design and analysis of experiments
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Design and Analysis of Experiments. Dr. Tai-Yue Wang Department of Industrial and Information Management National Cheng Kung University Tainan, TAIWAN, ROC. Factorial Experiments. Dr. Tai- Yue Wang Department of Industrial and Information Management National Cheng Kung University - PowerPoint PPT PresentationTRANSCRIPT
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Design and Analysis of Experiments
Dr. Tai-Yue Wang Department of Industrial and Information Management
National Cheng Kung UniversityTainan, TAIWAN, ROC
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Factorial Experiments
Dr. Tai-Yue Wang Department of Industrial and Information Management
National Cheng Kung UniversityTainan, TAIWAN, ROC
2/33
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Outline Basic Definition and Principles The Advantages of Factorials The Two Factors Factorial Design The General Factorial Design Fitting Response Curve and Surfaces Blocking in Factorial Design
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Basic Definitions and Principles Factorial Design—all of the possible
combinations of factors’ level are investigated When factors are arranged in factorial design,
they are said to be crossed Main effects – the effects of a factor is defined
to be changed Interaction Effect – The effect that the
difference in response between the levels of one factor is not the same at all levels of the other factors.
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Basic Definitions and Principles Factorial Design without interaction
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Basic Definitions and Principles Factorial Design with interaction
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Basic Definitions and Principles Average response – the average value at one
factor’s level Average response increase – the average value
change for a factor from low level to high level No Interaction:
40 52 20 3021
2 230 52 20 40
112 2
52 20 30 401
2 2
A A
B B
A y y
B y y
AB
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50 12 20 401
2 240 12 20 50
92 2
12 20 40 5029
2 2
A A
B B
A y y
B y y
AB
Basic Definitions and Principles With Interaction:
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0 1 1 2 2 12 1 2
1 2 1 2 1 2
The least squares fit is
ˆ 35.5 10.5 5.5 0.5 35.5 10.5 5.5
y x x x x
y x x x x x x
Basic Definitions and Principles Another way to look at interaction: When factors are quantitative
In the above fitted regression model, factors are coded in (-1, +1) for low and high levels
This is a least square estimates
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Basic Definitions and Principles Since the interaction is small, we can ignore it. Next figure shows the response surface plot
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Basic Definitions and Principles The case with interaction
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Advantages of Factorial design Efficiency Necessary if interaction effects are presented The effects of a factor can be estimated at several
levels of the other factors
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The Two-factor Factorial Design
Two factors a levels of factor A, b levels of factor B n replicates In total, nab combinations or experiments
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The Two-factor Factorial Design – An example
Two factors, each with three levels and four replicates
32 factorial design
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The Two-factor Factorial Design – An example
Questions to be answered: What effects do material type and temperature
have on the life the battery Is there a choice of material that would give
uniformly long life regardless of temperature?
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Statistical (effects) model:
1,2,...,
( ) 1, 2,...,
1, 2,...,ijk i j ij ijk
i a
y j b
k n
means model
The Two-factor Factorial Design
nk
bj
,...,a,i
y ijkijijk
,...,2,1
,...,2,1
21
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The Two-factor Factorial Design
Hypothesis Row effects:
Column effects:
Interaction:
0 oneleast at :
0: 210
ia
a
H
H
0 oneleast at :
0: 210
ia
a
H
H
0)( oneleast at :
0)(:0
ija
ij
H
H
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The Two-factor Factorial Design -- Statistical Analysis
2 2 2... .. ... . . ...
1 1 1 1 1
2 2. .. . . ... .
1 1 1 1 1
( ) ( ) ( )
( ) ( )
a b n a b
ijk i ji j k i j
a b a b n
ij i j ijk iji j i j k
y y bn y y an y y
n y y y y y y
breakdown:
1 1 1 ( 1)( 1) ( 1)
T A B AB ESS SS SS SS SS
df
abn a b a b ab n
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The Two-factor Factorial Design -- Statistical Analysis
Mean square: A:
B:
Interaction:
11)( 1
2
2
a
bn
a
SSEMSE
a
ii
AA
11)( 1
2
2
b
an
b
SSEMSE
a
ii
BB
)1)(1(
)(
)1)(1()( 1 1
2
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ba
n
ba
SSEMSE
a
i
b
jij
ABAB
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The Two-factor Factorial Design -- Statistical Analysis
Mean square: Error:
2
)1()(
nab
SSEMSE E
A
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The Two-factor Factorial Design -- Statistical Analysis
ANOVA table
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The Two-factor Factorial Design -- Statistical Analysis
Example
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The Two-factor Factorial Design -- Statistical Analysis
Example
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The Two-factor Factorial Design -- Statistical Analysis
Example
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The Two-factor Factorial Design -- Statistical Analysis
Example STATANOVA--GLMGeneral Linear Model: Life versus Material, Temp
Factor Type Levels ValuesMaterial fixed 3 1, 2, 3Temp fixed 3 15, 70, 125
Analysis of Variance for Life, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F PMaterial 2 10683.7 10683.7 5341.9 7.91 0.002Temp 2 39118.7 39118.7 19559.4 28.97 0.000Material*Temp 4 9613.8 9613.8 2403.4 3.56 0.019Error 27 18230.7 18230.7 675.2Total 35 77647.0
S = 25.9849 R-Sq = 76.52% R-Sq(adj) = 69.56%
Unusual Observations for LifeObs Life Fit SE Fit Residual St Resid 2 74.000 134.750 12.992 -60.750 -2.70 R 8 180.000 134.750 12.992 45.250 2.01 R
R denotes an observation with a large standardized residual.
