ANOVA Codings
This set of examples illustrates several different ways to code the same
two-way ANOVA model. Figure 65.33 displays the
input data set.
title 'Two-way ANOVA Models';
data x;
input a b @@;
do i = 1 to 2; input y @@; output; end;
drop i;
datalines;
1 1 16 14 1 2 15 13
2 1 1 9 2 2 12 20
3 1 14 8 3 2 18 20
;
proc print label;
run;
Obs |
a |
b |
y |
1 |
1 |
1 |
16 |
2 |
1 |
1 |
14 |
3 |
1 |
2 |
15 |
4 |
1 |
2 |
13 |
5 |
2 |
1 |
1 |
6 |
2 |
1 |
9 |
7 |
2 |
2 |
12 |
8 |
2 |
2 |
20 |
9 |
3 |
1 |
14 |
10 |
3 |
1 |
8 |
11 |
3 |
2 |
18 |
12 |
3 |
2 |
20 |
|
Figure 65.33: Input Data Set
The following statements fit a cell-means model. See Figure 65.34 and
Figure 65.35.
proc transreg data=x ss2 short;
title2 'Cell-Means Model';
model identity(y) = class(a * b / zero=none);
output replace;
run;
proc print label;
run;
Two-way ANOVA Models |
Cell-Means Model |
Identity(y) |
Algorithm converged. |
The TRANSREG Procedure Hypothesis Tests for Identity(y) |
Univariate ANOVA Table Based on the Usual Degrees of Freedom |
Source |
DF |
Sum of Squares |
Mean Square |
F Value |
Pr > F |
Model |
5 |
234.6667 |
46.93333 |
3.20 |
0.0946 |
Error |
6 |
88.0000 |
14.66667 |
|
|
Corrected Total |
11 |
322.6667 |
|
|
|
Root MSE |
3.82971 |
R-Square |
0.7273 |
Dependent Mean |
13.33333 |
Adj R-Sq |
0.5000 |
Coeff Var |
28.72281 |
|
|
Univariate Regression Table Based on the Usual Degrees of Freedom |
Variable |
DF |
Coefficient |
Type II Sum of Squares |
Mean Square |
F Value |
Pr > F |
Label |
Class.a1b1 |
1 |
15.0000000 |
450.000 |
450.000 |
30.68 |
0.0015 |
a 1 * b 1 |
Class.a1b2 |
1 |
14.0000000 |
392.000 |
392.000 |
26.73 |
0.0021 |
a 1 * b 2 |
Class.a2b1 |
1 |
5.0000000 |
50.000 |
50.000 |
3.41 |
0.1144 |
a 2 * b 1 |
Class.a2b2 |
1 |
16.0000000 |
512.000 |
512.000 |
34.91 |
0.0010 |
a 2 * b 2 |
Class.a3b1 |
1 |
11.0000000 |
242.000 |
242.000 |
16.50 |
0.0066 |
a 3 * b 1 |
Class.a3b2 |
1 |
19.0000000 |
722.000 |
722.000 |
49.23 |
0.0004 |
a 3 * b 2 |
|
Figure 65.34: Cell-Means Model
The parameter estimates are
Two-way ANOVA Models |
Cell-Means Model |
Obs |
_TYPE_ |
_NAME_ |
y |
Intercept |
a 1 * b 1 |
a 1 * b 2 |
a 2 * b 1 |
a 2 * b 2 |
a 3 * b 1 |
a 3 * b 2 |
a |
b |
1 |
SCORE |
ROW1 |
16 |
. |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
SCORE |
ROW2 |
14 |
. |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
SCORE |
ROW3 |
15 |
. |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |
SCORE |
ROW4 |
13 |
. |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
SCORE |
ROW5 |
1 |
. |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
1 |
6 |
SCORE |
ROW6 |
9 |
. |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
1 |
7 |
SCORE |
ROW7 |
12 |
. |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
2 |
8 |
SCORE |
ROW8 |
20 |
. |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
2 |
9 |
SCORE |
ROW9 |
14 |
. |
0 |
0 |
0 |
0 |
1 |
0 |
3 |
1 |
10 |
SCORE |
ROW10 |
8 |
. |
0 |
0 |
0 |
0 |
1 |
0 |
3 |
1 |
11 |
SCORE |
ROW11 |
18 |
. |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
2 |
12 |
SCORE |
ROW12 |
20 |
. |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
2 |
|
Figure 65.35: Cell-Means Model, Design Matrix
The following statements fit a reference cell model. The default reference level
is the last cell (3,2). See Figure 65.36 and Figure 65.37.
