regression result only except first variable

[Young Hwang]

Hello

When I regress as below, why don't I get the result for the first variable and how can I fix this?
It doesn't say even it is because of omitted variable...

[Joseph Coveney]

Try something like the following.

.ÿ
.ÿversionÿ15.1

.ÿ
.ÿsetÿseedÿ`=strreverse("1491005")'

.ÿ
.ÿquietlyÿsysuseÿauto,ÿclear

.ÿ
.ÿquietlyÿreplaceÿrep78ÿ=ÿruniformint(1,ÿ5)ÿifÿmi(rep78)

.ÿ
.ÿregressÿgear_ratioÿibn.rep78,ÿnoconstant

ÿÿÿÿÿÿSourceÿ|ÿÿÿÿÿÿÿSSÿÿÿÿÿÿÿÿÿÿÿdfÿÿÿÿÿÿÿMSÿÿÿÿÿÿNumberÿofÿobsÿÿÿ=ÿÿÿÿÿÿÿÿ74
-------------+----------------------------------ÿÿÿF(5,ÿ69)ÿÿÿÿÿÿÿÿ=ÿÿÿÿ722.64
ÿÿÿÿÿÿÿModelÿ|ÿÿ674.925991ÿÿÿÿÿÿÿÿÿ5ÿÿ134.985198ÿÿÿProbÿ>ÿFÿÿÿÿÿÿÿÿ=ÿÿÿÿ0.0000
ÿÿÿÿResidualÿ|ÿÿÿ12.888809ÿÿÿÿÿÿÿÿ69ÿÿ.186794333ÿÿÿR-squaredÿÿÿÿÿÿÿ=ÿÿÿÿ0.9813
-------------+----------------------------------ÿÿÿAdjÿR-squaredÿÿÿ=ÿÿÿÿ0.9799
ÿÿÿÿÿÿÿTotalÿ|ÿÿÿÿ687.8148ÿÿÿÿÿÿÿÿ74ÿÿ9.29479459ÿÿÿRootÿMSEÿÿÿÿÿÿÿÿ=ÿÿÿÿÿ.4322

------------------------------------------------------------------------------
ÿÿgear_ratioÿ|ÿÿÿÿÿÿCoef.ÿÿÿStd.ÿErr.ÿÿÿÿÿÿtÿÿÿÿP>|t|ÿÿÿÿÿ[95%ÿConf.ÿInterval]
-------------+----------------------------------------------------------------
ÿÿÿÿÿÿÿrep78ÿ|
ÿÿÿÿÿÿÿÿÿÿ1ÿÿ|ÿÿÿÿÿÿ2.905ÿÿÿ.3056095ÿÿÿÿÿ9.51ÿÿÿ0.000ÿÿÿÿÿ2.295326ÿÿÿÿ3.514674
ÿÿÿÿÿÿÿÿÿÿ2ÿÿ|ÿÿÿ2.781111ÿÿÿ.1440657ÿÿÿÿ19.30ÿÿÿ0.000ÿÿÿÿÿ2.493708ÿÿÿÿ3.068514
ÿÿÿÿÿÿÿÿÿÿ3ÿÿ|ÿÿÿ2.898788ÿÿÿ.0752359ÿÿÿÿ38.53ÿÿÿ0.000ÿÿÿÿÿ2.748696ÿÿÿÿ3.048879
ÿÿÿÿÿÿÿÿÿÿ4ÿÿ|ÿÿÿ3.176111ÿÿÿ.1018698ÿÿÿÿ31.18ÿÿÿ0.000ÿÿÿÿÿ2.972886ÿÿÿÿ3.379336
ÿÿÿÿÿÿÿÿÿÿ5ÿÿ|ÿÿÿ3.285833ÿÿÿ.1247646ÿÿÿÿ26.34ÿÿÿ0.000ÿÿÿÿÿ3.036935ÿÿÿÿ3.534732
------------------------------------------------------------------------------

.ÿ
.ÿexit

endÿofÿdo-file


.

help factor variables

[William Lisowski]

Stata will always omit the "base level" of a set of indicator variables constructed from a categorical variable - it doesn't try to "deduce" that because the constant is omitted there would be no problem with collinearity.

You can tell Stata there is to be no "base level" by using ibn.G1 rather than i.G1 (see help factor variables for details).

. sysuse auto, clear
(1978 Automobile Data)

. reg price i.rep78, nocons

      Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(4, 65)        =     68.53
       Model |  2.5731e+09         4   643271726   Prob > F        =    0.0000
    Residual |   610105737        65   9386242.1   R-squared       =    0.8083
-------------+----------------------------------   Adj R-squared   =    0.7965
       Total |  3.1832e+09        69  46133226.7   Root MSE        =    3063.7

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       rep78 |
          2  |   5967.625   1083.181     5.51   0.000     3804.364    8130.886
          3  |   6429.233   559.3521    11.49   0.000      5312.13    7546.337
          4  |     6071.5   722.1204     8.41   0.000     4629.326    7513.674
          5  |       5913   923.7395     6.40   0.000     4068.165    7757.835
------------------------------------------------------------------------------

. reg price ibn.rep78, nocons

      Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(5, 64)        =     58.88
       Model |  2.6148e+09         5   522951245   Prob > F        =    0.0000
    Residual |   568436416        64     8881819   R-squared       =    0.8214
-------------+----------------------------------   Adj R-squared   =    0.8075
       Total |  3.1832e+09        69  46133226.7   Root MSE        =    2980.2

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       rep78 |
          1  |     4564.5   2107.347     2.17   0.034     354.5913    8774.409
          2  |   5967.625   1053.673     5.66   0.000     3862.671    8072.579
          3  |   6429.233   544.1145    11.82   0.000      5342.24    7516.227
          4  |     6071.5   702.4489     8.64   0.000     4668.197    7474.803
          5  |       5913   898.5756     6.58   0.000     4117.889    7708.111
------------------------------------------------------------------------------