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Design of ExperimentsDoE)
Pendahuluan (Konsep Definisi)
Exp with a Single Factor: ANOVA (Ch: 3)
Randomized Bloks (Ch: 4)
Latin Square (Ch: 4)
Factorial Design (Ch:5)
Two level Fractional Factorial Design (Ch:8)
Taguchi approach
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PENDAHULUAN
1. Menentukan penyebab penyebab variasi dalamresponse
2. Untuk menentukan kondisi yang optimal(maksimum atau minimum) sehingga optimasiresponse dapat dicapai
3. Membandingkan response menurut setiap level
pada faktor faktor yang dikontrol4. Mengembangkan model untuk prediksi
responses
Mengapa Eksperimen
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24-Dec-13 (c) 2001, Ron S. Kenett, Ph.D. 3
A Serious Problem...
I want mycar to go
fastlikethat one!
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24-Dec-13 (c) 2001, Ron S. Kenett, Ph.D. 4
What Factors Affect the Speed?
Key Factor is: ___________________
Yes
Air Holes
No
Slow
Fast
Shape
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24-Dec-13 (c) 2001, Ron S. Kenett, Ph.D. 5
Key Factor is: _______________________________
Yes
Air Holes
No
Slow
FastSlow
Shape
Effect of Air Holes
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24-Dec-13 (c) 2001, Ron S. Kenett, Ph.D. 6
DOE Balanced Effects
Key Factor is: _______________________________
Yes
Air Holes
No
Slow
FastSlow
Shape
Slow
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KONSEP DEFINISI
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Definisi1. Treatmentsdifferent
combinations of conditions that we
wish to test2. Treatment Levelsthe relative
intensities at which a treatment will
be set during the experiment
3. Factor (Treatment factor)one of
the controlled conditions of the
experiment (these combine to form
the treatments)
4. Experimental Unitsubject on
which a treatment will be applied
and from which a response will be
elicitedalso called measurementor response units
5. Responsesoutcomes that will be
elicited from experimental units
after treatments have been applied
Perlakuanberbagai kombinasi
kosndisi dan situasi yang berbeda
yang akan kita uji Level Perlakuanintensitas relatif
dimana satu perlakuan akan disetel
selama percobaan
Faktor (Faktor perlakuan)salah satu
kondisi terkontrol dari percobaan(kombinasi ini untuk membentuk
perlakuan)
Unit percobaansubyek pada yang
mana satu perlakuan akan diterapkan
dan dari yang mana satu tanggapan
akan timbuljuga pengukuran
dipanggil atau unit tanggapan
Tanggapihasil yang akan timbul dari
unit percobaan setelah perlakuan
telah diterapkan
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6. Design of Experiments (DOE)alsoreferred to as Experimental Design,this is the study of planningefficient and systematic collection
of responses from experimentalunits
7. Experimental Designrule forassigning treatment levels toexperimental units
8. Analysis of Variance (ANOVA)principal statistical means for
evaluating potential sources ofvariation in the responses
9. Replicationobserving individualresponses of multiple experimentalunits under identical experimentalconditions
10. Repeated Measurements
observing multiple responses of asingle experimental unit underidentical experimental conditions
Perancangan Percobaan (KIJANGBETINA)juga dikenal sebagai DesainPercobaan, ini adalah pembahasandari efisien perencanaan dan koleksi
sistematis dari tanggapan dari unitpercobaan
Desain percobaanketentuan untuktaraf perlakuan penugasan ke unitpercobaan
Analisa varians (ANOVA)datastatistik terpenting memaksudkan
untuk mengevaluasi sumberpotensial dari variasi pada tanggapan
Replikatanggapan individupengamatan dari unit percobaanperkalian pada kondisi percobaanserupa
Mengulangi Pengukurantanggapanperkalian pengamatan dari unitpercobaan tunggal pada kondisipercobaan serupa
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11. Blockingpartition theexperimental units into groups (orblocks) that are homogeneous insome sense
12. Covariateadditional responsescollected from the experimentalunits, usually to be used aspredictors (and so are sometimescalled predictive responses)theseare not part of a designedexperiment (why?)
13. Randomizationnonsystematicassignment of experimental unitsto treatments
14. Blindinghiding whichexperimental units have beenassigned to treatments from theanalyst
15. Confoundingdesign situation inwhich the effect of one factor ortreatment can not be distinguishedfrom another factor or treatment -this is the experimental equivalentof perfect multicolinearity (why?)
