That's what you'd guess. -regress- is compiled code, beyond a minor
wrapper, and it knows the direct way to a solution. Neither is quite
true of -nl-.
The advantage of -nl- here is at most convenience in making the call.
Nick
[email protected]
Friedrich Huebler
-nl- is slower than -regress- and produces identical results.
. sysuse auto
. gen weight2 = weight^2
. regress mpg weight weight2
Source | SS df MS Number of obs =
74
-------------+------------------------------ F( 2, 71) =
72.80
Model | 1642.52197 2 821.260986 Prob > F =
0.0000
Residual | 800.937487 71 11.2808097 R-squared =
0.6722
-------------+------------------------------ Adj R-squared =
0.6630
Total | 2443.45946 73 33.4720474 Root MSE =
3.3587
------------------------------------------------------------------------
------
mpg | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
weight | -.0141581 .0038835 -3.65 0.001 -.0219016
-.0064145
weight2 | 1.32e-06 6.26e-07 2.12 0.038 7.67e-08
2.57e-06
_cons | 51.18308 5.767884 8.87 0.000 39.68225
62.68392
------------------------------------------------------------------------
------
. predict pmpg1
. nl (mpg = {a} + {b1}*weight + {b2}*weight^2), variables(weight)
(obs = 74)
Iteration 0: residual SS = 800.9375
Iteration 1: residual SS = 800.9375
Source | SS df MS
-------------+------------------------------ Number of obs =
74
Model | 1642.52197 2 821.260986 R-squared =
0.6722
Residual | 800.937487 71 11.2808097 Adj R-squared =
0.6630
-------------+------------------------------ Root MSE =
3.358692
Total | 2443.45946 73 33.4720474 Res. dev. =
386.25
------------------------------------------------------------------------
------
mpg | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
/a | 51.18308 5.767884 8.87 0.000 39.68225
62.68392
/b1 | -.0141581 .0038835 -3.65 0.001 -.0219016
-.0064145
/b2 | 1.32e-06 6.26e-07 2.12 0.038 7.67e-08
2.57e-06
------------------------------------------------------------------------
------
Parameter a taken as constant term in model & ANOVA table
. predict pmpg2
. compare pmpg1 pmpg2
---------- difference ----------
count minimum average maximum
------------------------------------------------------------------------
pmpg1=pmpg2 74
----------
jointly defined 74 0 0 0
----------
total 74
On Tue, Feb 17, 2009 at 2:15 AM, Martin Weiss <[email protected]>
wrote:
> <>
>
> Three responses advised you to create a new variable and then use
-regress-.
> Note, though, that a better option would be to use -nl-
>
> nl (y = {a} + {b1}*x + {b2}*x^2), variables(x)
>
> as in http://www.stata-journal.com/article.html?article=st0141
>
> which would allow Stata to know that x and x squared "move in tandem"
and
> calculate the correct marginal effects...
>
_______________________
> ----- Original Message ----- From: "Shell makka"
<[email protected]>
>>
>> It would be greatly appreciated if you can answer my question.
>> I would like to fit a quadratic regression Model (Y=a+bX+cX^2) on my
>> data and do predictions , would you please let me know what will be
>> the code for that in stata?
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