# How to perform RMSE analysis in SPSS?

My thesis coach wants me to perform a predictive analysis based on OLS. What I understand is the following:

• divide the dataset into a training set and a holdout set, for instance 50-50
• perform OLS on the training set
• construct linear equation based on regression output
• create a new variable (DV2) in the holdout set, and use the linear equation to calculate its values
• now you have F (forecasted) and A (actual) DV values in the holdout set
• calculate the performance of the predictive linear equation with RMSE:
• a lower RMSE is better

• Am I doing this right?
• I have no clue how to have SPSS perform the RSME operation, so can't I just do it in Excel? If I paste the holdout set into Excel, performing this calculation seems easy enough. Is there something I'm missing?
• If you know how to perform this calculation in SPSS, please let me know because I expect that SPSS might be able to output some extra insightful statistics and / or graphs
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Compute your random sample definition, e.g.,

compute part = rv.uniform(0,1) <= .5.


Run the regression. Include this subcommand

/SELECT part EQ 1


and this

/SAVE PRED RESID


You can do this by specifying a selection variable in the Regression dialog box and by using the Save subdialog.

Now select the other part of the data, e.g.,

compute holdout = 1 - part.


Run Descriptives on RES_1.

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 I dont'quite understand this. I understand that by compuate part, you create a holdout and testset. I already got those. Then you perform OLS on the testset and save the residuals. But then I'm lost. You then just run descriptives on the residuals from the trainingset? – Pr0no Aug 27 '12 at 21:07