Standard Deviation Differences Between Excel and R (and my code in Cube Voyager)

I had a need to get the correlation of count to assignment in Cube Voyager.  I don't know how to do this off the top of my head, and I'm instantly mistrusting of doing things I'd normally trust to R or Excel.  So I looked on Wikipedia for the Pearson product-moment correlation coefficient and ended up at Standard Deviation.  I didn't make it that far down on the page and used the first, which generally made Voyager Code like this:

I left the print statements in, because the output is important.

Avg AADT_TRK = 1121.77
Avg VOLUME = 822.03
n = 230.00



sdx1 = 1588160175
sdy1 = 1196330474
n = 230.00
sd AADT_TRK = 2627.75
sd Volume = 2280.67
r2 = 155.06

Note the standard deviations above.  Ignore the R2 because it's most certainly not correct!

Again, mistrusting my own calculations, I imported the DBF into R and looked at the standard deviations:

> sd(trkIn$AADT_TRK)
[1] 2633.476
> sd(trkIn$V_1)
[1] 2285.64

Now Standard Deviation is pretty easy to compute.  So WHY ARE THESE DIFFERENT?

Just for fun, I did the same in Excel:

Screenshot 2015-07-24 11.09.18

WTF? Am I right or wrong???

So I started looking into it and recalled something about n vs. n-1 in the RMSE equation and discussion in the latest Model Validation and Reasonableness Checking Manual.  So I decided to manually code the standard deviation in Excel and use sqrt(sum(x-xavg)^2/n-1) instead of Excel's function:

Looky there!  Matching Numbers!

Looky there! Matching Numbers!

It's not that Excel is incorrect, it's not using Bessel's Correction.  R is.

/A

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