measure. I am just seduced by the sexy name. Inclusion of "standard deviation" is nice. "Radial" meaning a circle is good too. Mulling this over for the 43rd time in my mind, how about "Rayleigh Radial Scale Factor"?? "Radial" is really the keyword. Fits in with wikipedia use too. What do you think?
You're right, bad terminology is hard to overcome. Electric circuits still are calculated as if a positive charge is moving instead of the electron. So a somewhat fresh start might be good.
I don't like calling the measure "Rayleigh Distribution" either since we aren't calculating the distribution per sey, but fitting a parameter which it uses.
Herb (talk) 23:50, 3 June 2015 (EDT)
"Rayleigh Parameter" would be better, but that's why I often just use "σ": Its meaning is consistent and its usage pervasive throughout the site. When I began I was calling the Rayleigh parameter the RSD until I realized that it wasn't! David (talk) 12:18, 4 June 2015 (EDT)
Just to be clear, at the top of this thread I was thinking of redefining RSD as \(r = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (r_i - \bar{r})^2}\),
where \(\bar{r}\) is the mean radius. Thus Mean Radius and Radial Standard deviation would relate back to the Rayleigh Distribution. The point is that if \(\sigma_h \neq \sigma_v\) then you shouldn't be assuming that the Rayleigh Distribution models the shots. "Radius" and "Radial" inherently make you think of a circular pattern. Maybe "Mean Radius Variance"? That would really do what I want and make the two tie together. To let you in on a secret, I think the mean radius would be a better predictor of variance than the actual variance measurement (with a big assumption that the pattern is truly round. If pattern isn't round then the MR estimator of RSD wouldn't be robust.)
Herb (talk) 13:27, 4 June 2015 (EDT)



3.12 String Method

Need some literature reference for this. No point in just pulling crap measures out of thin air. Found a couple of forum references. I remember it being something like mean radius and looked it up. Sexy in that it is a nice example of Rice Distribution.
Herb (talk) 23:50, 3 June 2015 (EDT)

This is just Mean Radius * n, so I would note it under the Mean Radius section as evidence that MR is nothing new. David (talk) 12:18, 4 June 2015 (EDT)
No the "original" string measurement was from POA to shots which gives Rice Distribution. It was a measure much more weighed towards accuracy than precision. The two variations are mean radius variations. Sorry to have confused you.
Herb (talk) 12:38, 4 June 2015 (EDT)
Oh yeah, that is good. Worth covering! David (talk) 12:43, 4 June 2015 (EDT)