Transcript Slide 1

Chapter 3
Design & Analysis of Experiments
7E 2009 Montgomery
1
3.7 Sample Size Determination
Text, Section 3.7, pg. 101
• FAQ in designed experiments
• Answer depends on lots of things; including
what type of experiment is being
contemplated, how it will be conducted,
resources, and desired sensitivity
• Sensitivity refers to the difference in means
that the experimenter wishes to detect
• Generally, increasing the number of
replications increases the sensitivity or it
makes it easier to detect small differences in
means
Chapter 3
Design & Analysis of Experiments
7E 2009 Montgomery
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Sample Size Determination
Fixed Effects Case
• Can choose the sample size to detect a specific
difference in means and achieve desired values of
type I and type II errors
• Type I error – reject H0 when it is true ( )
• Type II error – fail to reject H0 when it is false (  )
• Power = 1 - 
• Operating characteristic curves plot  against a
a
parameter  where
n  i2
2 
Chapter 3
i 1
a 2
Design & Analysis of Experiments
7E 2009 Montgomery
3
Sample Size Determination
Fixed Effects Case---use of OC Curves
• The OC curves for the fixed effects model are in the
Appendix, Table V
• A very common way to use these charts is to define a
difference in two means D of interest, then the minimum
value of  2 is
2
nD
 
2a 2
2
• Typically work in term of the ratio of D /  and try values
of n until the desired power is achieved
• Most statistics software packages will perform power and
sample size calculations – see page 103
• There are some other methods discussed in the text
Chapter 3
Design & Analysis of Experiments
7E 2009 Montgomery
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Exemplo
• Suponha que no exemplo taxa de gravação vs potência, com
a=4 níveis de potência, deseja-se rejeitar a hipótese nula com
probabilidade pelo menos 0,90 se quaisquer dois tratamentos
diferem dde pelo menos 75 (u.m.) com nível de significância
0,01.
• Suponha desvio-padrão de 25 (u.m.).
• Assim o valor mínimo de 2 é
2
2
nD
n
(
75
)
2 

 1, ,125n
2
2
2a
2(4)(25)
N=na, n=4 no exemplo considerado.
n
N-a
2

4
12
4,5
2,12
5
16
5,625
2,37
6
20
6,75
2,60
app_A_12a
Sugestão de atividades
• 1,2,4,6,8 a 11, 16, 18 a 20 (quinta edição)
• Ler
Design of Engineering
Experiments
– The Blocking Principle
• Text Reference, Chapter 4
• Blocking and nuisance factors
• The randomized complete block design
or the RCBD
• Extension of the ANOVA to the RCBD
• Other blocking scenarios…Latin square
designs
The Blocking Principle
• Blocking is a technique for dealing with nuisance
factors
• A nuisance factor is a factor that probably has some
effect on the response, but it’s of no interest to the
experimenter…however, the variability it transmits to
the response needs to be minimized
• Typical nuisance factors include batches of raw
material, operators, pieces of test equipment, time
(shifts, days, etc.), different experimental units
• Many industrial experiments involve blocking (or
should)
• Failure to block is a common flaw in designing an
experiment (consequences?)
The Blocking Principle
• If the nuisance variable is known and controllable,
we use blocking
• If the nuisance factor is known and uncontrollable,
sometimes we can use the analysis of covariance
(see Chapter 15) to remove the effect of the nuisance
factor from the analysis
• If the nuisance factor is unknown and
uncontrollable (a “lurking” variable), we hope that
randomization balances out its impact across the
experiment
• Sometimes several sources of variability are
combined in a block, so the block becomes an
aggregate variable
The Hardness Testing Example
• Text reference, pg 121, 122
• We wish to determine whether 4 different tips
produce different (mean) hardness reading on a
Rockwell hardness tester
• Gauge & measurement systems capability studies
are frequent areas for applying DOX
• Assignment of the tips to an experimental unit; that
is, a test coupon
• Structure of a completely randomized experiment
• The test coupons are a source of nuisance
variability
• Alternatively, the experimenter may want to test the
tips across coupons of various hardness levels
• The need for blocking
The Hardness Testing Example
• To conduct this experiment as a RCBD, assign all 4
tips to each coupon
• Each coupon is called a “block”; that is, it’s a more
homogenous experimental unit on which to test the
tips
• Variability between blocks can be large, variability
within a block should be relatively small
• In general, a block is a specific level of the nuisance
factor
• A complete replicate of the basic experiment is
conducted in each block
• A block represents a restriction on randomization
• All runs within a block are randomized
The Hardness Testing Example
• Suppose that we use b = 4 blocks:
• Notice the two-way structure of the experiment
• Once again, we are interested in testing the equality
of treatment means, but now we have to remove the
variability associated with the nuisance factor (the
blocks)
Extension of the ANOVA to the
RCBD
• Suppose that there are a treatments (factor
levels) and b blocks
• A statistical model (effects fixed model) for
the RCBD is
 i  1, 2,..., a
yij     i   j   ij 
 j  1, 2,..., b
• The relevant (fixed effects) hypotheses are
H 0 : 1  2 
 a where i  (1/ b) j 1 (    i   j )    i
b
Extension of the ANOVA to the
RCBD
ANOVA partitioning of total variability:
a
b
 ( y
i 1 j 1
ij
a
b
 y.. )   [( yi.  y.. )  ( y. j  y.. )
2
i 1 j 1
( yij  yi.  y. j  y.. )]2
a
b
i 1
j 1
 b ( yi.  y.. ) 2  a  ( y. j  y.. ) 2
a
b
  ( yij  yi.  y. j  y.. ) 2
i 1 j 1
SST  SSTreatments  SS Blocks  SS E
Extension of the ANOVA to the
RCBD
The degrees of freedom for the sums of squares in
SST  SSTreatments  SSBlocks  SSE
are as follows:
ab  1  a  1  b  1  (a  1)(b  1)
Therefore, ratios of sums of squares to their degrees
of freedom result in mean squares and the ratio of
the mean square for treatments to the error mean
square is an F statistic that can be used to test the
hypothesis of equal treatment means
ANOVA Display for the RCBD
Manual computing (ugh!)…see Equations (4-9) –
(4-12), page 124
Design-Expert analyzes the RCBD
Manual computing:
Vascular Graft Example (pg.
126)
• To conduct this experiment as a RCBD, assign all
4 pressures to each of the 6 batches of resin
• Each batch of resin is called a “block”; that is, it’s a
more homogenous experimental unit on which to
test the extrusion pressures
Vascular Graft Example
Design-Expert Output
dado3=read.table("e:\\dox\\graft3.txt",header=T)
psi=as.factor(dado3$psi)
y=c(dado3$X1,dado3$X2,dado3$X3,dado3$X4,dado3$X5,dado3$X6)
graft3=data.frame(block=gl(6,4),psi,y)
graft3.aov=aov(y~psi+block,data=graft3)
summary(graft3.aov)
Df Sum Sq Mean Sq F value Pr(>F)
psi
3 178.171 59.390
8.1071 0.001916 **
block
5 192.252 38.450
5.2487 0.005532 **
Residuals 15 109.886 7.326
--Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual Analysis for the
Vascular Graft Example
Residual Analysis for the
Vascular Graft Example
Residual Analysis for the
Vascular Graft Example
• Basic residual plots indicate that normality,
constant variance assumptions are satisfied
• No obvious problems with randomization
• No patterns in the residuals vs. block
• Can also plot residuals versus the pressure
(residuals by factor)
• These plots provide more information about the
constant variance assumption, possible outliers