<>
-regress- holds an advantage of btw 0.1 and 0.13 seconds. But lacks the
advantages enumerated in
http://www.stata-journal.com/article.html?article=st0141
*************
sysuse auto, clear
cap timer clear
timer on 1
gen weight2 = weight^2
regress mpg weight weight2
predict pmpg1
timer off 1
timer on 2
nl (mpg = {a} + {b1}*weight + {b2}*weight^2), variables(weight)
predict pmpg2
timer off 2
timer list
*************
Anyway, it was just a suggestion which hopefully helps Shell sometime down
the road. It is unlikely that all he will ever want from Stata is predicted
values. At some point, marginal effects will become part of his agenda...
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Friedrich
Huebler
Gesendet: Dienstag, 17. Februar 2009 16:38
An: [email protected]
Betreff: Re: st: Re: Quadratic regression
-nl- is slower than -regress- and produces identical results.
Friedrich
. 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...
>
> HTH
> Martin
> _______________________
> ----- Original Message ----- From: "Shell makka" <[email protected]>
> To: <[email protected]>
> Sent: Tuesday, February 17, 2009 4:18 AM
> Subject: st: Quadratic regression
>
>
>> Dear statalist
>>
>>
>> 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?
>>
>>
>> Many thanks,
>> Shell
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