 MTB > plot c2 c1
 
      1.05+
          -         *         *         *   *   2     *     *   2   *
  C2      -
          -
          -
      0.70+
          -
          -
          -
          -
      0.35+
          -
          -
          -
          -
      0.00+ 2 * 2     *   * * * *       * *         *         *
          -
            --+---------+---------+---------+---------+---------+----C1      
            5.0      10.0      15.0      20.0      25.0      30.0
 

 MTB > regress c2 1 c1 c11 c12
 
 The regression equation is
 C2 = - 0.092 + 0.0315 C1
 
 Predictor       Coef       Stdev    t-ratio        p
 Constant     -0.0922      0.1833      -0.50    0.620
 C1          0.031528    0.009606       3.28    0.003
 
 s = 0.4271      R-sq = 31.9%     R-sq(adj) = 28.9%
 
 Analysis of Variance
 
 SOURCE       DF          SS          MS         F        p
 Regression    1      1.9648      1.9648     10.77    0.003
 Error        23      4.1952      0.1824
 Total        24      6.1600
 
 Unusual Observations
 Obs.      C1        C2       Fit Stdev.Fit  Residual   St.Resid
   2     29.0    0.0000    0.8221    0.1444   -0.8221     -2.05R 
  25      8.0    1.0000    0.1600    0.1207    0.8400      2.05R 
 
 R denotes an obs. with a large st. resid.
 
 MTB > print c1 c2 c12
 
  ROW    C1   C2        C12
 
    1    14    0   0.349199
    2    29    0   0.822122
    3     6    0   0.096973
    4    25    1   0.696009
    5    18    1   0.475312
    6     4    0   0.033916
    7    18    0   0.475312
    8    12    0   0.286142
    9    22    1   0.601425
   10     6    0   0.096973
   11    30    1   0.853651
   12    11    0   0.254614
   13    30    1   0.853651
   14     5    0   0.065444
   15    20    1   0.538368
   16    13    0   0.317670
   17     9    0   0.191557
   18    32    1   0.916707
   19    24    0   0.664481
   20    13    1   0.317670
   21    19    0   0.506840
   22     4    0   0.033916
   23    28    1   0.790594
   24    22    1   0.601425
   25     8    1   0.160029
 
 MTB > mplot c2 c1, c12 c1.
 
      1.05+
          -         A         A         A   A   2     A     A   2   A
          -                                                         B
          -                                                   B 2
          -                                                 B
      0.70+                                           B
          -                                     2   B
          -                                 B
          -                             2 B
          -
      0.35+                   2 B
          -               B B
          -           B
          -         B
          -   B 2
      0.00+ 4 A 2     A   A A A A       A A         A         A
          -
            --+---------+---------+---------+---------+---------+----
            5.0      10.0      15.0      20.0      25.0      30.0
         A = C2 vs. C1             B = C12 vs. C1
 

****start weighted least squares procedures********************************
 MTB > name c3 'wi' 
 MTB > let c3 = 1/(c12*(1 - c12))
 MTB > print c1 c2 c12 c3
 
  ROW    C1   C2        C12        wi
 
    1    14    0   0.349199    4.4003
    2    29    0   0.822122    6.8382
    3     6    0   0.096973   11.4196
    4    25    1   0.696009    4.7263
    5    18    1   0.475312    4.0098
    6     4    0   0.033916   30.5196
    7    18    0   0.475312    4.0098
    8    12    0   0.286142    4.8956
    9    22    1   0.601425    4.1717
   10     6    0   0.096973   11.4196
   11    30    1   0.853651    8.0044
   12    11    0   0.254614    5.2691
   13    30    1   0.853651    8.0044
   14     5    0   0.065444   16.3502
   15    20    1   0.538368    4.0237
   16    13    0   0.317670    4.6135
   17     9    0   0.191557    6.4573
   18    32    1   0.916707   13.0967
   19    24    0   0.664481    4.4854
   20    13    1   0.317670    4.6135
   21    19    0   0.506840    4.0007
   22     4    0   0.033916   30.5196
   23    28    1   0.790594    6.0403
   24    22    1   0.601425    4.1717
   25     8    1   0.160029    7.4394
 
 MTB > regress c2 1 c1 c21 c22;
 SUBC> weights c3.
 