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The Two-factor Factorial Design -- Statistical Analysis
Example STATANOVA--GLM
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The Two-factor Factorial Design -- Statistical Analysis
Example STATANOVA--GLM
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The Two-factor Factorial Design -- Statistical Analysis
Estimating the model parameters
1,2,...,
( ) 1, 2,...,
1, 2,...,ijk i j ij ijk
i a
y j b
k n
........
.....
.....
...
yyyy
yy
yy
y
jiijij
jj
ii
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The Two-factor Factorial Design -- Statistical Analysis
Choice of sample size Row effects
Column effects
Interaction effects
D:difference, :standard deviation
2
22
2 a
nbD
2
22
2 b
naD
]1)1)(1[(2 2
22
ba
nD
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The Two-factor Factorial Design -- Statistical Analysis
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The Two-factor Factorial Design -- Statistical Analysis
Appendix Chart V For n=4, giving D=40 on temperature, v1=2,
v2=27, Φ 2 =1.28n. β =0.06
n Φ2 Φ υ1 υ2 β
2 2.56 1.6 2 9 0.45
3 3.84 1.96 2 18 0.18
4 5.12 2.26 2 27 0.06
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The Two-factor Factorial Design -- Statistical Analysis – example with no interaction
Analysis of Variance for Life, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F PMaterial 2 10684 10684 5342 5.95 0.007Temp 2 39119 39119 19559 21.78 0.000Error 31 27845 27845 898Total 35 77647
S = 29.9702 R-Sq = 64.14% R-Sq(adj) = 59.51%
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The Two-factor Factorial Design – One observation per cell
Single replicate The effect model
bj
aiy ijijjiij ,...,2,1
,...,2,1 )(
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The Two-factor Factorial Design – One observation per cell
ANOVA table
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The Two-factor Factorial Design -- One observation per cell
The error variance is not estimable unless interaction effect is zero
Needs Tuckey’s method to test if the interaction exists.
Check page 183 for details.
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The General Factorial Design
In general, there will be abc…n total observations if there are n replicates of the complete experiment.
There are a levels for factor A, b levels of factor B, c levels of factor C,..so on.
We must have at least two replicate (n 2≧ ) to include all the possible interactions in model.
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The General Factorial Design
If all the factors are fixed, we may easily formulate and test hypotheses about the main effects and interaction effects using ANOVA.
For example, the three factor analysis of variance model:
nl
ckbj
ai
y ijklijkjkikijkjiijkl
,...,2,1
,...,2,1,...,2,1
,...,2,1
)()()()(
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The General Factorial Design ANOVA.
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The General Factorial Design where
a
i
b
j
c
k
n
lijklT abcn
yySS
1 1 1 1
2....2
a
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bcnSS
1
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1 1
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abcn
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1 1 1
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.
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kijkTE abcn
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1 1 1
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.
1
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The General Factorial Design --example
Three factors: pressure, percent of carbonation, and line speed.
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The General Factorial Design --example
ANOVA
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Fitting Response Curve and Surfaces
When factors are quantitative, one can fit a response curve (surface) to the levels of the factor so the experimenter can relate the response to the factors.
