# 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

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.

• 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? Commented Aug 27, 2012 at 21:07