Want this question answered?
standard normal
Standard Deviation
The mean deviation (also called the mean absolute deviation) is the mean of the absolute deviations of a set of data about the data's mean. The standard deviation sigma of a probability distribution is defined as the square root of the variance sigma^2,
It is not called anything special, just 2 standard deviations or 3 sd.
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
The square of the standard deviation is called the variance. That is because the standard deviation is defined as the square root of the variance.
The standard error is the standard deviation divided by the square root of the sample size.
standard error
Standard deviation
standard normal
It is called a standard normal distribution.
The variance is standard deviation squared, or, in other terms, the standard deviation is the square root of the variance. In many cases, this means that the variance is bigger than the standard deviation - but not always, it depends on the specific values.
Deviation, actually called "standard deviation" is, in a set of numbers, the average distance a number in that set is away from the mean, or average, number.
The mean deviation (also called the mean absolute deviation) is the mean of the absolute deviations of a set of data about the data's mean. The standard deviation sigma of a probability distribution is defined as the square root of the variance sigma^2,
Standard Deviation
It is not called anything special, just 2 standard deviations or 3 sd.
The set of X1, X2, ..., XN is called X. Given that mean(X), is the sum of all X divided by N, the variance of X is mean((Xi - mean(X))2). The standard deviation of X is the square root of the variance.