proc transreg data=x ss2 short;
title2 'Reference Cell Model, (3,2) Reference Cell';
model identity(y) = class(a | b);
output replace;
run;
proc print label;
run;
Two-way ANOVA Models |
Reference Cell Model, (3,2) Reference Cell |
Identity(y) |
Algorithm converged. |
The TRANSREG Procedure Hypothesis Tests for Identity(y) |
Univariate ANOVA Table Based on the Usual Degrees of Freedom |
Source |
DF |
Sum of Squares |
Mean Square |
F Value |
Pr > F |
Model |
5 |
234.6667 |
46.93333 |
3.20 |
0.0946 |
Error |
6 |
88.0000 |
14.66667 |
|
|
Corrected Total |
11 |
322.6667 |
|
|
|
Root MSE |
3.82971 |
R-Square |
0.7273 |
Dependent Mean |
13.33333 |
Adj R-Sq |
0.5000 |
Coeff Var |
28.72281 |
|
|
Univariate Regression Table Based on the Usual Degrees of Freedom |
Variable |
DF |
Coefficient |
Type II Sum of Squares |
Mean Square |
F Value |
Pr > F |
Label |
Intercept |
1 |
19.0000000 |
722.000 |
722.000 |
49.23 |
0.0004 |
Intercept |
Class.a1 |
1 |
-5.0000000 |
25.000 |
25.000 |
1.70 |
0.2395 |
a 1 |
Class.a2 |
1 |
-3.0000000 |
9.000 |
9.000 |
0.61 |
0.4632 |
a 2 |
Class.b1 |
1 |
-8.0000000 |
64.000 |
64.000 |
4.36 |
0.0817 |
b 1 |
Class.a1b1 |
1 |
9.0000000 |
40.500 |
40.500 |
2.76 |
0.1476 |
a 1 * b 1 |
Class.a2b1 |
1 |
-3.0000000 |
4.500 |
4.500 |
0.31 |
0.5997 |
a 2 * b 1 |
|
Figure 65.36: Reference Cell Model, (3,2) Reference Cell
The parameter estimates are
The structural zeros are
Two-way ANOVA Models |
Reference Cell Model, (3,2) Reference Cell |
Obs |
_TYPE_ |
_NAME_ |
y |
Intercept |
a 1 |
a 2 |
b 1 |
a 1 * b 1 |
a 2 * b 1 |
a |
b |
1 |
SCORE |
ROW1 |
16 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
2 |
SCORE |
ROW2 |
14 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
3 |
SCORE |
ROW3 |
15 |
1 |
1 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |
SCORE |
ROW4 |
13 |
1 |
1 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
SCORE |
ROW5 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
2 |
1 |
6 |
SCORE |
ROW6 |
9 |
1 |
0 |
1 |
1 |
0 |
1 |
2 |
1 |
7 |
SCORE |
ROW7 |
12 |
1 |
0 |
1 |
0 |
0 |
0 |
2 |
2 |
8 |
SCORE |
ROW8 |
20 |
1 |
0 |
1 |
0 |
0 |
0 |
2 |
2 |
9 |
SCORE |
ROW9 |
14 |
1 |
0 |
0 |
1 |
0 |
0 |
3 |
1 |
10 |
SCORE |
ROW10 |
8 |
1 |
0 |
0 |
1 |
0 |
0 |
3 |
1 |
11 |
SCORE |
ROW11 |
18 |
1 |
0 |
0 |
0 |
0 |
0 |
3 |
2 |
12 |
SCORE |
ROW12 |
20 |
1 |
0 |
0 |
0 |
0 |
0 |
3 |
2 |
|
Figure 65.37: Reference Cell Model, (3,2) Reference Cell, Design Matrix
The following statements fit a reference cell model, but this time the reference level
is the first cell (1,1). See Figure 65.38 through
Figure 65.39.
