MTB > let c4 = c3/c2
 MTB > let c5 = loge(c4/(1 - c4))
 MTB > print c1-c5
 
  ROW    C1     C2     C3      C4        C5
 
    1     5    200     32   0.160  -1.65823
    2    10    200     51   0.255  -1.07212
    3    15    200     70   0.350  -0.61904
    4    20    200    103   0.515   0.06002
    5    30    200    148   0.740   1.04597
 
 MTB > let c6 = c2*c4*(1 - c4)
 MTB > print c1-c6
 
  ROW    C1     C2     C3      C4        C5       C6
 
    1     5    200     32   0.160  -1.65823   26.880
    2    10    200     51   0.255  -1.07212   37.995
    3    15    200     70   0.350  -0.61904   45.500
    4    20    200    103   0.515   0.06002   49.955
    5    30    200    148   0.740   1.04597   38.480
 
 MTB > plot c5 c1 
 
          -
       1.0+                                                       *
          -
  C5      -
          -
          -
       0.0+                                   *
          -
          -
          -                         *
          -
      -1.0+               *
          -
          -
          -     *
          -
      -2.0+
            ----+---------+---------+---------+---------+---------+--C1      
              5.0      10.0      15.0      20.0      25.0      30.0

 MTB > regress c5 1 c1 c11 c12 
 
 The regression equation is
 C5 = - 2.19 + 0.109 C1
 
 Predictor       Coef       Stdev    t-ratio        p
 Constant    -2.18602     0.05722     -38.20    0.000
 C1          0.108584    0.003150      34.47    0.000
 
 s = 0.06059     R-sq = 99.7%     R-sq(adj) = 99.7%
 
 Analysis of Variance
 
 SOURCE       DF          SS          MS         F        p
 Regression    1      4.3624      4.3624   1188.27    0.000
 Error         3      0.0110      0.0037
 Total         4      4.3735
 
 
 
 MTB > regress c5 1 c1 c21 c22;
 SUBC> weights c6.
 
 The regression equation is
 C5 = - 2.19 + 0.109 C1
 
 Predictor       Coef       Stdev    t-ratio        p
 Constant    -2.18506     0.06783     -32.21    0.000
 C1          0.108700    0.003632      29.93    0.000
 
 Analysis of Variance
 
 SOURCE       DF          SS          MS         F        p
 Regression    1      151.98      151.98    895.53    0.000
 Error         3        0.51        0.17
 Total         4      152.49
 
 
 
 MTB > print c1 c4 c12 c22
 
  ROW    C1      C4       C12       C22
 
    1     5   0.160  -1.64310  -1.64156
    2    10   0.255  -1.10018  -1.09806
    3    15   0.350  -0.55726  -0.55455
    4    20   0.515  -0.01435  -0.01105
    5    30   0.740   1.07149   1.07595
 
 MTB > let c13 = expo(c12)/(1 + expo(c12))
 MTB > let c23 = expo(c22)/(1 + expo(c22))
 MTB > print c1 c4 c13 c23
 
  ROW    C1      C4        C13        C23
 
    1     5   0.160   0.162044   0.162253
    2    10   0.255   0.249706   0.250104
    3    15   0.350   0.364181   0.364808
    4    20   0.515   0.496414   0.497237
    5    30   0.740   0.744880   0.745727