These surface could be linear or quadratic. Linear regression model is generally used
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Fitting Response Curve and Surfaces -- example
Battery life data Factor temperature is quantitative
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Example STATANOVA—GLM Response life Model temp, material temp*temp,
material*temp, material*temp*temp Covariates temp
Fitting Response Curve and Surfaces -- example
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General Linear Model: Life versus Material Factor Type Levels ValuesMaterial fixed 3 1, 2, 3
Analysis of Variance for Life, using Sequential SS for TestsSource DF Seq SS Adj SS Seq MS F PTemp 1 39042.7 1239.2 39042.7 57.82 0.000Material 2 10683.7 1147.9 5341.9 7.91 0.002Temp*Temp 1 76.1 76.1 76.1 0.11 0.740Material*Temp 2 2315.1 7170.7 1157.5 1.71 0.199Material*Temp*Temp 2 7298.7 7298.7 3649.3 5.40 0.011Error 27 18230.8 18230.8 675.2Total 35 77647.0S = 25.9849 R-Sq = 76.52% R-Sq(adj) = 69.56%
Term Coef SE Coef T PConstant 153.92 11.87 12.96 0.000Temp -0.5906 0.4360 -1.35 0.187Temp*Temp -0.001019 0.003037 -0.34 0.740Temp*Material 1 -1.9108 0.6166 -3.10 0.005 2 0.4173 0.6166 0.68 0.504Temp*Temp*Material 1 0.013871 0.004295 3.23 0.003 2 -0.004642 0.004295 -1.08 0.289
Two kinds of coding methods:1. 1, 0, -12. 0, 1, -1
coding method: -1, 0, +1
Fitting Response Curve and Surfaces -- example
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Final regression equation:
]2[*004642.0]1[*013871.0]2[*4173.0
]1[*9108.1*001019.0]2[*7.5
]1[*46.15*5906.092.153
22
2
BABAAB
ABAB
BALife
Fitting Response Curve and Surfaces -- example
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Tool life Factors: cutting speed, total angle Data are coded
Fitting Response Curve and Surfaces – example –32 factorial design
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Fitting Response Curve and Surfaces – example –32 factorial design
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Fitting Response Curve and Surfaces – example –32 factorial design
Regression Analysis: Life versus Speed, Angle, ... The regression equation isLife = - 1068 + 14.5 Speed + 136 Angle - 4.08 Angle*Angle - 0.0496 Speed*Speed - 1.86 Angle*Speed + 0.00640 Angle*Speed*Speed + 0.0560 Angle*Angle*Speed - 0.000192 Angle*Angle*Speed*Speed
Predictor Coef SE Coef T PConstant -1068.0 702.2 -1.52 0.163Speed 14.480 9.503 1.52 0.162Angle 136.30 72.61 1.88 0.093Angle*Angle -4.080 1.810 -2.25 0.051Speed*Speed -0.04960 0.03164 -1.57 0.151Angle*Speed -1.8640 0.9827 -1.90 0.090Angle*Speed*Speed 0.006400 0.003272 1.96 0.082Angle*Angle*Speed 0.05600 0.02450 2.29 0.048Angle*Angle*Speed*Speed -0.00019200 0.00008158 - 2.35 0.043
S = 1.20185 R-Sq = 89.5% R-Sq(adj) = 80.2%
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Fitting Response Curve and Surfaces – example –32 factorial design
Analysis of Variance
Source DF SS MS F PRegression 8 111.000 13.875 9.61 0.001Residual Error 9 13.000 1.444Total 17 124.000
Source DF Seq SSSpeed 1 21.333Angle 1 8.333Angle*Angle 1 16.000Speed*Speed 1 4.000Angle*Speed 1 8.000Angle*Speed*Speed 1 42.667Angle*Angle*Speed 1 2.667Angle*Angle*Speed*Speed 1 8.000
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Fitting Response Curve and Surfaces – example –32 factorial design
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We may have a nuisance factor presented in a factorial design
Original two factor factorial model:
Blocking in a Factorial Design
bj
aiy ijijjiij ,...,2,1
,...,2,1 )(
Two factor factorial design with a block factor model:
nk
bj
ai
y ijkkijjiijk
,...,2,1
,...,2,1
,...2,1
)(
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Blocking in a Factorial Design
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Blocking in a Factorial Design -- example
Response: intensity level Factors: Ground cutter and filter type Block factor: Operator
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Blocking in a Factorial Design -- example
General Linear Model: Intensity versus Clutter, Filter, Blocks
Factor Type Levels ValuesClutter fixed 3 High, Low, MediumFilter fixed 2 1, 2Blocks fixed 4 1, 2, 3, 4
Analysis of Variance for Intensity, using Sequential SS for Tests
Source DF Seq SS Adj SS Seq MS F PClutter 2 335.58 335.58 167.79 15.13 0.000Filter 1 1066.67 1066.67 1066.67 96.19 0.000Clutter*Filter 2 77.08 77.08 38.54 3.48 0.058Blocks 3 402.17 402.17 134.06 12.09 0.000Error 15 166.33 166.33 11.09Total 23 2047.83
S = 3.33000 R-Sq = 91.88% R-Sq(adj) = 87.55%
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Blocking in a Factorial Design -- example
General Linear Model: Intensity versus Clutter, Filter, Blocks
Term Coef SE Coef T PConstant 94.9167 0.6797 139.64 0.000Clutter High 4.3333 0.9613 4.51 0.000 Low -4.7917 0.9613 -4.98 0.000Filter 1 6.6667 0.6797 9.81 0.000Clutter*Filter High 1 2.0833 0.9613 2.17 0.047 Low 1 -2.2917 0.9613 -2.38 0.031Blocks 1 0.417 1.177 0.35 0.728 2 1.583 1.177 1.34 0.199 3 4.583 1.177 3.89 0.001