# Thread: RMSE vs. standard error

1. ## RMSE vs. standard error

RMSE vs. standard error

Date: May 29, 2011 10:14PM

RMSE is the square root of the mean squared error.

Standard Error of Estimate (SEE) = square root of sum of squares divided by n-k-1

So does RMSE= SEE?

2. Date: May 30, 2011 01:25AM

Way to confuse. Throw in a quant question, and stare at the blank faces of candidates.

By the way i'd think the answer to your question is NO.

SEE = std deviation of error terms.
SEE = sqrt(variance of error)
SEE = sqrt(SSE/n-k-1)

where as MSE = SSE/ n-k-1 <-- there is no square root here.
SSE = squared sum of all errors, or residual sum of errors.

SSE/n-k-1 is not equal to SEE.

By the way what is RMSE? seeing it for the first time.

3. Date: May 30, 2011 01:59AM

they are not the same thing, but closely related. RMSE is for the MEAN, not the total errors. it is the average error.

4. Date: May 30, 2011 09:03AM

RMSE is sqrt(MSE). Same thing as far as I can tell.

It's a tool used to gauge in-sample and out-fo-sample forecasting accuracy. Low RMSE relative to another model = better forecasting.

5. Date: May 30, 2011 09:30AM

As is with SEE

6. Date: May 30, 2011 09:50AM

So it boils down to whether MSE = Sum of squares / n, or MSE = sum of squares / n-k-1. On an Anove table you will find MSS and the associated degrees of freedom is n-k-1.

I think denominator for MSE = n, denominator in the SEE is n-k-1 and that's my story.

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