*The program below is on the first SAS handout;

options ls = 70;
data clover;
input strain $ nitrogen @@;
cards;
3dok1  19.4  3dok1  32.6  3dok1  27.0  3dok1  32.1  3dok1  33.0
3dok5  17.7  3dok5  24.8  3dok5  27.9  3dok5  25.2  3dok5  24.3
3dok4  17.0  3dok4  19.4  3dok4   9.1  3dok4  11.9  3dok4  15.8
3dok7  20.7  3dok7  21.0  3dok7  20.5  3dok7  18.8  3dok7  18.6
3dok13 14.3  3dok13 14.4  3dok13 11.8  3dok13 11.6  3dok13 14.2
compos 17.3  compos 19.4  compos 19.1  compos 16.9  compos 20.8
;
proc anova;
  class strain;
  model nitrogen=strain;
  means strain / duncan waller;
  means strain / lsd tukey cldiff;
proc print;
run;

*Math 583 HW3.2, from SAS User's Guide: Statistics (1985, p. 126-129);

*Uses proc glm to get residual and response plots;

options ls = 70;
data clover;
input strain $ nitrogen @@;
cards;
3dok1  19.4  3dok1  32.6  3dok1  27.0  3dok1  32.1  3dok1  33.0
3dok5  17.7  3dok5  24.8  3dok5  27.9  3dok5  25.2  3dok5  24.3
3dok4  17.0  3dok4  19.4  3dok4   9.1  3dok4  11.9  3dok4  15.8
3dok7  20.7  3dok7  21.0  3dok7  20.5  3dok7  18.8  3dok7  18.6
3dok13 14.3  3dok13 14.4  3dok13 11.8  3dok13 11.6  3dok13 14.2
compos 17.3  compos 19.4  compos 19.1  compos 16.9  compos 20.8
;
proc glm;
  class strain;
  model nitrogen=strain;
  output out =a p = pred r = resid;
proc plot data = a;
  plot resid*pred;
  plot nitrogen*pred;
run;

*The program below is for the two way Anova handout for SAS handout;
*Also for HW5 problem2;
*Data from Montgomery (1984, p. 198); 

options ls = 70;
data voltage;
input material $ temp $ mvoltage @@;
cards;
1  50  130 1  50  155  1  50   74 1  50  180 
1  65   34 1  65   40  1  65   80 1  65   75  
1  80   20 1  80   70  1  80   82 1  80   58   
2  50  150 2  50  188  2  50  159 2  50  126 
2  65  136 2  65  122  2  65  106 2  65  115  
2  80   25 2  80   70  2  80   58 2  80   45   
3  50  138 3  50  110  3  50  168 3  50  160 
3  65  174 3  65  120  3  65  150 3  65  139  
3  80   96 3  80  104  3  80   82 3  80   60   
;
proc glm;
  class material temp;
  model mvoltage =material|temp;
  output out =a p = pred r = resid;
proc plot data = a;
  plot resid*pred;
  plot mvoltage*pred;
proc means data = voltage nway;
class material temp;
var mvoltage;
output out=means mean=ymn;
symbol1 v=square color=black i=join;
symbol2 v=circle color=black i=join;
symbol3 v=triangle color=black i=join;
proc gplot data=means;
title"Interaction Plot";
plot ymn * material = temp;
proc gplot data=means;
title"Interaction Plot";
plot ymn * temp = material;
run;

*The HW5 problem 3 program below is for a one way block design;
*Data from Box, Hunter and Hunter (2005, p. 146); 

options ls = 70;
data penicillan;
input block $ treat $ yield;
cards;
1  1  89
1  2  88
1  3  97
1  4  94
2  1  84
2  2  77
2  3  92
2  4  79
3  1  81
3  2  87
3  3  87
3  4  85
4  1  87
4  2  92
4  3  89
4  4  84
5  1  79
5  2  81
5  3  80
5  4  88
;
proc glm;
  class block treat;
  model yield =block treat;
  output out =a p = pred r = resid;
proc plot data = a;
  plot resid*pred;
  plot yield*pred;
run;


*The HW6 problem 1 program below is for a latin squares design;
*Data from Box, Hunter and Hunter (2005, p. 157-160); 

options ls = 70;
data auto;
input rblocks $ cblocks $ additives $ emmissions;
cards;
1  1  1  19
1  2  2  24
1  3  4  23
1  4  3  26
2  1  4  23
2  2  3  24
2  3  1  19
2  4  2  30
3  1  2  15
3  2  4  14
3  3  3  15
3  4  1  16
4  1  3  19
4  2  1  18
4  3  2  19
4  4  4  16
;
proc glm;
  class rblocks cblocks additives;
  model emmissions = rblocks cblocks additives;
  output out =a p = pred r = resid;
**the following commands can be used;
**to make a response and residual plot;
*proc plot data = a;
 * plot resid*pred;
 * plot emmissions*pred;
run;