proc transreg data=x ss2 short;
title2 'Reference Cell Model, (1,1) Reference Cell';
model identity(y) = class(a | b / zero=first);
output replace;
run;
proc print label;
run;
Two-way ANOVA Models |
Reference Cell Model, (1,1) Reference Cell |
Identity(y) |
Algorithm converged. |
The TRANSREG Procedure Hypothesis Tests for Identity(y) |
Univariate ANOVA Table Based on the Usual Degrees of Freedom |
Source |
DF |
Sum of Squares |
Mean Square |
F Value |
Pr > F |
Model |
5 |
234.6667 |
46.93333 |
3.20 |
0.0946 |
Error |
6 |
88.0000 |
14.66667 |
|
|
Corrected Total |
11 |
322.6667 |
|
|
|
Root MSE |
3.82971 |
R-Square |
0.7273 |
Dependent Mean |
13.33333 |
Adj R-Sq |
0.5000 |
Coeff Var |
28.72281 |
|
|
Univariate Regression Table Based on the Usual Degrees of Freedom |
Variable |
DF |
Coefficient |
Type II Sum of Squares |
Mean Square |
F Value |
Pr > F |
Label |
Intercept |
1 |
15.000000 |
450.000 |
450.000 |
30.68 |
0.0015 |
Intercept |
Class.a2 |
1 |
-10.000000 |
100.000 |
100.000 |
6.82 |
0.0401 |
a 2 |
Class.a3 |
1 |
-4.000000 |
16.000 |
16.000 |
1.09 |
0.3365 |
a 3 |
Class.b2 |
1 |
-1.000000 |
1.000 |
1.000 |
0.07 |
0.8027 |
b 2 |
Class.a2b2 |
1 |
12.000000 |
72.000 |
72.000 |
4.91 |
0.0686 |
a 2 * b 2 |
Class.a3b2 |
1 |
9.000000 |
40.500 |
40.500 |
2.76 |
0.1476 |
a 3 * b 2 |
|
Figure 65.38: Reference Cell Model, (1,1) Reference Cell
The parameter estimates are
The structural zeros are
Two-way ANOVA Models |
Reference Cell Model, (1,1) Reference Cell |
Obs |
_TYPE_ |
_NAME_ |
y |
Intercept |
a 2 |
a 3 |
b 2 |
a 2 * b 2 |
a 3 * b 2 |
a |
b |
1 |
SCORE |
ROW1 |
16 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
SCORE |
ROW2 |
14 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
SCORE |
ROW3 |
15 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
2 |
4 |
SCORE |
ROW4 |
13 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
2 |
5 |
SCORE |
ROW5 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
2 |
1 |
6 |
SCORE |
ROW6 |
9 |
1 |
1 |
0 |
0 |
0 |
0 |
2 |
1 |
7 |
SCORE |
ROW7 |
12 |
1 |
1 |
0 |
1 |
1 |
0 |
2 |
2 |
8 |
SCORE |
ROW8 |
20 |
1 |
1 |
0 |
1 |
1 |
0 |
2 |
2 |
9 |
SCORE |
ROW9 |
14 |
1 |
0 |
1 |
0 |
0 |
0 |
3 |
1 |
10 |
SCORE |
ROW10 |
8 |
1 |
0 |
1 |
0 |
0 |
0 |
3 |
1 |
11 |
SCORE |
ROW11 |
18 |
1 |
0 |
1 |
1 |
0 |
1 |
3 |
2 |
12 |
SCORE |
ROW12 |
20 |
1 |
0 |
1 |
1 |
0 |
1 |
3 |
2 |
|
Figure 65.39: Reference Cell Model, (1,1) Reference Cell, Design Matrix
The following statements fit a deviations-from-means model.
The default reference level
is the last cell (3,2). This coding is also called effects coding.