Menghalangisekat unit percobaan ke
dalam group (atau halangi) yang
homogen di beberapa rasa
Covariate tanggapanadditional
terkumpul dari unit percobaan,biasanya dipergunakan seperti peramal
(dan demikian sering menjadi
tanggapan bersifat prediksi yang
dipanggil)ini bukan bagian dari satu
percobaan didisain (kenapa?)
Pengacakantugas nonsystematic
dengan unit percobaan ke perlakuan
Membutakansembunyi yang mana
unit percobaan telah ditugaskan ke
perlakuan dari ahli analisa
Mengacaukankeadaan desaindarimana akibat dari faktor sesuatu
atau perlakuan tidak dapat dicirikan
faktor lain atau perlakuan ini adalah
padanan percobaan dengan
multicolinearity sempurna (kenapa?)
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Experimentation
Manipulation of the
values (or levels) of one
or more (independent)
variables or treatmentsand observation of the
corresponding change
in the values of one or
more (dependent)variables or responses
Manipulasi dari nilai
(atau tingkat) dari satu
atau lebih (bebas tak
terikat) variabel atauperlakuan dan observasi
dari perubahan sesuai
pada nilai dari satu atau
lebih (bergantung)variabel atau tanggapan
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Basic Principles
1. Replicationobservingindividual responses ofmultiple experimentalunits under identicalexperimental conditions
2. Randomizationnonsystematic assignment ofexperimental units totreatments
3. Blockingpartition the
experimental units intogroups (or blocks) that arehomogeneous in somesense
Replikatanggapanindividu pengamatan dariunit percobaan perkalianpada kondisi percobaanserupa
Pengacakanbukan tugassistematis dengan unitpercobaan ke perlakuan
Menghalangisekat unitpercobaan ke dalam group
(atau blok ) yang homogendi beberapa rasa
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Chap 3. Exp with a Single Factor:ANOVA
1. Simple design: Experimen dengan satu
faktor misalnya siswa di kelas A dg
kurikulum KTSP vs kelas B dg MBS dalam
UAN.
2. Faktor dapat dilakukan lebih dari dua level.
3. Analisis dilakukan dg Anova sederhana.
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Cotton weight
Percentage
Observasi (replikasi) T Rerata
1 2 3 4 5
15 7 7 15 11 9 49 9.820 12 17 12 18 18 77 15.4
25 14 18 18 19 19 88 17.6
30 19 25 22 19 23 108 21.635 7 10 11 15 11 54 10.8
Sumber: Table 2.1 hal 62
Strength
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/nGn
TSSTr/nGxSST
SST1-lnT
MSESSE2-nE
MSTr/MSEMSTrSSTr1Tr
FMSSSdfSV
2
j
J22
ANOVA
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Mixing Technique Tensile Strenght (lb/n2)
12
3
4
31293200
2800
2600
30003300
2900
2700
28652975
2985
2600
28903150
3050
2765
3-1. The tensile strength of Portland cement is being studied. Four different mxing
techniques can be used economically. The following data have been collected:
Test the hypothesis that mixing techniques affect the strength of the comnet. Use
= 0,05.
Temperature Density
100
125
150
175
21.8
21.7
21.9
21.9
21.9
21.4
21.8
21.7
21.7
21.5
21.8
21.8
21.6
21.4
21.6
21.4
21.7
21.5
3-4. And in experiment was run to determine whether four specific firing temperaturesaffect the density of a certain type of brick. The experiment led to the following data:
Does the firing temperature affect the density of the brick? Use = 0,05.
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Coating Type Conductivity
12
3
4
143152
134
129
141149
136
127
150137
132
132
146143
127
129
3-6. A manufacturer of television sets is interested in the effect on tube conductivity
data are obtained:
Is there a difference in conductivity due to coating type? Use = 0,05.
Estimate
Rodding Level Compressive Strenght
10
15
20
25
1530
1610
1560
1500
1530
1650
1730
1490
1440
1500
1530
1510
3-8. An article in theACI Material Journal(Vol. 84, 1947, pp. 213-216) describe
several experiments investigating the rodding of concrete to remove entrapped air. A 3-
inch X 6-inch cylinder was used, and the number of times this rod was used is the
design variable. The resulting compressive strength of the concrete specimen is the
reponse. The data are shown in the following table:
Is there any difference in compressive strength due the rodding level? Use = 0,05.
