The second method won't work if the sales do not follow a Poisson distribution, for instance a product will have better sales at launch and during advertising campaigns. The sales figures don't always float around a constant average.
Also, I hadn't checked the math on the second method but the units don't add up.
On the LHS, you have [sales / pixel]x[pixels] = [sales]
On the RHS, you have ([sales / pixel]x[pixels])² = [sales]²
IIRC, the left hand side should be squared.
S = P / Var(P)
Numbers of pixels: 13,6,16,29,6,16,13,6,19,13,19,9,26; followed by the large one.
Mean ~ 14.692;
Using Sample Variance ~ 53.397;
Using Population Variance ~ 49.289;
SalesPerPixel(Sample) ~ .275
This doesn't seem right, where does the math screw up?
That said, this isn't very useful. The only time someone will show sales graphs is for special events... aka when it DOESN'T follow a possion distribution.
This made me laugh.
These methods will work on a sales graph built from numbers like [27, 41, 55, 73]. Part of these methods (not all) may work on a sales graph built from numbers like [27 000, 41 000, 55 000, 73 000] ... and give the exact same result as for [27, 41, 55, 73].
And none of them will give any reasonable results for numbers like [27 123, 41 432, 55 132, 73 433] - there's not enough information for that.