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This method was introduced at the last lecture.
Cell means model
ππππ = πππ + ππππ
Factor effects model
ππππ = πββ + πΌπ + π½π + πΌπ½
ππ
+ ππππ
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Method of least squares or maximum likelihood is
used to minimize:
π=
ππππ − πππ
π
π
2
π
or
π=
ππππ − πββ − πΌπ − π½π − πΌπ½
π
π
2
ππ
π
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ππππ =
ππππ − πβββ
π
π
π
ππππ
= π
πππβ − πβββ
π
πππΈ =
π
π
2
π
ππππ − πππβ
π
2
2
2
ππππ
=
π
π
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4
SSTR can be partitioned:
πππ΄ = ππ
ππββ − πβββ
2
π
πππ΅ = ππ
πβπβ − πβββ
2
π
πππ΄π΅ = π
πππβ − ππββ − πβπβ + πβββ
π
2
π
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As usual, the sum of squares are divided by their
degrees of freedom:
πππ΄
πππ΄ =
π−1
πππ΅
πππ΅ =
π−1
πππ΄π΅
πππ΄π΅ =
π−1 π−1
πππΈ
πππΈ =
ππ π − 1
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First of all the means should be visualized in a
treatment means plot (Interaction plot in Minitab).
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To test if interactions are important, a F-test is
calculated.
π»0 : πππ πΌπ½
ππ
π»π : πππ‘ πππ πΌπ½
πΉ∗
=0
ππ
=0
πππ΄π΅
=
πππΈ
πΌπ πΉ ∗ > πΉ 1 − πΌ; π − 1 π − 1 , π − 1 ππ
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π
πππππ‘ π»0
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If the interactions aren’t important, test for factor
effects.
Factor A
Factor B
π»0 : πππ πΌπ = 0
π»0 : πππ π½π = 0
π»π : πππ‘ πππ πΌπ = 0
πππ΄
∗
πΉ =
πππΈ
Critical value with a-1 and
n-1(ab) degrees of freedom.
π»π : πππ‘ πππ π½π = 0
πππ΅
∗
πΉ =
πππΈ
Critical value with b-1 and
n-1(ab) degrees of freedom.
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The methods on the following slides are used when
the interaction is considered unimportant.
Point estimators are:
ππβ = ππβββ
πππ
πβπ = πβπβ
Unbiased estimators of the variances:
πππΈ
πππΈ
2
2
π ππββ =
πππ π πβπβ =
ππ
ππ
Confidence interval is created with the t distribution:
π ± π‘ 1 − πΌ 2 ; π − 1 ππ ∗ π π
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Tukey is used when all pairwise comparisons are to
be made:
π· = ππββ − ππ ′ββ
1
2πππΈ
π·±
π 1 − πΌ; π, π − 1 ππ ∗
=
ππ
2
π·±π∗π π·
π· = πβπβ − πβπ ′β
1
2πππΈ
π·±
π 1 − πΌ; π, π − 1 ππ ∗
ππ
2
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If only a few pairwise comparisons is to be made, the
Bonferroni method usually is the best.
Instead of T, B should be used.
π΅ = π‘ 1 − πΌ 2π ; π − 1 ππ
Where g is the number of comparisons.
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Contrasts can also be obtained, with either the
Scheffé procedure or Bonferroni (see p. 852).
Be aware that the standard deviation and the
multiplicator is not the same for the different
factors.
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When the interactions are important, only the
treatment (cell) means should be analyzed.
I only show Tukey at the slides.
πππβ − ππ ′π ′β
1
2πππΈ
±
π 1 − πΌ; ππ, π − 1 ππ ∗
π
2
=π·±π∗π π·
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On the upcoming (last) lecture we will discuss:
• Two-way ANOVA with only one case per treatment
(ch 20)
• Randomized complete block designs
(ch 21)
• Analysis of Covariance
(ch 22)
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ο½
Chapter 19
ο½
Start reading chapter 20, 21, 22
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