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Stata Textbook Examples
Regression Models for Categorical and Limited Dependent Variables
Chapter 2: Continuous Outcomes: The Linear Regression Model

Note: This chapter uses a suite of commands, called spost, written by J. Scott Long and Jeremy Freese. The commands must be downloaded prior to their use, and this can be done by typing findit spost on the Stata command line (see How can I use the findit command to search for programs and get additional help? for more information about using findit).
Table 2.1, page 19.
use http://www.ats.ucla.edu/stat/stata/examples/long/regjob2, clear

describe

Contains data from use http://www.ats.ucla.edu/stat/stata/examples/long/regjob2.dta
  obs:           408                          Academic Biochemists / S Long
 vars:             7                          15 Jan 2001 15:24
 size:         8,568 (99.9% of memory free)   (_dta has notes)
-------------------------------------------------------------------------------
              storage  display     value
variable name   type   format      label      variable label
-------------------------------------------------------------------------------
job             float  %9.0g                  Prestige of first job
fem             byte   %9.0g       sexlbl     Gender: 1=female 0=male
phd             float  %9.0g                  PhD prestige
ment            float  %9.0g                  Citations received by mentor
fel             byte   %9.0g       fellbl     Fellow: 1=yes 0=no
art             byte   %9.0g                  # of articles published
cit             int    %9.0g                  # of citations received
-------------------------------------------------------------------------------
Sorted by:  job

sum

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         job |       408    2.233431    .9736029          1        4.8
         fem |       408    .3897059    .4882823          0          1
         phd |       408    3.200564    .9537509          1        4.8
        ment |       408    45.47058    65.52988          0   531.9999
         fel |       408    .6176471    .4865587          0          1
-------------+--------------------------------------------------------
         art |       408    2.276961    2.256143          0         18
         cit |       408    21.71569    33.05988          0        203
Table 2.2, page 20.
reg job fem phd ment fel art cit

      Source |       SS       df       MS              Number of obs =     408
-------------+------------------------------           F(  6,   401) =   17.78
       Model |  81.0584763     6  13.5097461           Prob > F      =  0.0000
    Residual |  304.737915   401  .759944926           R-squared     =  0.2101
-------------+------------------------------           Adj R-squared =  0.1983
       Total |  385.796392   407  .947902683           Root MSE      =  .87175

------------------------------------------------------------------------------
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         fem |  -.1391939   .0902344    -1.54   0.124    -.3165856    .0381977
         phd |   .2726826   .0493183     5.53   0.000     .1757278    .3696375
        ment |   .0011867   .0007012     1.69   0.091    -.0001917    .0025651
         fel |   .2341384   .0948206     2.47   0.014     .0477308    .4205461
         art |   .0228011   .0288843     0.79   0.430    -.0339824    .0795846
         cit |   .0044788   .0019687     2.28   0.023     .0006087     .008349
       _cons |   1.067184   .1661357     6.42   0.000     .7405785     1.39379
------------------------------------------------------------------------------

listcoef, help /*listcoef part of spostado*/

regress (N=408): Unstandardized and Standardized Estimates 

 Observed SD: .97360294
 SD of Error: .8717482

-------------------------------------------------------------------------------
         job |      b         t     P>|t|    bStdX    bStdY   bStdXY      SDofX
-------------+-----------------------------------------------------------------
         fem |  -0.13919   -1.543   0.124  -0.0680  -0.1430  -0.0698     0.4883
         phd |   0.27268    5.529   0.000   0.2601   0.2801   0.2671     0.9538
        ment |   0.00119    1.692   0.091   0.0778   0.0012   0.0799    65.5299
         fel |   0.23414    2.469   0.014   0.1139   0.2405   0.1170     0.4866
         art |   0.02280    0.789   0.430   0.0514   0.0234   0.0528     2.2561
         cit |   0.00448    2.275   0.023   0.1481   0.0046   0.1521    33.0599
-------------------------------------------------------------------------------
       b = raw coefficient
       t = t-score for test of b=0
   P>|t| = p-value for t-test
   bStdX = x-standardized coefficient
   bStdY = y-standardized coefficient
  bStdXY = fully standardized coefficient
   SDofX = standard deviation of X

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