**HW7 problem 4;
*Box, Hunter and Hunter (2005, p. 183) pilot plant data;
*SAS will treat a -1 like a 0;
*T = temp, C = concentration, K = catalyst;

options ls = 70;
data pilotplant;
input T $ C $ K $ yield;
cards;
-1  -1  -1  59
 1  -1  -1  74
-1   1  -1  50
 1   1  -1  69
-1  -1   1  50
 1  -1   1  81
-1   1   1  46
 1   1   1  79
-1  -1  -1  61
 1  -1  -1  70
-1   1  -1  58
 1   1  -1  67
-1  -1   1  54
 1  -1   1  85
-1   1   1  44
 1   1   1  81
;
proc glm;
  class T C K;
  model yield = T|C|K;
  output out =a p = pred r = resid;
proc plot data = a;
plot resid*pred;
plot yield*pred;
run;

*HW8 problem 1;
*this data is actually for Minitab;
*A   B   C AB  AC  BC ABC yield;

-1  -1  -1  1   1   1  -1 59
 1  -1  -1 -1  -1   1   1 74
-1   1  -1 -1   1  -1   1 50
 1   1  -1  1  -1  -1  -1 69
-1  -1   1  1  -1  -1   1 50
 1  -1   1 -1   1  -1  -1 81
-1   1   1 -1  -1   1  -1 46
 1   1   1  1   1   1   1 79
-1  -1  -1  1   1   1  -1 61
 1  -1  -1 -1  -1   1   1 70
-1   1  -1 -1   1  -1   1 58
 1   1  -1  1  -1  -1  -1 67
-1  -1   1  1  -1  -1   1 54
 1  -1   1 -1   1  -1  -1 85
-1   1   1 -1  -1   1  -1 44
 1   1   1  1   1   1   1 81

#HW9.1 for R

#Box, Hunter and Hunter (2005, p. 183 ) pilot plant data
mn <- 1 + 0*1:16
x1 <- rep(c(-1,-1,1,1),4)
x2 <- rep(c(-1,-1,-1,-1,1,1,1,1),2)
x3 <- mn
x3[1:8] <- -1
x12 <- x1*x2
x13 <- x1*x3
x23 <- x2*x3
x123 <- x12*x3
x<-cbind(x1,x2,x3,x12,x13,x23,x123)
y<-c(59,61,74,70,50,58,69,67,50,54,81,85,46,44,79,81)
out<-lsfit(x,y)
ls.print(out)
out2 <- aov(y~x1*x2*x3)
summary(out2)
summary.lm(out2)
rm(mn,x1,x2,x3,x12,x13,x23,x123,x,y,out,out2)

##lab

#Ledolter and Swersey (2007, p. 80) cracked pots $2^4$ data
mns <- c(14,16,8,22,19,37,20,38,1,8,4,10,12,30,13,30)
twofourth(mns)

##HW 11 4

*Box, Hunter and Hunter (2005, p. 336) steel split plot data;
*Need to divide MS heat by MS wplots to get the correct
*F statistic to test heat;
options ls = 70;
data steel;
input coating $ heat $ repl $ resistance wplots $;
cards;
       1    1    1         67      1
       1    2    1         65      2
       1    3    1        155      3
       2    1    1         73      1
       2    2    1         91      2
       2    3    1        127      3
       3    1    1         83      1
       3    2    1         87      2
       3    3    1        147      3
       4    1    1         89      1
       4    2    1         86      2
       4    3    1        212      3
       1    1    2         33      4
       1    2    2        140      5
       1    3    2        108      6
       2    1    2          8      4
       2    2    2        142      5
       2    3    2        100      6
       3    1    2         46      4
       3    2    2        121      5
       3    3    2         90      6
       4    1    2         54      4
       4    2    2        150      5
       4    3    2        153      6
;
proc glm;
  class coating heat repl wplots;
  model resistance =  heat wplots coating heat*coating;
  test H = heat E = wplots;
run;


**HW 11 5;
*SAS User's Guide: Statistics (1985, p. 131-132);
*Block 1 A 2 B 3 and 142 makes block = 1, A = 4, B = 2;
data split;
input Block 1 A 2 B 3 Response;
cards;
142 40.0
141 39.5
112 37.9
111 35.4 
121 36.7
122 38.2
132 36.4
131 34.8
221 42.7
222 41.6
212 40.3
211 41.6
241 44.5
242 47.6
231 43.6
232 42.8
;
**Use proc print to see what the data looks like;
*proc print;
proc anova;
class Block A B;
model Response = Block A Block*A B A*B;
test H=A E=Block*A;
run;