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Bab 4 Randomized Block, Latin Sq,and Related Designs1. RCBD (Randomized Complete Block Design)
2. Latin Square Design3. Graeco Latin Square Design
4. BIBD (Balanced Incomplete Block Design)
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1. RCBD (Randomized Complete Block
Design)
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2. The Latin Square Design
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Chemical
Bolt
1 2 3 4 5
1
2
3
4
73
73
75
73
68
67
68
71
74
75
78
75
71
72
73
75
67
70
68
69
4-1. A chemist wishes to test the effect of four chemical
agents on the strength of a particular type of cloth.
Because there might be variability from one bolt toanother, the chemist decides to use a randomized block
design, with the bolts of cloth considered as blocks. She
selects five bolt and applies all four chemicals ain
random order to each bolt. The resulting tensile strength
follow. Analyze the data from this experiment (use =0.05) and draw appropriate conclusions.
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Solution
Days
1 2 3 41
2
3
13
16
5
22
24
4
18
17
1
39
44
22
4-2. Three different washing solutions are being compared
to study their effectiveness in retarding bacteria growth in 5-
gallon milk containers. The analysis is done in a laboratory,and only three trials can be run on any day. Because days
could represent a potential source of variability, the
experimenter decides to use a randomized block design.
Observations are taken for four days, and the data areshown here. Analyze the data from this experiments (use
= 0.05) and draw conclusions.
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Nozzle
Design
Jet Efflux Velocity (m/s)
11.73 14.73 16.59 20.43 23.46 28.74
1
2
3
4
5
0.78
0.85
0.93
1.14
0.97
0.80
0.85
0.92
0.97
0.86
0.81
0.92
0.95
0.98
0.78
0.75
0.86
0.89
0.88
0.76
0.77
0.81
0.89
0.86
0.76
0.78
0.83
0.83
0.83
0.75
4-5. An article in the Fire Safety Journal (TheEffect
of Nozzle Design on the Stability and Performance of
Turbulent Water Jets, Vol. 4. August 1981) describes an
experiment in which a shape factor was determined forseveral different nozzle design at six levels of jet efflux
velocity. Interest focused on potential differences
between nozzle design, with velocity considered as a
nuisance variable. the data are shown below:
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Stirring
Rate (rpm)
Furnace
1 2 3 4
5
10
1520
8
14
1417
4
5
69
5
6
93
4
9
26
4-7. Any alumunium master alloy manufacturer produces grain refiners in
ingot form. The comperating produces the product in four furnaces is known to
have its own unique operating characteristics, soa any experiment run in the
foundry thay involves more than one furnace will consider furnaces as anuisance variable. The process engineers suspect that stiring rate affects the
grain size of the product. Each furnace can be run at four different atirring
rates. A randomized block design is run for a particular refiner, and the
resulting grain size data is show below:
1. Graph the residuals from thisexperiment on a normal probability
plot. Interpret this plot.
2. Is there any evidence that stirring
rate affects grain seze?
3. Plot the residuals versus furnace
and stirring rate. Does this plot
convey any useful information?4. What should the process engineers
recommend concerning the choice
of stirring rate and furnace for this
particular grain refiner if small grain
significant test.
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Distanc
e (ft)
Subject
1 2 3 4 5
46
8
19
107
5
6
66
3
4
66
3
4
61
2
2
66
5
3
4-13. An industrial engineer is conducting an experiment
on eye focus time. he is interested in the effect of the
distance of the object from the eye on the focus time.
Four different distances are of interest. He has fivesubjects available for the experiment. Because there may
be differences among individuals, he decides to conduct
the experiment in arandomized block design. The data
obtained follow. Analyze the data from this experiment(use = 0.05) and draw appropriate conclusions
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ANOVA Latin Sq Design
Source DF Seq SS Adj SS Adj MS F P
RM 4 68 68 17.000 1.590 0.239
Operator 4 150 150 37.500 3.520 0.040
Latin 4 330 330 82.500 7.730 0.003
Error 12 128 128 10.670
Total 24 676
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Batch
Day
1 2 3 4 5
12
3
4
5
A= 8
C = 11
B = 4
D= 6
E= 4
B= 7
E = 2
A = 9
C= 8
D= 2
D= 1
A = 7
C = 10
E= 6
B= 3
C= 7
D = 3
E = 1
B= 6
A= 8
E= 3
B = 8
D = 5
A= 10
C= 4
4-14. The effect of five different ingredients (A, B, C, D, E)
on the reaction time of a chemical process is being studied.
