Originally Posted by

**jesterea** Thanks again Malay. It appears this method does not scale beyond more than two factors. For example, if we change the function to look like the following, I am no longer able to isolate the effect of the different factors.

Factor1 x Factor2 x Factor3= Value

Year 1: Factor1 = 10, Factor2 = 40, Factor3 = 20, Value = 8000

Year 2: Factor1 = 12, Factor2 = 41, Factor3 = 17, Value = 8364

Year over Year change = 4.6%

Factor 1 isolation:

Holding Factor2 & 3 constant yields 12 * 40* 20 = 9600

- 9600/8000 - 1 = 20% change attributed to Factor1

Factor 2 isolation:

Holding Factor1 & 3 constant yields 10 * 41* 20 = 8200

- 8200/8000 - 1 = 2.5% change attributed to Factor2

Factor 3 isolation:

Holding Factor1 & 2 constant yields 10 * 40 * 17 = 6800

- 6800/8000 - 1 = -15% change attributed to Factor3

If I sum the impact of these changes up I get 7.5%. If I then try to add the "interplay" of the three factors (20% * 2.5% * -15%), it comes to 7.4%, which is significantly different than the actual total year over year change of 4.6%.

Is this a flawed approach when there are more than two variables (e.g., is it applying a linear approach to a non-linear problem, or something similar to that). If so, is there a better approach to isolate the impact of changes in factors, when three factors are involved?

Thank you again for the help,

Eric