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Completely Randomized Design
Reviews for later topics– Model parameterization
(estimability)– Contrasts (power analysis)
Analysis with contrasts– Orthogonal polynomial contrasts– Polynomial goodness-of-fit
Completely Randomized Design
Cell means model:
2,0~,
,,1,,,1,
Niid
njaiY
ijij
iijiij
Effects Model
Yij ˜ . i ij
Possible constraints : ii1
a
0 or a 0
ij independent, ij ~ N 0, 2
GLM for Effects Model
aa an
a
n
n
a
an
a
n
n
Y
Y
Y
Y
Y
Y
1
2
21
11
11
1
2
1
.
1
2
21
1
11
22
1 ~
1111
1111
0101
0101
0011
0011
CRD Contrasts
Balanced case (ni=n)
-A linear combination L has the form:
-A contrast is a linear combination with the additional constraint: 0
1
a
i ic
i
a
iicL
1
Cotton Fiber Example
Treatment--% cotton by weight (15%, 20%, 25%, 30%, 35%)
Response--Tensile strength
Montgomery, D. (2005) Design and Analysis of Experiments, 6th Ed. Wiley, NY.
51
51
,,
,,
kkk cc
c
Cotton Fiber Example
c 1 1, 1,0,0,0 L1 c1
' 1 2
Cotton Fiber Example
ˆ L 1 c 1 ˆ y 1. y 2.
ˆ V ˆ L 1 ˆ 2
nc1i
2
i1
a
SSL1
ˆ L 12
1
nc1i
2
i1
a
Contrast Test Statistic
T ˆ L 1
ˆ n
c1i2
i1
a
~ tn . a,
T 2 SSL1
ˆ 2F ~ F1,n . a
Under Ho:L1=0,
Unbalanced CRD Contrast SS
5
1
2
2ˆ
i i
iL
nc
LSS
Orthogonality
Contrasts are orthogonal if, for contrasts L1 and L2, we have
)caseunbalanced(0
)casebalanced(0
21
21
i
ii
ii
n
cccc
Orthogonality
The usual a-1 ANOVA contrasts are not orthogonal (though columns are linearly independent)
Orthogonality implies effect estimates are unaffected by presence/absence of other model terms
Orthogonality
Sums of squares for orthogonal contrasts are additive, allowing treatment sums of squares to be partitioned
Mathematically attractive, though not all contrasts will be interesting to the researcher
Cotton Fiber Example
Two sets of covariates (orthogonal and non-orthogonal) to test for linear and quadratic terms
Term Orth. SS Non-Orth SS
L 33.6 33.6
L|Q 33.6 364.0
Q 343.2 12.8
Q|L 343.2 343.2
L & Q 376.8 376.8
Cotton Fiber Example
For Orthogonal SS, L&Q=L+Q; Q=Q|L; L=L|Q
For Nonorthogonal SS, L&Q=L+Q|L=Q+L|QTerm Orth. SS Non-Orth SS
L 33.6 33.6
L|Q 33.6 364.0
Q 343.2 12.8
Q|L 343.2 343.2
L & Q 376.8 376.8
Orthogonal polynomial contrasts
Require quantitative factors Equal spacing of factor levels (d)
Equal ni
Usually, only the linear and quadratic contrasts are of interest
Orthogonal polynomial contrasts
Cotton Fiber Example
Orthogonal polynomial contrasts
Cotton Fiber Example
Orthogonal polynomial contrasts
F 33.62 343.21 /2
8.0623.38 (p .0001)
F 64.98 33.95 /2
8.066.137 (p .0084)
Cotton Fiber ExampleIs a L+Q model better than an intercept model?Is a L+Q model not as good as a cell means model? (Lack of Fit test)
Orthogonal polynomial contrasts
Yandell has an interesting approach to reconstructing these tests– Construct the first (linear) term– Include a quadratic term that is neither
orthogonal, nor a contrast– Do not construct higher-order contrasts
at all– Use a Type I analysis for testing