Each batch of new material is only large enough to permit
five runs to be made. Furthermore, each run requiresapproximately 1 hours, so only five runs can be made in
one day. The experimenter decided to run the experiment
as a Latin square so that day and batch effects may be
systematically controlled. She obtains the data that follow.
Analyze the data from this experiment (use = 0.05) and
draw conclusions.
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Order of
Assembly
Operator
1 2 3 4
1
2
3
4
C= 10
B = 7
A = 5
D= 10
D= 14
C = 18
B = 10
A= 10
A= 7
D = 11
C = 11
B= 12
B= 8
A = 8
D = 9
C= 14
4-15. An industrial engineer is investigating the effect of
four assembly methods (A, B, C, D,) on the assembly
time for a color television component. Four operators are
selected for the study. Furthermore, the engineer knows
that each assembly method produces such fatigue that
the time required for the last assembly may be greater
than the time required for the first, regardless of the
method. That is, a trend develops in the requiredassembly time. To account for this source of variability,
the engineer uses the Latin square design shown below.
Analyze the data from this experiment (use = 0.05) and
drw appropriate conclusions.
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Graeco Latin Square Design
Montgomery tabel 4.9 and Tabel 4.20
Operator
RM 1 2 3 4 5 T1 24 20 19 24 24 111
2 17 24 30 27 36 134
3 18 38 26 27 21 130
Operator 4 26 31 26 23 22 128
RM 1 2 3 4 5 5 22 30 20 29 31 132
1 A=24 B=20 C=19 D=24 E=24 T 107 143 121 130 134 6352 B=17 A=24 D=30 E=27 A=36
3 C=18 B=38 A=26 A=27 B=21
4 D=26 C=31 B=26B=23 C=22 Graeco
5 E=22 D=30C=20 C=29 D=31 Latin 1 2 3 4 5 T
1 24 30 26 27 36 143
2 21 17 20 20 23 1013 29 22 18 24 19 112
4 30 24 18 26 38 149
5 31 26 18 24 22 130
T 135 119 18 121 138 635
3. The Graeco-Latin Sq Design
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ANOVA Graeco-Latin Sq Design
Source DF Seq SS Adj SS Adj MS F P
RM 4 68 68 17.00 2.06 0.178
Operator 4 150 150 37.50 4.55 0.033
Latin 4 330 330 82.50 10.00 0.003
Greek 4 62 62 15.50 1.88 0.208
Error 8 66 66 8.25
Total 24 676
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Batches
Acid concentrations
1 2 3 4 5
1
2
34
5
A= 26
B= 18
C= 20D= 15
E = 10
B= 18
C = 21
D= 12E = 15
A= 24
C = 14
D= 18
E =16A= 22
B= 17
D= 16
E = 11
A= 25B= 14
C= 17
E = 13
A = 21
B= 13C= 17
D= 14
4-22. The yield of a chemical process was measured
using five batches of raw material, five acid
concentrations, ive satnding times (A, D, C, D, E), andfive catalyst concentrations (,,,,). The Graceo-
Latin square that follow was used. Analyze the data from
this experiment (use = 0.05) and draw conclusions
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Order of
Assembly
Operator
1 2 3 4
1
2
3
4
C= 11
B = 8
A = 9
D = 9
B = 10
C =12
D = 11
A =8
D= 14
A= 10
B= 7
C= 18
A= 8
D= 12
C= 15
B= 6
4-23. Suppose that in Problem 4-15 the engineer
suspects that the workplaces used by the faour
operators may represent an additional source ofvariation. A fourth factor, workplace (,,,,)may
be introduced and another experiment conducted,
yielding the Graceo-Latin square that follows,
Analyze the data from this experiment (use =
0.05) and draw conclusiona.
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4. The BIBD
Table 4.22 Montgomery
CataliystRaw Material
Ti1 2 3 4
1 73 74 71 2182 75 67 72 214
3 73 75 68 216
4 75 72 75 222
Tj 221 224 207 218 870
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Perhitungan Anova BIBDsama dengan
BCBD
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ANOVA BIBD
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Additive Car
1 2 3 4 5
1
23
4
5
1412
13
11
17
1411
12
14
1311
10
13
1312
12
12
109
8
4-27. An engineer is studying the mileage of
performance characteristics of five types of gasoline
additives. In the road test he wishes to use cars asblocks; however, because of a time constraint, he
must use an incomplete block design. He runs the
balanced design with the five blocks that follow.
