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Copyright: 1996  2024 Webmaster:
Kirt Blattenberger,
BSEE  KB3UON
RF Cafe began life in 1996 as "RF Tools" in an AOL screen name web space totaling
2 MB. Its primary purpose was to provide me with ready access to commonly needed
formulas and reference material while performing my work as an RF system and circuit
design engineer. The World Wide Web (Internet) was largely an unknown entity at
the time and bandwidth was a scarce commodity. Dialup modems blazed along at 14.4 kbps
while typing up your telephone line, and a nice lady's voice announced "You've Got
Mail" when a new message arrived...
All trademarks, copyrights, patents, and other rights of ownership to images
and text used on the RF Cafe website are hereby acknowledged.
My Hobby Website:
AirplanesAndRockets.com


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Variance and Standard & Mean Deviation 
In probability theory and
statistics, the variance of a random variable, probability distribution, or sample is a measure of statistical
dispersion, averaging the squared distance of its possible values from the expected value (mean). Whereas the mean
is a way to describe the location of a distribution, the variance is a way to capture its scale or degree of being
spread out. The unit of variance is the square of the unit of the original variable. The positive square root of
the variance, called the standard deviation, has the same units as the original variable and can be easier to
interpret for this reason.
The variance of a realvalued random variable is its second central moment, and
it also happens to be its second cumulant.
In statistics, standard deviation is a simple measure of the
variability or dispersion of a population, a data set, or a probability distribution. A low standard deviation
indicates that the data points tend to be very close to the same value (the mean), while high standard deviation
indicates that the data are “spread out” over a large range of values. 
Wikipedia
Mean Deviation 

Standard Deviation 



where x is the mean of the sample, n is an
integer
Variance is the square of the Standard Deviation = s^{2} 

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