Tatsuoka's results:       Proc IML results:
Eigenvalues -               1.080548   .324078      1.0805177  0.3241748

Eigenvectors -                 v1       v2              v1        v2
                             .3524    .9145        0.0060257   0.0147046
                            -.7331    .1960       -0.01254     0.0031524
                             .5818   -.3540        0.0099517  -0.005692

Chi-square -                243.19    67.37       243.21749    67.389473
degrees of freedom -          6         2             6           2

Standardized Coefficients -   v*1      v*2           v*1         v*2
                             22.22    57.6         0.379566     0.976084
                            -48.7     13.0        -0.789904     0.2092569
                             30.1    -18.3         0.6268675   -0.377862

Sturcture Matrix -            F1       F2              F1         F2
                            .3927    .9124         0.3216749    0.9369232
                           -.8577    .2387        -0.765185     0.2669573
                            .6012   -.2655         0.4674273   -0.258791

-------------------------------------------------------------------------------
proc iml;
start;
reset noname;
w = {3967.8301 351.6142 76.6342,
351.6142 4406.2517 235.4365,
76.6342 235.4365 2683.3164};
b = {1572.7441 -773.0506 273.6214,
-773.0506 2889.3193 -1405.9955,
273.6214 -1405.9955 691.6068};
upper = root(w);
lower = t(upper);
a = inv(lower)*b*inv(upper);
call eigen(l,uv,a);
v = inv(upper)*uv;
print "Raw Eigenvalues", l;
l = l(|{1 2}|);
print "Trimmed Eigenvalues", l;
d = diag(l);
v = v(|{1 2 3},{1 2}|);
print "Eigenvectors",v;
/* Canonical Correlations */
l1 = l(|1|);
l2 = l(|2|);
rc1 = sqrt(l1/(1+l1));
rc2 = sqrt(l2/(1+l2));
print "Canonical Correlations";
print rc1;
print rc2;
/* tests of significance */
n =244;
k = 3;
p = nrow(w);
t1 = 1 - rc1*rc1;
t2 = 1 - rc2*rc2;
lambda1 = t1*t2;
lambda2 = t2;
c1 = (n-1-(p+k)/2);
v1 = -c1*log(lambda1);
v2 = -c1*log(lambda2);
print "Chi-square";
print v1;
print v2;
df1 = p*(k-1);
df2 = (p-1)*(k-2);
print "degrees of freedom";
print df1;
print df2;
/* end tests of significance */
t1 = sqrt(diag(w));
t1 = t1(|{1 2},{1 2}|);
r = v*t1;
print "Standardized Weights",r;
m1 = t(v)*w*v;
m2 = inv(sqrt(diag(m1)));
m3 = inv(sqrt(diag(w)));
a = m3*w*v*m2;
print "Discriminant Structure Matrix",a;
finish;
run;

-------------------------------------------------------------------------------

                                Raw Eigenvalues
                                   1.0805177
                                   0.3241748
                                   -0.000067

                              Trimmed Eigenvalues
                                   1.0805177
                                   0.3241748

                                 Eigenvectors
                              0.0060257 0.0147046
                               -0.01254 0.0031524
                              0.0099517 -0.005692

                            Canonical Correlations
                                   0.7206597
                                   0.4947855

                                  Chi-square
                                   243.21749
                                   67.389473

                              degrees of freedom
                                       6
                                       2

                             Standardized Weights
                               0.379566  0.976084
                              -0.789904 0.2092569
                              0.6268675 -0.377862
                           
                         Discriminant Structure Matrix
                              0.3216749 0.9369232
                              -0.765185 0.2669573
                              0.4674273 -0.258791