What is this method for seasonal adjustment calculation? I got an example calculation for multiplicative model, which is shown as follows:
Quarter           1         2        3       4
Average         0.866    1.0005   1.403    0.660
--------------------------------------------------
Adjustment      0.0176   0.0176   0.0176   0.0176
Seasonal factor  0.884    1.018   1.421    0.678

Then there is a note below:

Sum of averages = 3.9295. These should sum to 4, 4-3.9295=0.0705.
  Adding 0.0705/4=0.0176 to each average, to obtain the seasonal
  factors.

I saw from other resources that they are using "seasonal index" instead of "seasonal factor" by normalizing the values. Besides that, they also mentioned about X11, X12, ARIMA, and so on. I would like to know is, based on the example above, what is the method called?
 A: The following site give you a good overview of seasonal adjustment. Importantly, it also contains a good description of how to do seasonal adjustment yourself (in R). Of course, one can use the census program directly. However, especially for beginners, the program might be too complex to use and the output that the program provides simply comprises too much information for the average user.
Link to the overview of seasonal adjustment:
https://economictheoryblog.com/2017/05/02/seasonal-adjustment
Seasonal Adjustment in R:
https://economictheoryblog.com/2017/05/16/seasonal-adjustment-in-r
You can try to replicate the above seasonal adjustment output by adjusting the settings of the seasonal adjustment program until your reach the given output. 
A: X11 is an older method of seasonally adjusting data.  Here's a link:
http://www.census.gov/srd/www/sapaper/historicpapers.html
X12 is a newer version:
http://www.census.gov/srd/www/x12a/
You can download X12 and run it yourself.   Part of the output will be similar to your data above.   For example, in Quarter 1, it gave a "Seasonal Factor" of 0.884.    That came from the "Average" of 0.866, with an "Adjustment" of 0.0176.   In other words,
0.884 = 0.866 + 0.0176

Another way to look at this is, zero seasonality would give you a "Seasonal Factor" of 1.0 for each quarter (it would be "flat-line" for zero seasonality).   If you add these four 1.0's together, you get a total of 4.0 for the year.
However, your data doesn't have zero seasonality.  It is typically (or seasonally) down 11.6% in Q1 (0.884), up 1.8% in Q2 (1.018), up 42.1% in Q3 (1.421) and down 32.2% in Q4 (0.678) when compared to "flat-line".  If you add those numbers up, again you'll get 4.0 for the year.    In other words, the "Adjustment" was the evenly distributed adder that was required to get the "Average" to provide a "Seasonal Factor" that totals 4.0 for the year.
