After a few weeks of collecting *data*, I played around with the factor until the *deviation* of different runs that “felt the same” was as low as possible.

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One could probably take the full *data* available now and calculate a factor which, based on the weekly average RFI, shows the smallest standard *deviation*.

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It quantifies the relationship between the the standard *deviation* (basically the distance your *data* is to itself...how spread out it is) to the mean (the average).

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In other words, while the standard *deviation* tells you how far you have to go for the *data* to be statistically different the relative standard *deviation* tells you what percent of the average that is.

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I compare this *data* to the covariance and standard *deviation* of each players actual points they have scored throughout the year (mostly use last years *data* for the first 4 or 5 weeks then I start to use current years stats).

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My rough ballpark (going off of *data* that has a large standard *deviation* and thus isn't that indicative of a average) is a decrease of 27 points, putting PFM at 285 points next year.

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The bulk of the posts (the automated ones) are essentially random *data* (doesnt mean it cant be decrypted), the way we can tell that is from the standard *deviation* which basically tells you how random the *data* is.

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A useful property of the standard *deviation* is that, unlike the variance, it is expressed in the same units as the *data*.

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A useful property of the standard *deviation* is that, unlike the variance, it is expressed in the same units as the *data*.

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The means all fall within one standard *deviation* of each other, so I think it would be hard to argue that they differ, but maybe the *data* are distributed bimodally such that the means do not capture the signal that you are looking for.

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In this particular case the means may fall within one standard *deviation*, but I have 40 *data* sets on hand.

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Think we'd have to calculate the Standard *deviation* for this and I'm no mathemagician to work it out with the *data* we have at the moment.

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*Deviation* *data* is harder to come by, but a cursory search indicates that 90% are below 200lbs.

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Results of four consecutive tests yielded an average *data* use of 39MB/hour for the target device, with a *deviation* of less than 2 MB.

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In terms of physics, there have been some experimental tests of gravity working through "large" extra dimensions, including very precise measurements of gravitational forces at small distances (down to ~100 microns) to see if there are *deviations* from Newtons laws, and looking at *data* from the Large Hadron Collider to see if there are *deviations* from the Standard Model consistent with large extra dimensions (e.g.

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We've only been working on Dutch spatial *data*, which is almost always projected in double stereographic with pretty much no *deviation* from the standard line at any place in the Netherlands (a few meters max around the edges, IIRC).

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The standard *deviation* has the same dimension as the *data*, and hence is comparable to *deviations* from the mean.

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Categorically from a *data* point of view though, wouldn't you want to look at the 50th percentile because by definition we'll encompass most of the population in the nearest standard *deviations*?

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if i remember right from what someone posted long long ago, about 500 *data* points would equal about 5% *deviation*.

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A chi-square would rely on the standard *deviation* being known, not estimated (or based on so much *data* you'd be happy to say that its estimate had converged to the population value).

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If you can assume your *data* come from a population that is symmetric, you can use similar theory to confidence intervals and state something is a potential outlier if it lies 3 standard *deviations* from the mean.

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I took the mean over the set of *data* from market sold prices and calculated for both standard *deviations* and averaged both.

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For example, I could get the standard *deviation*, max, min, and mean for a *data* set...

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Were you given those mean values and standard *deviations* or did you calculate them based on given *data*?

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Saying that on day 363, global ice extend in 2014 is higher than average says almost nothing at all - it is a remarkably average year based on this *data* almost always staying within one standard *deviation* of the mean in this *data*.

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If I am not mistaken, that is not the standard *deviation*, because in your picture, it would be only a point of the *data* set, while the standard *deviation* is a statistical characteristic of a set, which is defined even for a set of equal points.

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Because all *data* fits a perfect bell curve with extremes several standard *deviations* from the norm.

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If you want to look at historical *data* there's actually very little change over time, so the standard *deviation* will be quite low.

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Standard *deviation* over the entire population, is completely unrelated to the standard *deviation* over non-random subset of the *data*.

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In a normal distribution, 50% of *data* lie within 2/3 standard *deviations* of the mean, and the 2" difference from 5'10" to 6' happens to be 2/3 of the 3" standard *deviation*.

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One standard *deviation* above and below the mean encompasses about 68% of the *data*, confirming /u/FedoraToppedLurker's estimation that 16% of men are shorter than 5'7".

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The wages of CEOs are probably not figured in because they are so wildly disproportionate to the rest of the *data* points that they would cause huge *deviations* in the overall curve.

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Any *deviation* may result in *data* loss, file corruption, permissions issues, and other bad things.

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Any *deviation* from the expectation, no matter the magnitude, is statistically significant if the uncertainty is on the same scale as the *data*.

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The current *data* suggests he's within one standard *deviation* of the average.

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Given that they haven't eaten for so many hours, it would be quite telling if your *data* indicated a *deviation* (large gap in fares before sunup and after sundown) where the cabbie is eating.

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i keep a *data* warehouse of the info sorted by type (type of dev) and hours used versus hours estimated, and the *deviation*.

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Either their answers conform with your *data* and no correction is necessary, or their answers are a significant *deviation* and they are corrected.

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A *data* set's standard *deviation* is the average distance a *data* point is from the mean.

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If I used "moving standard *deviation*" I couldn't even plot the line together with the *data* points.

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my only gripe would be you may need to look at more definite *data* for standard *deviations* based on average times to find an appropriate scaling for each tier

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Between updates, the spacecraft can use *data* from star trackers, gyroscopes, and accelerometers to keep track of *deviations* in their path.

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