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This is a pretty basic question, but I can't find an answer by searching for different statements of the same problem.

Is there a straightforward way to test if a regression parameter is different from a non-zero value in (non-) linear regression? I can only think of one way, but it seems too roundabout. Here's an example:

INPUT PROGRAM.
       LOOP #I = 1 TO 10.
             COMPUTE X = #I.
             COMPUTE Y = RV.NORMAL(5,1)+x*RV.NORMAL(4,1.5).
           END CASE.
       END LOOP.
       END FILE.
END INPUT PROGRAM.
dataset name exampleData WINDOW=front.
EXECUTE.

Say I want to test if the slope from linear regression deviates from 3. The only way I know how in SPSS is NLR:

MODEL PROGRAM  b0=5 b1=0.
COMPUTE  PRED_=b0 + (3+b1)*x.
NLR Y
  /OUTFILE='C:\temp\SPSSFNLR.TMP'
  /PRED PRED_
  /CRITERIA SSCONVERGENCE 1E-8 PCON 1E-8.

This will give you an estimate, std. error, and 95% CI for b1, but no p value. I also get a weird feeling that this method isn't statistically "correct". Does SPSS have a built-in test for this without resorting to NLR? Is there one that gives a p value?

What if my regression model is non-linear? I suppose I can use the same approach as above, but how can I get a p value?

EDIT: I would like to clarify that I am asking about more than just SPSS methodology. Although I'd love to get an SPSS-specific answer, I am also looking for a more general statistical method to a) get a p value from regression parameters from the estimate, std. error, and degrees of freedom and b) if there's a statistical test designed to test deviation of a regression parameter from a non-zero constant.

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