data shoes;
  input sales subject a b;
  label subject = 'Test Market'
              a = 'Campaign'
	      b = 'Time';
cards;
    958  1  1  1
   1005  2  1  1
    351  3  1  1
    549  4  1  1
    730  5  1  1
   1047  1  1  2
   1122  2  1  2
    436  3  1  2
    632  4  1  2
    784  5  1  2
    933  1  1  3
    986  2  1  3
    339  3  1  3
    512  4  1  3
    707  5  1  3
    780  1  2  1
    229  2  2  1
    883  3  2  1
    624  4  2  1
    375  5  2  1
    897  1  2  2
    275  2  2  2
    964  3  2  2
    695  4  2  2
    436  5  2  2
    718  1  2  3
    202  2  2  3
    817  3  2  3
    599  4  2  3
    351  5  2  3
;
run;


data plot;
  set shoes;
  if subject=1 then s1=sales;
  if subject=2 then s2=sales;
  if subject=3 then s3=sales;
  if subject=4 then s4=sales;
  if subject=5 then s5=sales;
run;
symbol1 c=blue v=dot i=join;
symbol2 c=blue v=dot i=join;
symbol3 c=blue v=dot i=join;
symbol4 c=blue v=dot i=join;
symbol5 c=blue v=dot i=join;
axis1 label=(a=90 'Sales') offset=(1, 2) order=(0 to 1200 by 300);
proc gplot data=plot;
  by a;
  plot (s1 s2 s3 s4 s5)*b / overlay vaxis=axis1;
run;

proc glm data=shoes;
  class A B subject;
  model sales = A subject(A) B A*B;
  random subject(A);
  lsmeans A;
  lsmeans B / pdiff cl adjust=tukey alpha=.01;
  test h=A e=subject(A); 
* Based on expected mean squares, correct denominator MS for test about A is MSS(A);
* See page 1142-1143 for details;
run;
quit;