TOP MENUStatistics such as root mean square |
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Describes misleading statistics such as root mean square, mean square error, and Square root of sum squares. ■What is a statistic?
A statistic is a numerical value that represents the characteristics of sample data,
calculated statistically according to the purpose for sample data extracted from a certain population. ■RMS:Root Mean Square
It takes the mean of the squared deviations from a reference point, and then the square root of that.
You may wonder what is the point of squaring, since the square root is taken in the end, but this means that by squaring, weight is given to the values that are far from the standard.
This formula is also used to determine the effective value of AC current.
■RMSE:Root Mean Square Error
Even in RMS, when the reference value is some kind of estimated value, it is called the root mean square error in the sense that it is an error from the estimated value. ■MSE:Mean Square Error
When the square root is not taken for the standard deviation, it is called the mean squared error, which is the variance. ■RSS:Residual Sum of Squares
It is used for the LSM(Least Squares Method), etc., and is the sum of the residuals (difference between the actual value and the estimated value) squared. Also known as the sum of squared errors.
RSS is a form of equation that is not divided by the number of sample data for RMS. ■SRSS:Square root of sum squares
A statistic used to calculate component tolerances, for example,
if the tolerances of i components are x1,x2～xi, respectively, the square root of the sum of squares is as follows.
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