PRESS statistic from a regression using a power transformation

This macro computes the model fits, residuals, deleted fits, deleted prediction sum of squares (PRESS) residuals, and the PRESS statistic in the original units of the response when a power transformation of the response is applied in a linear regression.

This macro computes the model fits, residuals, deleted fits, deleted prediction sum of squares ((PRESS) residuals, and the PRESS statistic in the original units of the response when a power transformation of the response is applied in a linear regression.

Download the Macro

Be sure that Minitab knows where to find your downloaded macro. Choose File > Options > General. Under Macro location browse to the location where you save macro files.

Important

If you use an older web browser, when you click the Download button, the file may open in Quicktime, which shares the .mac file extension with Minitab macros. To save the macro, right-click the Download button and choose Save target as.

Download PRESS.mac

Required Inputs

The number of predictors in the regression
The storage columns for the predictors
The storage column for the response
The power transformation parameters

Running the Macro

Suppose you have one predictor variable stored in C1, and the response variable is in C2. The transformation parameter value was -1.

Choose View > Command Line/History and type the following:
```
%PRESS
```

Click Run. You will be prompted for additional information. For example:

Please enter the number of predictor variables in the regression...
DATA> 1
Please enter column number of predictor variable...
DATA> 1
Please enter column number of response variable...
DATA> 2
Please enter response power transformation parameter value...
DATA> -1 <-- reciprocal transformation of response specified

More Information

References

Allen, D. M. (1971), "The Prediction Sum of Squares as a Criterion for Selecting Predictor Variables," Technical Report Number 23, Department of Statistics, University of Kentucky.

Delozier, M. R. (2004), Introduction to Applied Industrial Statistics, Industrial Short-Course Participant Manual.

Myers, R. H. (1990), Classical and Modern Regression