Standard deviation is a measure of variance within a data set.
It can be thought of as the average distance from the mean (calculated average) for each individual data point in a data set.Standard deviation is a statistical operation that has wide applications, but for our purposes we’re discussing it as it relates to the Six Sigma program.
As a reference, below is the standard deviation formula, along with a key and the appropriate order of operations.
The Standard Deviation Formula
σ = Lower case sigma is the symbol for standard deviation
Σ = Upper case sigma is the summation symbol
X = Each individual value in the data set
x̅ = The arithmetic mean (known as “x-bar”)
n = The number of data points in the set (the number of X values)
First, we will need to find the arithmetic mean of the data set so that we have a value for x-bar within the equation. This is the basic average formula: find the sum of all the numbers within the data set, then divide by the number of numbers.
Here we are describing doing things the old-fashioned way, but any spreadsheet software can find the average of a data set with the click of a button. In many cases, standard deviation is calculated as well. Though this formula is important, it’s often unnecessary to keep it in your memory.
Now we need to perform the (x – x̅)2 function.
On a computer, this is easy. With drag and drop formulas and copy and paste functions there is no need to write everything out.
If, however, you want to complete this calculation by hand, the best way to do it is to build a table like the one below. This demonstration table is constructed using the sample data set 5, 12, 16, 21, 28.
The average for this data set is 16.4.
Plugged into our equation, it now looks like this:
Now that we have our results for x minus x-bar quantity squared, we see the uppercase sigma statistical operator in the equation. Simply add the values in the last column.
129.96 + 19.36 + .16 + 21.16 + 134.56 = 305.2
Plugged into our equation…
The square root of that number rounds to 8.73, meaning that our formula is solved as
σ = 8.73
To put that another way, the average distance that any one data point is away from the average of 16.4 is 8.73.
Identify and eliminate waste with the process optimization steps that are presented within these bestselling related business excellence titles.