See Figure 65.40 and Figure 65.41.
proc transreg data=x ss2 short;
title2 'Deviations From Means, (3,2) Reference Cell';
model identity(y) = class(a | b / deviations);
output replace;
run;
proc print label;
run;
Two-way ANOVA Models |
Deviations From Means, (3,2) Reference Cell |
Identity(y) |
Algorithm converged. |
The TRANSREG Procedure Hypothesis Tests for Identity(y) |
Univariate ANOVA Table Based on the Usual Degrees of Freedom |
Source |
DF |
Sum of Squares |
Mean Square |
F Value |
Pr > F |
Model |
5 |
234.6667 |
46.93333 |
3.20 |
0.0946 |
Error |
6 |
88.0000 |
14.66667 |
|
|
Corrected Total |
11 |
322.6667 |
|
|
|
Root MSE |
3.82971 |
R-Square |
0.7273 |
Dependent Mean |
13.33333 |
Adj R-Sq |
0.5000 |
Coeff Var |
28.72281 |
|
|
Univariate Regression Table Based on the Usual Degrees of Freedom |
Variable |
DF |
Coefficient |
Type II Sum of Squares |
Mean Square |
F Value |
Pr > F |
Label |
Intercept |
1 |
13.3333333 |
2133.33 |
2133.33 |
145.45 |
<.0001 |
Intercept |
Class.a1 |
1 |
1.1666667 |
8.17 |
8.17 |
0.56 |
0.4837 |
a 1 |
Class.a2 |
1 |
-2.8333333 |
48.17 |
48.17 |
3.28 |
0.1199 |
a 2 |
Class.b1 |
1 |
-3.0000000 |
108.00 |
108.00 |
7.36 |
0.0349 |
b 1 |
Class.a1b1 |
1 |
3.5000000 |
73.50 |
73.50 |
5.01 |
0.0665 |
a 1 * b 1 |
Class.a2b1 |
1 |
-2.5000000 |
37.50 |
37.50 |
2.56 |
0.1609 |
a 2 * b 1 |
|
Figure 65.40: Deviations-From-Means Model, (3,2) Reference Cell
The parameter estimates are
The structural zeros are
Two-way ANOVA Models |
Deviations From Means, (3,2) Reference Cell |
Obs |
_TYPE_ |
_NAME_ |
y |
Intercept |
a 1 |
a 2 |
b 1 |
a 1 * b 1 |
a 2 * b 1 |
a |
b |
1 |
SCORE |
ROW1 |
16 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
2 |
SCORE |
ROW2 |
14 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
3 |
SCORE |
ROW3 |
15 |
1 |
1 |
0 |
-1 |
-1 |
0 |
1 |
2 |
4 |
SCORE |
ROW4 |
13 |
1 |
1 |
0 |
-1 |
-1 |
0 |
1 |
2 |
5 |
SCORE |
ROW5 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
2 |
1 |
6 |
SCORE |
ROW6 |
9 |
1 |
0 |
1 |
1 |
0 |
1 |
2 |
1 |
7 |
SCORE |
ROW7 |
12 |
1 |
0 |
1 |
-1 |
0 |
-1 |
2 |
2 |
8 |
SCORE |
ROW8 |
20 |
1 |
0 |
1 |
-1 |
0 |
-1 |
2 |
2 |
9 |
SCORE |
ROW9 |
14 |
1 |
-1 |
-1 |
1 |
-1 |
-1 |
3 |
1 |
10 |
SCORE |
ROW10 |
8 |
1 |
-1 |
-1 |
1 |
-1 |
-1 |
3 |
1 |
11 |
SCORE |
ROW11 |
18 |
1 |
-1 |
-1 |
-1 |
1 |
1 |
3 |
2 |
12 |
SCORE |
ROW12 |
20 |
1 |
-1 |
-1 |
-1 |
1 |
1 |
3 |
2 |
|
Figure 65.41: Deviations-From-Means Model, (3,2) Reference Cell, Design Matrix
The following statements fit a deviations-from-means model, but this time the
reference level is the first cell (1,1). This coding is also called
effects coding. See Figure 65.42 through
Figure 65.43.
