An annualized Standard Deviation (ASD) is a statistic that was developed in 1968 by the U.S. Bureau of Labor Statistics. The statistic measures the average deviation of a person’s annual earnings, which is commonly referred to as the annualized standard deviation, from the actual standard deviation. In other words, the higher the number, the more that person deviates from the average. The annualized standard deviation is used by U.S.

U.S. based on the number of people who live in the United States. We used a number of statistics to measure the “average” standard deviation of American people, and we’re going to look at some of that in the next section.

The U.S.U.S. annualized standard deviation is based on the number of people who live in a country, and is the average of the number of people who live in each country at the same time. This makes it easier to compare the United States to other countries, but it also makes it easier to compare the United States to itself, because the average American is the same everywhere.

The US annualized standard deviation is based on the number of people in the United States who live in a given time period during the year. The time period used to calculate the US annualized standard deviation is the time period from January 1, 2011 to December 31, 2016.

US annualized standard deviation for the year 2016 was 19.97, which is higher than the average US annualized standard deviation of 19.78. This means that there are more people in the US in 2016, even though the median US annualized standard deviation was 19.78.

This is because the US does not have a standard number of people who live during the year, so it can’t be compared to a standard number. The annualized standard deviation is computed over the entire year, which makes it a bit arbitrary. We can, though, use it to compare the spread in the data between time periods. For instance, for time period A, the US annualized standard deviation for time period B is 19.

In the first paragraph, I tried to make this sound as clear as possible. In reality, the term is just a way to measure the spread of a time series. For instance, if I have a time series with a yearly variance, I can compute the variance of the entire time series over a particular time period and get the standard deviation of the entire series over that period. That is the annualized standard deviation.

For a time series that has a standard deviation, this may be overkill. But for a time series with a regular, periodic trend, it gives you a good idea of the amount of variability in the data. For those that are interested, you can also compute the standard deviation of the entire series with a moving average, which is similar to the annualized standard deviation but only uses the last 10 years to compute the standard deviation.

Here’s a table that shows the trend, the standard deviation, and the total change of the series over that period.

The annualized standard deviation also allows you to estimate the annual rate of change of the series.