Unbiased Estimation Of Standard Deviation Puns

I'm doing monte carlo black box uncertainty propagation. I applied noise to inputs, and saw how it affected the output. I have a matrix of the propagated noise, data points vs trials. I took the standard deviation of the trials to check the uncertainty in every datapoint. To check the uncertainty of the overall system, do I take the mean of the standard deviation or the square root of the mean of the variance?

Each point is expected to have a different local uncertainty. I believe the mean of the variance makes more statistical sense than the mean of standard deviation, but it's more a hunch than something I'm sure

We assume uncertainty is the variance of noise, but display in standard deviation to have proper units

First of all, English is not my mother tongue, neither is statistics my speciality, so I'll try to translate the terms I know from French but may fail. Sorry for that.

I am currently trying to estimate the random error of an estimator, considering that there is no other bias in the sampling.

The problem is simple : I have, in a primary volume (pV), particles that move freely, do not interact with each other and bounce freely on the wall of the container. Something like a perfect gas. I know the primary volume, and the concentration of particle in this volume (pC).

Now, if I take a portion of this primary volume (sample volume sV), the "function of repartition" (I don't know if it is the correct term) of this estimator follow a normal distribution.

It seems evident to me that, in this theoretical situation without bias, the standard derivation (SD) of this estimator follow a statistical law with pV, pC, and sV as only parameters, and that, with this law, I can calculate the SD in different settings of pV, pC and sV.

Then the questions :

• Am I wrong (please elaborate if I am) ?
• If not, what is the law that described the behaviour of SD in this case ?

I swear if someone even thinks of attempting to find the standard deviation i'mma kame hame ha them into the stratosphere.

Up until this point I've had a pretty good grade in the class and was aiming for a B by the end of the semester. But the study guide the prof gave us was not like the final and they don't give partial credit on exams. I just finished the final and was wondering if anyone else knew if it was possible to pass/fail after taking the final itself. The title describes the situation. Advice?

For a project in a DSP class, I had to simulate 50 monte-carlo runs of spectral estimates of a sum of sinusoids and gaussian noise using different spectral estimators. I plotted in decibels both the means of the 50 spectral estimates and the standard deviations over the 50 runs (i.e. storing each of the 50 estimates as a row vector in a matrix and then obtaining a vector of the standard deviation of each column), and while the magnitudes are quite different, the envelope of the standard deviation plot very nearly matches the envelope of the spectrum in the sense that wherever the magnitude peaks, so does the standard deviation (except in the cases of a maximum likelihood spectrum, where the standard deviation reaches a minimum wherever the spectrum reaches a peak, and the maximum entropy spectrum, where the standard deviation appears to be fairly constant). What is this actually representing though, and why do the envelopes appear to match with each other?

Say I have two data sets in different units, on measures temperature and ranges from 15-30, and the other measures distance, ranging from 10-1300. I get back two standard deviations that are wildly different because the scale of the datasets are different, let’s say they’re 2.7 and 310.

What is the correct way to quantify them in a comparable way? I was thinking I could present them as a percentage of the range?

Thanks

Sorry for the probably very basic question but I'm currently trying to understand Z-tests and I'm lost.

Also if anyone of knows of the existence something like a complete or close to complete dictionary/vocabulary of all common variables in statistics and all the ways they can be spelled out in formulas it would be highly appreciated.

I'm sorry if this question has been asked here already. I haven't been able to find a definitive answer.

I've heard squaring the distances from the mean makes the resulting numbers easier to work with. If this is true, why does it make things easier (aside from producing positive values)? Also, why is the SD always bigger than the average distance from the mean? Is this a potential reason why the SD is a better measure of dispersion?

Thank you.

Was looking at my team and noticed that Mike Davis scored between 10.2 and 13.3 PPR points every week so far.

Got me thinking about who consistently scores within the smallest range? This isn’t a question of quality, I am curious about who the most predictable performer is.

My nomination is Mike Davis, as his standard deviation of PPR scores is 1.125, which is hard to beat imo.

Does anyone else know of any player who scores so predictably every week? Let me know if there’s someone with a smaller SD.

I'm doing some hypothesis contrast exercises where I have the value of the sample's standard deviation but not the one of the population, so I've read that in these cases I have to do the hypothesis contrast following a t Student distribution. To calculate the t value for the contrast I don't know if I have to use the standard deviation (S) or the standard quasi variation (Ŝ), which is S * √ ( n / n - 1 ).

So the way to calculate t is:

t = (M - μ ) / (S / √n) ?

where M (sample's mean), μ (population's mean), S (standard deviation), n (sample's size)

or

t = (M - μ ) / ( Ŝ / √n) ?

where M (sample's mean), μ (population's mean), Ŝ (standard quasi deviation), n (sample's size)

Any help is greatly appreciated because I have the global exam next week and I'm so lost.

I'm attempting to build a new dashboard that calculates z-score for changes in client volume, and to do so I need standard deviation. I found the formula:

Stddevp([argument])

When I place my columns in question in the parentheses, it gives me an invalid argument error. The Salesforce help pages only show the formula referencing a single column...which isn't useful for standard deviation.

What I have tried:

Stddevp(E,F,G) <- where columns E,F,G have the numbers in question, tried with and without quotes. stddevp("12 month", "11 month", "10 month") <- what I custom named the columns.

Any help would be much appreciated!

So the annual arithmetic return = 5% and the standard deviation is 10%

How would I go about finding the arithmetic return and standard deviation over a two-year period? Or over an n-year period?

I've thought about translating it back to annual standard deviation. So that the 10% / sqrt (2) = the two year standard deviation. Does that sound right to you guys? How would I compute the average 2-year return that incorporates the volatility.

I want to look at the standard deviation of a player’s wOBA for individual years and cumulative if possible for a project. I was wondering if anyone knew where I could find that data

just like how a statistics is the estimate of the parameter, is the standard error the estimate of the true sampling distribution standard deviation, or is the true 'parameter' called the standard error?