proc transreg data=x ss2 short;
title2 'Deviations From Means, (1,1) Reference Cell';
model identity(y) = class(a | b / deviations zero=first);
output replace;
run;
proc print label;
run;
Two-way ANOVA Models |
Deviations From Means, (1,1) Reference Cell |
Identity(y) |
Algorithm converged. |
The TRANSREG Procedure Hypothesis Tests for Identity(y) |
Univariate ANOVA Table Based on the Usual Degrees of Freedom |
Source |
DF |
Sum of Squares |
Mean Square |
F Value |
Pr > F |
Model |
5 |
234.6667 |
46.93333 |
3.20 |
0.0946 |
Error |
6 |
88.0000 |
14.66667 |
|
|
Corrected Total |
11 |
322.6667 |
|
|
|
Root MSE |
3.82971 |
R-Square |
0.7273 |
Dependent Mean |
13.33333 |
Adj R-Sq |
0.5000 |
Coeff Var |
28.72281 |
|
|
Univariate Regression Table Based on the Usual Degrees of Freedom |
Variable |
DF |
Coefficient |
Type II Sum of Squares |
Mean Square |
F Value |
Pr > F |
Label |
Intercept |
1 |
13.3333333 |
2133.33 |
2133.33 |
145.45 |
<.0001 |
Intercept |
Class.a2 |
1 |
-2.8333333 |
48.17 |
48.17 |
3.28 |
0.1199 |
a 2 |
Class.a3 |
1 |
1.6666667 |
16.67 |
16.67 |
1.14 |
0.3274 |
a 3 |
Class.b2 |
1 |
3.0000000 |
108.00 |
108.00 |
7.36 |
0.0349 |
b 2 |
Class.a2b2 |
1 |
2.5000000 |
37.50 |
37.50 |
2.56 |
0.1609 |
a 2 * b 2 |
Class.a3b2 |
1 |
1.0000000 |
6.00 |
6.00 |
0.41 |
0.5461 |
a 3 * b 2 |
|
Figure 65.42: Deviations-From-Means Model, (1,1) Reference Cell
The parameter estimates are
The structural zeros are
Two-way ANOVA Models |
Deviations From Means, (1,1) Reference Cell |
Obs |
_TYPE_ |
_NAME_ |
y |
Intercept |
a 2 |
a 3 |
b 2 |
a 2 * b 2 |
a 3 * b 2 |
a |
b |
1 |
SCORE |
ROW1 |
16 |
1 |
-1 |
-1 |
-1 |
1 |
1 |
1 |
1 |
2 |
SCORE |
ROW2 |
14 |
1 |
-1 |
-1 |
-1 |
1 |
1 |
1 |
1 |
3 |
SCORE |
ROW3 |
15 |
1 |
-1 |
-1 |
1 |
-1 |
-1 |
1 |
2 |
4 |
SCORE |
ROW4 |
13 |
1 |
-1 |
-1 |
1 |
-1 |
-1 |
1 |
2 |
5 |
SCORE |
ROW5 |
1 |
1 |
1 |
0 |
-1 |
-1 |
0 |
2 |
1 |
6 |
SCORE |
ROW6 |
9 |
1 |
1 |
0 |
-1 |
-1 |
0 |
2 |
1 |
7 |
SCORE |
ROW7 |
12 |
1 |
1 |
0 |
1 |
1 |
0 |
2 |
2 |
8 |
SCORE |
ROW8 |
20 |
1 |
1 |
0 |
1 |
1 |
0 |
2 |
2 |
9 |
SCORE |
ROW9 |
14 |
1 |
0 |
1 |
-1 |
0 |
-1 |
3 |
1 |
10 |
SCORE |
ROW10 |
8 |
1 |
0 |
1 |
-1 |
0 |
-1 |
3 |
1 |
11 |
SCORE |
ROW11 |
18 |
1 |
0 |
1 |
1 |
0 |
1 |
3 |
2 |
12 |
SCORE |
ROW12 |
20 |
1 |
0 |
1 |
1 |
0 |
1 |
3 |
2 |
|
Figure 65.43: Deviations-From-Means Model, (1,1) Reference Cell, Design Matrix
The following statements fit a less-than-full-rank model. The parameter estimates
are constrained to sum to zero within each effect.
