Given the dataset below, for each idnum, I would like to create two new
variables which summarize the least squares relationship between lcresponse
and lresistnc. Ie. newvar1=slope, newvar2 = y-intercept.
The following command using "by" generates regression output for each idnum:
by idnum: regress lcresponse lresistnc
However, I'm stuck on how to save the slope '_b[lresistnc]'
and yint '-b[_cons]' as new variables for each idnum.
Thanks...
idnum lcresponse lresistnc
1. 401 . .
2. 401 .3579348 .39794
3. 401 .5587085 .69897
4. 401 .6627578 1
5. 401 .888741 1.30103
6. 401 .923244 1.477121
7. 402 . .
8. 402 .0253059 .39794
9. 402 .071882 .69897
10. 402 .5658478 1
11. 402 .748188 1.30103
12. 402 .9314579 1.477121
13. 500 . .
14. 500 -.39794 .39794
15. 500 .2741579 .69897
16. 500 .4183013 1
17. 500 .7041505 1.30103
18. 500 .7512791 1.47712
...
. by idnum: regress lcresponse lresistnc
____________________________________________________________________________
___
-> idnum = 401
Source | SS df MS Number of obs =
5
-------------+------------------------------ F( 1, 3) =
180.84
Model | .217846518 1 .217846518 Prob > F =
0.0009
Residual | .003613953 3 .001204651 R-squared =
0.9837
-------------+------------------------------ Adj R-squared =
0.9782
Total | .221460472 4 .055365118 Root MSE =
.03471
----------------------------------------------------------------------------
--
lcresponse | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
lresistnc | .5325099 .0395989 13.45 0.001 .4064885
.6585312
_cons | .1590736 .0416127 3.82 0.032 .0266434
.2915037
----------------------------------------------------------------------------
--
...
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