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st: RE: why is F statistic still missing even though there is no singleton dummy problem?


From   "Maarten Buis" <[email protected]>
To   <[email protected]>
Subject   st: RE: why is F statistic still missing even though there is no singleton dummy problem?
Date   Fri, 18 Aug 2006 09:56:06 +0200

Jian Zhang:
Your data has very extreme values and ones you know var5, var6 doesn't add enough information. I would perform the following transformations:
o I would add either var5 or var6, but not both
o I would either recode var5 as a dummy distinguishing between zero and non zero values or if the sign is really important I would accept that I just don't have enough cases to make such a distinction and not add that variable at all.
o I would replace var1 and var3 with their natural logarithms to "tug in" the outliers.

see example below:

*----- begin example -----
input /*
*/var1   var3   var5   var6 
   1    789      0      0 
   3     45      0      0 
   5   2358     -4      4 
   4     65      0      0 
   5     12     14     -7 
 453     12      0      0 
   6      4      0      0 
  45      2      0      0 
   8      3   -123    123 
  897      5      0      0 
   43     87      0      0 
   43     56      0      0 
    4     25      0      0 
end

gen lnvar1 = ln(var1)
gen dvar5 = var5!=0
gen lnvar3=ln(var3)
reg lnvar1 lnvar3 dvar5
predict yhatnew
twoway scatter lnvar1 lnvar3 if dvar5==0 || /*
    */ scatter lnvar1 lnvar3 if dvar5==1 || /*
    */ line yhatnew lnvar3 if dvar==0, sort || /*
    */ line yhatnew lnvar3 if dvar==1, sort /*
    */ name(newdata)
    
reg var1 var3 dvar5
predict yhatold
twoway scatter var1 var3 if dvar5==0 || /*
    */ scatter var1 var3 if dvar5==1 || /*
    */ line yhatold var3 if dvar==0, sort || /*
    */ line yhatold var3 if dvar==1, sort /*
    */ name(olddata)
*------- end example -------

HTH,
Maarten

-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology 
Vrije Universiteit Amsterdam 
Boelelaan 1081 
1081 HV Amsterdam 
The Netherlands

visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z434 

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------

-----Original Message-----
From: [email protected] [mailto:[email protected]]On Behalf Of Jian Zhang
Sent: vrijdag 18 augustus 2006 1:51
To: [email protected]
Subject: st: why is F statistic still missing even though there is no singleton dummy problem?

Dear Statalisters,

I am running a ols regression on a small data set.  The reported F statistic is still mssing
although the data set doesn' have so-called singleton dummies.  Is there anyone knowing what is
going on?  Here are the data set and the regression results.  Many thanks!


. list var1 var3 var5 var6

+---------------------------+
      var1   var3   var5   var6
---------------------------
1.     1    789      0      0
2.     3     45      0      0
3.     5   2358     -4      4
4.     4     65      0      0
5.     5     12     14     -7
---------------------------
6.   453     12      0      0
7.     6      4      0      0
8.    45      2      0      0
9.     8      3   -123    123
10.   897      5      0      0
---------------------------
11.    43     87      0      0
12.    43     56      0      0
13.     4     25      0      0
+---------------------------+


 reg var1 var3 var5 var6,       robust nocons

Linear regression                       Number of obs   =      13
                        F(  2,    10)   =       .
                        Prob > F        =       .
                        R-squared       =  0.0002
                        Root MSE        =  318.67

                               
        Robust
var1       Coef.        Std. Err.      t        P>t     [95% Conf.      Interval]
                               
var3    .0046225        .0031     1.49  0.167   -.0022847       .0115297
var5    .7696625        .0060938   126.30       0.000   .7560848        .7832403
var6    .8329636        .007418   112.29        0.000   .8164353        .8494919
                               






Best regards,
Jian Zhang 
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