 MTB > read 'e:\cpy\corr.dat' c1-c2
      28 ROWS READ
 
  ROW    C1    C2
 
    1    47    33
    2    54    49
    3    48    40
    4    47    44
   .  .  .
 
 MTB > brief 3
 
 MTB > regress c2 1 c1 c3 c4;
 SUBC> residuals c5.
 
 The regression equation is
 C2 = - 7.4 + 0.956 C1
 
 Predictor       Coef       Stdev    t-ratio        p
 Constant       -7.40       10.07      -0.73    0.469
 C1            0.9560      0.2065       4.63    0.000
 
 s = 4.557       R-sq = 45.2%     R-sq(adj) = 43.1%
 
 Analysis of Variance
 
 SOURCE       DF          SS          MS         F        p
 Regression    1      444.94      444.94     21.42    0.000
 Error        26      540.02       20.77
 Total        27      984.96
 
 Obs.      C1        C2       Fit Stdev.Fit  Residual   St.Resid
   1     47.0    33.000    37.533     0.920    -4.533     -1.02  
   2     54.0    49.000    44.225     1.414     4.775      1.10  
   3     48.0    40.000    38.489     0.869     1.511      0.34  
   4     47.0    44.000    37.533     0.920     6.467      1.45  
   5     50.0    48.000    40.401     0.910     7.599      1.70  
   6     45.0    36.000    35.621     1.134     0.379      0.09  
   7     50.0    35.000    40.401     0.910    -5.401     -1.21  
   8     56.0    50.000    46.137     1.760     3.863      0.92  
   9     54.0    46.000    44.225     1.414     1.775      0.41  
  10     48.0    37.000    38.489     0.869    -1.489     -0.33  
  11     53.0    40.000    43.269     1.256    -3.269     -0.75  
  12     47.0    39.000    37.533     0.920     1.467      0.33  
  13     38.0    32.000    28.930     2.347     3.070      0.79 X
  14     47.0    42.000    37.533     0.920     4.467      1.00  
  15     48.0    39.000    38.489     0.869     0.511      0.11  
  16     49.0    37.000    39.445     0.866    -2.445     -0.55  
  17     53.0    42.000    43.269     1.256    -1.269     -0.29  
  18     53.0    40.000    43.269     1.256    -3.269     -0.75  
  19     49.0    40.000    39.445     0.866     0.555      0.12  
  20     52.0    47.000    42.313     1.115     4.687      1.06  
  21     40.0    37.000    30.842     1.969     6.158      1.50  
  22     48.0    34.000    38.489     0.869    -4.489     -1.00  
  23     43.0    21.000    33.710     1.437   -12.710     -2.94R 
  24     50.0    40.000    40.401     0.910    -0.401     -0.09  
  25     49.0    34.000    39.445     0.866    -5.445     -1.22  
  26     52.0    39.000    42.313     1.115    -3.313     -0.75  
  27     48.0    38.000    38.489     0.869    -0.489     -0.11  
  28     42.0    34.000    32.754     1.608     1.246      0.29  
 
 R denotes an obs. with a large st. resid.
 X denotes an obs. whose X value gives it large influence.
Note: create "standardized residuals" and put in c6. 
 MTB > let c6 = c5/4.557
Note heres all three kinds of residuals: studentized, "raw", standardized
 MTB > print c3 c5 c6
 
  ROW        C3        C5        C6
 
    1  -1.01567   -4.5335  -0.99483
    2   1.10204    4.7746   1.04776
    3   0.33765    1.5106   0.33148
    4   1.44876    6.4665   1.41904
    5   1.70160    7.5986   1.66745
    6   0.08575    0.3785   0.08306
    7  -1.20957   -5.4014  -1.18530
    8   0.91880    3.8627   0.84764
    9   0.40961    1.7746   0.38943
   10  -0.33293   -1.4894  -0.32685
   11  -0.74629   -3.2694  -0.71744
   12   0.32856    1.4665   0.32182
   13   0.78598    3.0704   0.67378
   14   1.00068    4.4665   0.98015
   15   0.11412    0.5106   0.11204
   16  -0.54653   -2.4454  -0.53663
   17  -0.28976   -1.2694  -0.27855
   18  -0.74629   -3.2694  -0.71744
   19   0.12394    0.5546   0.12170
   20   1.06058    4.6866   1.02844
   21   1.49832    6.1585   1.35143
   22  -1.00351   -4.4894  -0.98517
   23  -2.93874  -12.7095  -2.78901
   24  -0.08989   -0.4014  -0.08809
   25  -1.21701   -5.4454  -1.19496
   26  -0.74982   -3.3134  -0.72710
   27  -0.10940   -0.4894  -0.10740
   28   0.29229    1.2465   0.27353