 The regression equation is
 C2 = - 0.117 + 0.0327 C1
 
 Predictor       Coef       Stdev    t-ratio        p
 Constant     -0.1171      0.1118      -1.05    0.306
 C1          0.032672    0.006644       4.92    0.000
 
 Analysis of Variance
 
 SOURCE       DF          SS          MS         F        p
 Regression    1      23.809      23.809     24.18    0.000
 Error        23      22.642       0.984
 Total        24      46.451
 
 Unusual Observations
 Obs.      C1        C2       Fit Stdev.Fit  Residual   St.Resid
   2     29.0     0.000     0.830     0.124    -0.830     -2.32R 
   6      4.0     0.000     0.014     0.092    -0.014     -0.09 X
  18     32.0     1.000     0.928     0.141     0.072      0.30 X
  22      4.0     0.000     0.014     0.092    -0.014     -0.09 X
  25      8.0     1.000     0.144     0.077     0.856      2.41R 
 R denotes an obs. with a large st. resid.
 X denotes an obs. whose X value gives it large influence.
 
 MTB > print c1 c2 c12 c22
 
  ROW    C1   C2        C12        C22
 
    1    14    0   0.349199   0.340294
    2    29    0   0.822122   0.830373
    3     6    0   0.096973   0.078918
    4    25    1   0.696009   0.699685
    5    18    1   0.475312   0.470981
    6     4    0   0.033916   0.013574
    7    18    0   0.475312   0.470981
    8    12    0   0.286142   0.274950
    9    22    1   0.601425   0.601669
   10     6    0   0.096973   0.078918
   11    30    1   0.853651   0.863045
   12    11    0   0.254614   0.242278
   13    30    1   0.853651   0.863045
   14     5    0   0.065444   0.046246
   15    20    1   0.538368   0.536325
   16    13    0   0.317670   0.307622
   17     9    0   0.191557   0.176934
   18    32    1   0.916707   0.928388
   19    24    0   0.664481   0.667013
   20    13    1   0.317670   0.307622
   21    19    0   0.506840   0.503653
   22     4    0   0.033916   0.013574
   23    28    1   0.790594   0.797701
   24    22    1   0.601425   0.601669
   25     8    1   0.160029   0.144262

*********fit from logistic BMDPLR********** 
 MTB > let c7 = expo(-3.06 + .161*c1)
 MTB > let c8 = c7/(1 + c7)
 MTB > print c1 c2 c8
 
  ROW    C1   C2         C8
 
    1    14    0   0.308744
    2    29    0   0.833273
    3     6    0   0.109681
    4    25    1   0.724122
    5    18    1   0.459588
    6     4    0   0.081961
    7    18    0   0.459588
    8    12    0   0.244530
    9    22    1   0.618220
   10     6    0   0.109681
   11    30    1   0.854458
   12    11    0   0.216022
   13    30    1   0.854458
   14     5    0   0.094919
   15    20    1   0.539915
   16    13    0   0.275479
   17     9    0   0.166450
   18    32    1   0.890123
   19    24    0   0.690829
   20    13    1   0.275479
   21    19    0   0.499750
   22     4    0   0.081961
   23    28    1   0.809690
   24    22    1   0.618220
   25     8    1   0.145294
 
 MTB > print c1 c2 c12 c22 c8
 
  ROW    C1   C2        C12        C22         C8
 
    1    14    0   0.349199   0.340294   0.308744
    2    29    0   0.822122   0.830373   0.833273
    3     6    0   0.096973   0.078918   0.109681
    4    25    1   0.696009   0.699685   0.724122
    5    18    1   0.475312   0.470981   0.459588
    6     4    0   0.033916   0.013574   0.081961
    7    18    0   0.475312   0.470981   0.459588
    8    12    0   0.286142   0.274950   0.244530
    9    22    1   0.601425   0.601669   0.618220
   10     6    0   0.096973   0.078918   0.109681
   11    30    1   0.853651   0.863045   0.854458
   12    11    0   0.254614   0.242278   0.216022
   13    30    1   0.853651   0.863045   0.854458
   14     5    0   0.065444   0.046246   0.094919
   15    20    1   0.538368   0.536325   0.539915
   16    13    0   0.317670   0.307622   0.275479
   17     9    0   0.191557   0.176934   0.166450
   18    32    1   0.916707   0.928388   0.890123
   19    24    0   0.664481   0.667013   0.690829
   20    13    1   0.317670   0.307622   0.275479
   21    19    0   0.506840   0.503653   0.499750
   22     4    0   0.033916   0.013574   0.081961
   23    28    1   0.790594   0.797701   0.809690
   24    22    1   0.601425   0.601669   0.618220
   25     8    1   0.160029   0.144262   0.145294
 