Analyze the data from this experiments (use = 0.05)and draw conclusions.
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4-29. Seven different hardwood concentrations are
being studied to determine their effect on the strength
of the paper produced. However, the pilot plant can
only produce three runs each day. As days may differ,
the analyst uses the balanced incomplete block
design that follows. Analyze the data from this
experiment (use = 0.05) and draw conclusions.
Hardwood
Concentration
(%)
Days
1 2 3 4 5 6 7
2 114 120 117
4 126 120 1196 137 117 134
8 141 129 149
10 145 150 143
12 120 118 123
14 136 130 127
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Bab 5 Factorial Design
5.1 Basic Definition
5.2 The Advantages
5.3 The Two Factor Factorial Design
5.4 The General Factorial Design
5.5 Fitting Response Curve and Surfaces
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5.1 Basic Definition and Principles
a. Main Effect: the change in response
produced by the change in the level of the
factor.
b. Interaction: diffrenece in response between
the levels of one factor is not the same at all
levels of the other factors.
c. The three dimensional graph is called aresponse surface plot.
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A B X Y
-1 -1 20 20
1 -1 40 50
-1 1 30 401 1 52 12
Ada interaksi pada
eksperiment A.Tetapi ada interaksi
pada eksperimen B
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5.2 The Advantages
1. efficient,
2. check the interaction,
3. the effects of a factor to be estimatedat several levels of the other factors.
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5.3 The Two Factor Factorial Design
An example
Statistical Analysis of the fixed Effects Model
Model Adequasy Checking
Estimating the Model Parameters
Choice of Sample Size
The Assumption of No Interaction in a two
factor model One Observation per cell
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Table 5-1 Life (in hours) Data for Battery Design Example
Material Temperature
Type 15 70 125
1 130 155 34 40 20 70
74 180 80 75 82 58
2 150 188 136 122 25 70
159 126 106 115 58 45
3 138 110 174 120 96 104168 160 150 139 82 60
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No MT Temp x No MT Temp x No MT Temp x
1 1 1 130 13 2 1 150 25 3 1 138
2 1 1 155 14 2 1 188 26 3 1 110
3 1 2 34 15 2 2 136 27 3 2 174
4 1 2 40 16 2 2 122 28 3 2 120
5 1 3 20 17 2 3 25 29 3 3 96
6 1 3 70 18 2 3 70 30 3 3 104
7 1 1 74 19 2 1 159 31 3 1 168
8 1 1 180 20 2 1 126 32 3 1 160
9 1 2 80 21 2 2 106 33 3 2 150
10 1 2 75 22 2 2 115 34 3 2 139
11 1 3 82 23 2 3 58 35 3 3 82
12 1 3 58 24 2 3 45 36 3 3 60
Desain Data untuk MINITAB
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Two-way ANOVA: x versus MT, Temp
Source DF SS MS F P
MT 2 10684.0 5341.9 7.91 0.002
Temp 2 39119.0 19559.0 28.97 -
Interaction 4 9613.8 2403.4 3.56 0.019
Error 27 18231.0 675.2
Total 35 77647.0
S = 25.98 R-Sq = 76.52% R-Sq(adj) = 69.56%
Analisis of Variance
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5.3.7 One Observation per Cell
Temp FPressure
25 30 35 40 45
100 5 4 6 3 5125 3 1 4 2 3
150 1 1 3 1 2
Table 5.10 Impuriry
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Hasil Analisis of Variance
Two-way ANOVA: Impurity versus Temp, Press
Source DF SS MS F P
Temp 2 23.33 11.67 46.67 0.00
Press 4 11.60 2.90 11.60 0.00Error 8 2.00 0.25
Total 14 36.93
S = 0.5 R-Sq = 94.58% R-Sq(adj) = 90.52%
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5.4 The General Factorial Design
1. Kelanjutan dari two factor factorial design.
Faktor A dengan a levels, faktor B dengan b
levels, faktor C dengan c levels, dst.
2. Misal nya faktor ABC dengan levels a=3, b=2,
dan c=2 tentang response x=fill height
deviation.
