Hello Everyone,

Am working on Stata 17.

Following are my code and output:
Please note that Y is a binary outcome (0 and 1).
Am using the polynomial option, not allowing the Xs to interact with each other, and including a factor control asis.

Code:
. npregress series Y X1 X2 X3 X4 X5 X6 X7 X8 X9, nointeract(X1 X2 X3 X4 X5 X6 X7 X8 X9) asis(i.gender) polynomial

Computing approximating function

Minimizing cross-validation criterion

Iteration 0:  Cross-validation criterion =  .2065269

Computing average derivatives

Polynomial-series estimation               Number of obs      =            301
Criterion: cross-validation                Polynomial order   =                2
------------------------------------------------------------------------------
             |               Robust
           Y |     Effect   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          X1 |   -.090843    .042029    -2.16   0.031    -.1732184   -.0084676
          X2 |   .0012329   .0459111     0.03   0.979    -.0887512    .0912171
          X3 |   .0515722   .0271503     1.90   0.057    -.0016414    .1047858
          X4 |  -.0221318    .034087    -0.65   0.516     -.088941    .0446775
          X5 |   .1323169   .0408576     3.24   0.001     .0522375    .2123964
          X6 |     .02101   .0362033     0.58   0.562    -.0499472    .0919672
          X7 |   .0310251   .0384144     0.81   0.419    -.0442657    .1063159
          X8 |   .0662618    .024961     2.65   0.008     .0173391    .1151846
          X9 |   .0710502   .0317508     2.24   0.025     .0088197    .1332807
             |
      gender |
   (1 vs 0)  |   -.135051    .056472    -2.39   0.017    -.2457342   -.0243678
------------------------------------------------------------------------------

The effect for X1 is negative and significant.

Had this been a logistic regression, I may have interpreted that unit increase in X1 reduces the likelihood of Y being = 1 by ...

But here, I must interpret it as unit increase in X1 reduces Y by 0.09. Am I correct?

Also, I drew up the marginsplot for X1 for the actual range of X1 in the dataset.

Code:
margins, at(X1=(1(0.25)7))
marginsplot
This is the output:
Array

So while the effect of X1 simply showed negative (-.091) in the output, the plot shows diminishing returns.
"Mean function" on y=axis and values of X1 on the x-axis

How am I to finally interpret the results?

Simply say increase in X1 reduces Y or that there is a diminishing returns relationship?

Please advise.