 MTB > mplot c12 c1, c8 c1, c2 c1 
 
      1.05+
          -         C         C         C   C   2     C     C   2   C
          -                                                         2
          -                                                 B 2 4
          -                                                 A
      0.70+                                         B 2
          -                                     4   A
          -                                 2
          -                             4 2
          -
      0.35+                   2 A
          -               A A 2 B
          -           A   B B
          -     2   2 B
          - 2 2 2
      0.00+ 4 C 2     C   C C C C       C C         C         C
          -
            --+---------+---------+---------+---------+---------+----
            5.0      10.0      15.0      20.0      25.0      30.0
         A = C12 vs. C1            C = C2 vs. C1
         B = C8 vs. C1             

 MTB > plot c8 c1
 
          -
      0.90+                                                         *
          -                                                   * 2
  C8      -                                                 *
          -                                         * *
          -
      0.60+                                     2
          -                                 *
          -                             2 *
          -
          -
      0.30+                   2 *
          -               * *
          -           *
          -   * 2   *
          - 2
      0.00+
            --+---------+---------+---------+---------+---------+----C1      
            5.0      10.0      15.0      20.0      25.0      30.0
 


================================================
NEW BLOG ver 11

MTB > Read "G:\DRR95\95ED257\WEBARCH\PROGRAM.DAT" c1-c2.
Entering data from file: G:\DRR95\95ED257\WEBARCH\PROGRAM.DAT
     25 rows read.
MTB > describe c1-c2

Descriptive Statistics


Variable        N     Mean   Median  Tr Mean    StDev  SE Mean
C1             25    16.88    18.00    16.78     9.08     1.82
C2             25    0.440    0.000    0.435    0.507    0.101

Variable      Min      Max       Q1       Q3
C1           4.00    32.00     8.50    24.50
C2          0.000    1.000    0.000    1.000

MTB > blogist c2 = c1

Binary Logistic Regression
Response  Information

Variable  Value          Count
C2        1                 11
          0                 14
          Total             25

Logistic Regression Table
                                                   Odds        95% CI
Predictor       Coef      StDev        Z     P    Ratio    Lower    Upper
Constant      -3.060      1.259    -2.43 0.015
C1           0.16149    0.06498     2.49 0.013     1.18     1.03     1.33

Log-Likelihood = -12.712
Test that all slopes are zero: G = 8.872, DF = 1, P-Value = 0.003

Goodness-of-Fit Tests

Method                Chi-Square    DF      P
Pearson                   19.623    17  0.294
Deviance                  19.879    17  0.280
Hosmer-Lemeshow            5.946     8  0.653

Measures of Association:
(Between the Response Variable and Predicted Probabilities)
Pairs           Number  Percent     Summary Measures
Concordant         127    82.5%     Somers D                0.66
Discordant          25    16.2%     Goodman-Kruskal Gamma   0.67
Ties                 2     1.3%     Kendalls Tau-a          0.34
Total              154   100.0%

MTB > blogist c2 = c1;
SUBC> brief 3.

Binary Logistic Regression


Response  Information

Variable  Value          Count
C2        1                 11
          0                 14
          Total             25

Factor Information
Logistic Regression Table
                                                   Odds        95% CI
Predictor       Coef      StDev        Z     P    Ratio    Lower    Upper
Constant      -3.060      1.259    -2.43 0.015
C1           0.16149    0.06498     2.49 0.013     1.18     1.03     1.33

Log-Likelihood = -12.712
Test that all slopes are zero: G = 8.872, DF = 1, P-Value = 0.003

Goodness-of-Fit Tests

Method                Chi-Square    DF      P
Pearson                   19.623    17  0.294
Deviance                  19.879    17  0.280
Hosmer-Lemeshow            5.946     8  0.653
Brown:
General Alternative        0.134     2  0.935
Symmetric Alternative      0.086     1  0.769

Measures of Association:
(Between the Response Variable and Predicted Probabilities)

Pairs           Number  Percent     Summary Measures
Concordant         127    82.5%     Somers D                0.66
Discordant          25    16.2%     Goodman-Kruskal Gamma   0.67
Ties                 2     1.3%     Kendalls Tau-a          0.34
Total              154   100.0%

MTB > 