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Table 5.13 Fill Height Deviation
Pressure (B) 25 psi 30 psi
Line Spee (C ) 200 250 200 250
Carbonation (A)
10-3 -1 -1 1
-1 0 0 1
120 2 2 6
1 1 3 5
145 7 7 10
4 6 9 11
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Data dalam format MINITAB atau SPSS
No A B C X No A B C X
1 10 25 200 -3 13 12 25 200 1
2 10 25 250 -1 14 12 25 250 1
3 10 30 200 -1 15 12 30 200 3
4 10 30 250 1 16 12 30 250 5
5 10 25 200 -1 17 14 25 200 5
6 10 25 250 0 18 14 25 250 7
7 10 30 200 0 19 14 30 200 7
8 10 30 250 1 20 14 30 250 10
9 12 25 200 0 21 14 25 200 4
10 12 25 250 2 22 14 25 250 611 12 30 200 2 23 14 30 200 9
12 12 30 250 6 24 14 30 250 11
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Analysis of Variance
5 5 Fitting Response Curve and
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5.5 Fitting Response Curve andSurfaces
i. Fitting response curve
ii. Fitting response surface
iii. Usinf regression
iv. To predict
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Table 5.16 Tool Life
Angle
cutting speed
in/min
degree 125 150 175
15
-2 -3 2
-1 0 3
20
0 1 4
2 3 6
25-1 5 00 6 -1
No A B X No A B X
1 15 125 -2 10 20 125 2
2 15 150 -3 11 20 150 3
3 15 175 2 12 20 175 6
4 15 125 -1 13 25 125 -1
5 15 150 0 14 25 150 5
6 15 175 3 15 25 175 0
7 20 125 0 16 25 125 08 20 150 1 17 25 150 6
9 20 175 4 18 25 175 -1
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5.6 Blocking in a Factorial Design
Untuk lebih efisien dan mengoptimalkan
pelaksanaan eksperimen terkadang diperlukan
blocking, hal ini untuk menghindari noise yang
menggangu proses eksperimen.
Blocking didasarkan atas keseragaman dari
response yang akan muncul.
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Table 5-19 Intensity Level at Target Detection
operator block 1 2 3 4
filter type 1 2 1 2 1 2 1 2
Ground cutter low 90 86 96 84 100 92 92 81
medium 102 87 106 90 105 97 6 80
hught 114 93 112 91 108 95 98 83
T b l 5 21 R d D t ti E i t
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Day
Operator
1 2 3 4 5 6
1 A B C D F E
90 106 108 81 90 88
2 C A B F E D
114 96 105 83 86 84
3 B E F A D C102 90 95 92 85 104
4 E D A B C F
87 84 100 96 110 91
5 F C D E A B93 112 92 80 90 98
6 D F E C B A
86 91 97 98 100 92
Tabel 5.21 Radar Detection Experiment
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5-4. An article in Industrial Quality Control(1956, pp. 5-
8) describes an experiment to investigate the effect of
the type of glass and the type of phosphor on the
brightness of an television tube. The response variableis the current necessary (in microamps) to obtain a
specified brightness level. The data are as follow:
i.Is there any indication that either factor influences
brightness? Use = 0.05.ii.Do the two factors iteract? Use = 0.05.
iii.Analyze the residuals from this experiment
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5-1 The yield of chemical process is being studied. The
two most important variables are thought to be the
pressure and the temperature. Three levels of each factor
are selected, and a factorial experiment with two
replicates is performed. The yield data follow:
i.Analyze the data and draw conclusions. Use = 0.05.
ii.Prepare appropriate residual plots and comment on the
models adequancy.iii.Under what conditions would you operate this process?
5 17 Th lit t l d t t f f b i fi i hi
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5-17. The quality control department of a fabric finishing
plant is studying the effect of several factors on the dyeing
of cotton-sunthetic cloth used to manufacture mens shirt.
Three operators, three cycle times, and two temperatureswere selected, and three small specimens of cloth were
dyed under each set of conditions. The finished cloth was
compared and drw cpnclusions. Comment on the models
adequacy.
UTS
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UTS
MK: Design and Analysis of Experiment
1.Dalam rancangan percobaan di kenal konsep:Simple design, RCBD, Latin Square, dan BIBD.Deskripsikan apa yang di maksud dengan
empat desain/ rancangan tersebut.2. Contoh: Anda diminta mengerjakan secara
manual analisis rancangan percobaansederhana (simple anova) dengandata pada
soal: 3.6 (Petunjuk lihat mk statistik atau dgexel maupun spss).
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3. Saudara diminta
membuat
rancanganpengolahan SPSS
dari tampilan
rancangan dalam
soal Montgomery
no: 3.17 3.30
4.2 4.5 5.7 4.15
4.22 4.27 4.29Hint: lihat contoh
disamping.