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Originally Posted by Garick
Thanks for posting this, especially the bolded. It helps make sense of the observed results that mpethy posted back in the day and I quoted ITT.
Regardless of "spot variance," LAGs have been observed to have smoother "big picture" graphs, and nits, especially short-stacking ones, to have much more jagged ones.
Like I said before, what mpethy posted had nothing to do with variance in the statistical sense. Cannabusto and mpethy are not talking about the same thing. The post you quoted is correct, but it doesn't support any of your claims in the thread.
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Originally Posted by cannabusto
I didn't say you weren't doing well. I don't know why you're being combative. I'd be delighted to be proven wrong. I'd rather learn than be right or have pride or whatever.
Sorry for being combative. Frankly you don't seem like you'd be delighted to be proven wrong. You've already eliminated the possibility that you might be wrong when you said you're "100% certain" that you're correct.
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I didn't say you didn't Google. I said you two need to Google more. Perhaps I was incorrect about that, but still I don't believe you understand what I'm talking about. I say this with all due respect.
I admit I do not like to be wrong, and for this reason I research the topics I am posting about when I'm unsure about something to the point where I feel prepared to make strong arguments, and it's a bit irritating that you and Garick seem to think I am just confused and a quick Google search will clear things up.
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Yes, I can answer the question. Calculating the standard error of a parameter estimate involves taking sigma and dividing by the sqrt of n, as you well know.
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Originally Posted by cannabusto
I accidentally wrote the standard error of the mean formula rather than what you asked, browni. For calculating the standard error of a statistic/parameter, you would subtract the mean from each observation, square the differences, and take the sqrt.
No disagreement here. Since we are in fact talking about the standard error of the mean (right?), the two formulae are equivalent, and we can use the first one.
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Still, this doesn't explain why we are differing in our views. The standard error of the mean involves a sample of sample means. Not a sample of observations.
You and Alan are looking at me sideways because you know damn well that a LAG has more variance in a given sample of observations. As do I. But I am not talking about sample variance.
I know that standard error of the mean involves a sample of the sample means. I know you are not talking about sample variance. However the SEM can be estimated knowing the sample variance using the formula you, Alan and I have all posted. I don't understand why you acknowledge the formula but deny the relationship between variance and SEM.
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"The*sampling distribution*of a population mean is generated by repeated sampling and recording of the means obtained. This forms a distribution of different means, and this distribution has its own*mean*and*variance."
I see this is quoted directly from the Wikipedia article on standard error. Read the next few sentences:
"Mathematically, the variance of the sampling distribution obtained is equal to the variance of the population divided by the sample size. This is because as the sample size increases, sample means cluster more closely around the population mean.
Therefore, the relationship between the standard error and the standard deviation is such that, for a given sample size, the standard error equals the standard deviation divided by the square root of the sample size. In other words, the standard error of the mean is a measure of the dispersion of sample means around the population mean. "
https://en.wikipedia.org/wiki/Standard_error