List of all mathematical symbols and signs-meaning and examples. 1  Basic math symbols. 2  Geometry symbols. 3  Algebra symbols. 4  Probability & statistics symbols. 5  Set theory symbols. 6  Logic symbols. 7  Calculus & analysis symbols. 8  Number symbols. 9  Greek symbols. 10  Roman numerals.
http://rapidtables.com/math/symbols/Basic_Math_Symbols.htm
First, calculate the deviations of each data point from the mean, and square the result of each: The variance is the mean of these values: and the population standard deviation is equal to the square root of the variance: This formula is valid only if the eight values with which we began form the complete population.
https://en.wikipedia.org/wiki/Standard_deviation
This means that the standard deviation is equal to the square root of the difference between the average of the squares of the values and the square of the average value. See computational formula for the variance for proof, and for an analogous result for the sample standard deviation.
https://en.wikipedia.org/wiki/Standard_deviation
A small variance indicates that the data points tend to be very close to the mean, and to each other. A high variance indicates that the data points are very spread out from the mean, and from one another. Variance is the average of the squared distances from each point to the mean. The process of finding the variance is very similar to finding the MAD, mean absolute deviation. The only difference is the squaring of the distances. Process: (1) Find the mean (average) of the set.
http://www.mathbitsnotebook.com/Algebra1/StatisticsData/STSD.html
In other words, the standard deviation σ (sigma) is the square root of the variance of X; i.e., it is the square root of the average value of (X − μ) 2. The standard deviation of a (univariate) probability distribution is the same as that of a random variable having that distribution.
https://en.wikipedia.org/wiki/Standard_deviation
Since standard deviation is the square root of the variance, we must first compute the variance. 1. Find the mean. 2. Subtract the mean from each data value and square each of these differences (the squared differences).
http://www.mathbitsnotebook.com/Algebra1/StatisticsData/STSD.html
The standard deviation of a random variable, statistical population, data set, or probability distribution is the square root of its variance. It is algebraically simpler, though in practice less robust, than the average absolute deviation.
https://en.wikipedia.org/wiki/Standard_deviation
The standard deviation can be thought of as a standard way of knowing what is normal (typical), what is very large, and what is very small in the data set. Standard deviation is a popular measure of variability because it returns to the original units of measure of the data set.
http://www.mathbitsnotebook.com/Algebra1/StatisticsData/STSD.html
(4) Divide by the number of pieces of data (for population variance). One problem with the variance is that it does not have the same unit of measure as the original data. For example, original data containing lengths measured in feet has a variance measured in square feet.
http://www.mathbitsnotebook.com/Algebra1/StatisticsData/STSD.html
Note also that if one of the variables has mean 0, then the covariance is simply the expected product. Trivially, covariance is a symmetric operation. \(\cov(X, Y) = \cov(Y, X)\). As the name suggests, covariance generalizes variance. \(\cov(X, X) = \var(X)\). Let \(\mu = \E(X) \). Then \(\cov(X, X) = \E\left[(X - \mu)^2\right] = \var(X) \). Covariance is a linear operation in the first argument, if the second argument is fixed.
http://www.math.uah.edu/stat/expect/Covariance.html
what is variance math
The variance math is the average of the squared distances from each point to the mean. In Maths, Variance is the average of the squared distances from each point to the mean.