See Figure 65.44 and Figure 65.45.
proc transreg data=x ss2 short;
title2 'Less Than Full Rank Model';
model identity(y) = class(a | b / zero=sum);
output replace;
run;
proc print label;
run;
Two-way ANOVA Models |
Less Than Full Rank Model |
Identity(y) |
Algorithm converged. |
The TRANSREG Procedure Hypothesis Tests for Identity(y) |
Univariate ANOVA Table Based on the Usual Degrees of Freedom |
Source |
DF |
Sum of Squares |
Mean Square |
F Value |
Pr > F |
Model |
5 |
234.6667 |
46.93333 |
3.20 |
0.0946 |
Error |
6 |
88.0000 |
14.66667 |
|
|
Corrected Total |
11 |
322.6667 |
|
|
|
Root MSE |
3.82971 |
R-Square |
0.7273 |
Dependent Mean |
13.33333 |
Adj R-Sq |
0.5000 |
Coeff Var |
28.72281 |
|
|
Univariate Regression Table Based on the Usual Degrees of Freedom |
Variable |
DF |
Coefficient |
Type II Sum of Squares |
Mean Square |
F Value |
Pr > F |
Label |
Intercept |
1 |
13.3333333 |
2133.33 |
2133.33 |
145.45 |
<.0001 |
Intercept |
Class.a1 |
1 |
1.1666667 |
8.17 |
8.17 |
0.56 |
0.4837 |
a 1 |
Class.a2 |
1 |
-2.8333333 |
48.17 |
48.17 |
3.28 |
0.1199 |
a 2 |
Class.a3 |
1 |
1.6666667 |
16.67 |
16.67 |
1.14 |
0.3274 |
a 3 |
Class.b1 |
1 |
-3.0000000 |
108.00 |
108.00 |
7.36 |
0.0349 |
b 1 |
Class.b2 |
1 |
3.0000000 |
108.00 |
108.00 |
7.36 |
0.0349 |
b 2 |
Class.a1b1 |
1 |
3.5000000 |
73.50 |
73.50 |
5.01 |
0.0665 |
a 1 * b 1 |
Class.a1b2 |
1 |
-3.5000000 |
73.50 |
73.50 |
5.01 |
0.0665 |
a 1 * b 2 |
Class.a2b1 |
1 |
-2.5000000 |
37.50 |
37.50 |
2.56 |
0.1609 |
a 2 * b 1 |
Class.a2b2 |
1 |
2.5000000 |
37.50 |
37.50 |
2.56 |
0.1609 |
a 2 * b 2 |
Class.a3b1 |
1 |
-1.0000000 |
6.00 |
6.00 |
0.41 |
0.5461 |
a 3 * b 1 |
Class.a3b2 |
1 |
1.0000000 |
6.00 |
6.00 |
0.41 |
0.5461 |
a 3 * b 2 |
The sum of the regression table DF's, minus one for the intercept, will be greater than the model df when there are ZERO=SUM constraints. |
|
Figure 65.44: Less-Than-Full-Rank Model
The parameter estimates are
The constraints are
Two-way ANOVA Models |
Less Than Full Rank Model |
Obs |
_TYPE_ |
_NAME_ |
y |
Intercept |
a 1 |
a 2 |
a 3 |
b 1 |
b 2 |
a 1 * b 1 |
a 1 * b 2 |
a 2 * b 1 |
a 2 * b 2 |
a 3 * b 1 |
a 3 * b 2 |
a |
b |
1 |
SCORE |
ROW1 |
16 |
1 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
SCORE |
ROW2 |
14 |
1 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
SCORE |
ROW3 |
15 |
1 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |
SCORE |
ROW4 |
13 |
1 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
SCORE |
ROW5 |
1 |
1 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
1 |
6 |
SCORE |
ROW6 |
9 |
1 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
1 |
7 |
SCORE |
ROW7 |
12 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
2 |
8 |
SCORE |
ROW8 |
20 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
2 |
9 |
SCORE |
ROW9 |
14 |
1 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
3 |
1 |
10 |
SCORE |
ROW10 |
8 |
1 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
3 |
1 |
11 |
SCORE |
ROW11 |
18 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
2 |
12 |
SCORE |
ROW12 |
20 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
2 |
|
Figure 65.45: Less-Than-Full-Rank Model, Design Matrix
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.