Dear friends,

I am trying to analyze the effect of this three variables (ml, mms, mic) in the outcome score. For that, I have tried cut the variable score (which is score10) and apply the command logistic. However, the variable mic is already include when I have to genarate the variable score. Therefore, my questions are:

1) Make sense use the variable mic that is already included in the previous model.

2) If I use the log values of the variables and apply regress without cut the variable the effect is different, what is the best in this case.



[CODE]
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(score score10 ml mms mic)
  .3  0      25.6  6122.143 .
  .3  0      19.6  5509.286 0
 3.6  0  28.01327  5147.143 0
  .7  0  22.11574      5025 0
  .7  0      28.2      6150 0
 3.6  0 37.449318  3859.286 0
 6.5  0  28.97162      5925 0
 7.6  0  24.54847  4317.857 0
  .1  0      20.7  6537.857 .
 7.6  0  73.35053  3544.286 0
 1.1  0 29.856247      4635 1
  .1  0  24.62219  6304.286 0
  .3  0      14.2  6812.143 0
 3.6  0      34.1      4920 .
47.4 10      41.4  7088.571 1
28.8 10        39  5631.429 0
 3.6  0  52.04571      4680 0
 1.6  0      10.8  5492.143 .
  .1  0  67.08441  5942.143 0
12.7 10       9.1  6893.571 1
 1.1  0  74.53004      5010 0
 1.1  0      21.2  5723.571 0
  .1  0 11.573903      6465 0
 7.6  0      31.6      5145 0
  .1  0  49.24438  4452.857 0
 1.1  0      26.3 4836.4287 0
 7.6  0  97.23553 4836.4287 .
 6.5  0 127.38666  5792.143 .
 1.6  0 14.596388  5498.571 .
47.4 10  96.27718 4444.2856 0
47.4 10      29.6  6083.571 1

Code:
logistic score10 ml mms  mic

Logistic regression                               Number of obs   =         24
                                                  LR chi2(3)      =      12.98
                                                  Prob > chi2     =     0.0047
Log likelihood = -5.7902812                       Pseudo R2       =     0.5285

------------------------------------------------------------------------------
     score10 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         lps |   1.087406   .0461957     1.97   0.049     1.000531    1.181824
       scd14 |   1.001812   .0011957     1.52   0.129     .9994716    1.004158
 infebac_cul |   78.14645    165.468     2.06   0.040     1.231878    4957.363
------------------------------------------------------------------------------

. regress score ml mms  mic

      Source |       SS       df       MS              Number of obs =      24
-------------+------------------------------           F(  3,    20) =    5.84
       Model |  2698.21218     3  899.404059           Prob > F      =  0.0049
    Residual |  3077.56648    20  153.878324           R-squared     =  0.4672
-------------+------------------------------           Adj R-squared =  0.3872
       Total |  5775.77865    23  251.120811           Root MSE      =  12.405

------------------------------------------------------------------------------
       score |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        lpsl |    15.0346   5.339857     2.82   0.011     3.895857    26.17335
      scd14l |   24.36535   17.51601     1.39   0.179     -12.1724     60.9031
 infebac_cul |   21.82136   7.222988     3.02   0.007     6.754472    36.88825
       _cons |  -254.8052   160.3527    -1.59   0.128    -589.2951     79.6846
------------------------------------------------------------------------------

. regress score ml mms 

      Source |       SS       df       MS              Number of obs =      31
-------------+------------------------------           F(  2,    28) =    2.17
       Model |   813.12484     2   406.56242           Prob > F      =  0.1332
    Residual |  5249.63484    28  187.486959           R-squared     =  0.1341
-------------+------------------------------           Adj R-squared =  0.0723
       Total |  6062.75968    30  202.091989           Root MSE      =  13.693

------------------------------------------------------------------------------
       score |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        lpsl |   8.534702   4.276687     2.00   0.056    -.2256938     17.2951
      scd14l |   23.85516   16.56384     1.44   0.161    -10.07432    57.78464
       _cons |  -226.2016   149.5938    -1.51   0.142    -532.6305    80.22734
------------------------------------------------------------------------------

. logistic score10 ml mms  

Logistic regression                               Number of obs   =         31
                                                  LR chi2(2)      =       3.80
                                                  Prob > chi2     =     0.1494
Log likelihood = -11.794517                       Pseudo R2       =     0.1388

------------------------------------------------------------------------------
     score10 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         lps |   1.017974   .0178374     1.02   0.309     .9836072    1.053542
       scd14 |   1.001284   .0007506     1.71   0.087     .9998135    1.002756
------------------------------------------------------------------------------
Thank you very much