Add all of the measurements together and then divide that sum by the number of measurements to get the mean.
Example: (2,2,3,5,7,9). 2+2+3+5+7+9= 28. Then 28 divided by 6 (the number of terms) = 4.666 or 4 2/3
95 percent of measurements are less than 2 standard deviations away from the mean, assuming a normal distribution.
Assuming var is variance, simply square the standard deviation and the result is the variance.
Outliers.
standard or imperial measurements are in : inches, feet, yards, pounds, gallons and asuming by nonstandard you mean metric measurements, they are in, millimeters, meters, kilometers, kilograms, liters. .........TADA!
The formula for calculating the standard error (or some call it the standard deviation) is almost the same as for the population; except the denominator in the equation is n-1, not N (n = number in your sample, N = number in population). See the formulas in the related link.
by ASR do you mean Area Specific Resistance?
95 percent of measurements are less than 2 standard deviations away from the mean, assuming a normal distribution.
variances
Assuming var is variance, simply square the standard deviation and the result is the variance.
Outliers.
Standard measurements are rather like the standard meanings of the words that you and I are using to communicate with each other. If a word has a standard meaning, then when I use that word you will know what I mean. If I use a standard measurement, then I can tell other people what I have measured and they will know what the measurement means.
unit is aquantity or an amount as a standard of measurements. ........bakar.........
Arithmatic Mean
standard or imperial measurements are in : inches, feet, yards, pounds, gallons and asuming by nonstandard you mean metric measurements, they are in, millimeters, meters, kilometers, kilograms, liters. .........TADA!
The formula for calculating the standard error (or some call it the standard deviation) is almost the same as for the population; except the denominator in the equation is n-1, not N (n = number in your sample, N = number in population). See the formulas in the related link.
I believe outliers is the best answer to this question. The previous answer was average, which is the mean.
VRMS = 1/N times square root of [ sum(Vn2) ]