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Re: st: Re: Quadratic regression


From   Friedrich Huebler <fhuebler@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: Quadratic regression
Date   Tue, 17 Feb 2009 10:38:03 -0500

-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 <martin.weiss1@gmx.de> 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" <shell.makka@gmail.com>
> To: <statalist@hsphsun2.harvard.edu>
> 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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