Design of factorial :
Factors = 3
Levels =2
we confound the three factor interaction abc with the blocks
Block 1: (1) ,ab,ac,bc
Block 2: a,b,c,abc
We confound the two factor interactions ab, ac and bc with the blocks.
Block 1: 1, abc
Block 2: a, bc
Block 3: b ac
Block 4: c ,ab
A<-c(-1,1,-1,1,-1,1,-1,1)
B<-c(-1,-1,1,1,-1,-1,1,1)
AB<-c(1,-1,-1,1,1,-1,-1,1)
C<-c(-1,-1,-1,-1,1,1,1,1)
AC<-c(1,-1,1,-1,-1,1,-1,1)
BC<-c(1,1,-1,-1,-1,-1,1,1)
ABC<-c(-1,1,1,-1,1,-1,-1,1)
obs<-c(22,32,35,55,44,40,60,39)
Data<-data.frame(A,B,AB,C,AC,BC,ABC)
Data
## A B AB C AC BC ABC
## 1 -1 -1 1 -1 1 1 -1
## 2 1 -1 -1 -1 -1 1 1
## 3 -1 1 -1 -1 1 -1 1
## 4 1 1 1 -1 -1 -1 -1
## 5 -1 -1 1 1 -1 -1 1
## 6 1 -1 -1 1 1 -1 -1
## 7 -1 1 -1 1 -1 1 -1
## 8 1 1 1 1 1 1 1
library(DoE.base)
## Warning: package 'DoE.base' was built under R version 4.5.2
## Loading required package: grid
## Loading required package: conf.design
## Warning: package 'conf.design' was built under R version 4.5.2
## Registered S3 method overwritten by 'DoE.base':
## method from
## factorize.factor conf.design
##
## Attaching package: 'DoE.base'
## The following objects are masked from 'package:stats':
##
## aov, lm
## The following object is masked from 'package:graphics':
##
## plot.design
## The following object is masked from 'package:base':
##
## lengths
?halfnormal
## starting httpd help server ...
## done
Model <- lm(obs~A*B*C,data = Data)
coef(Model)
## (Intercept) A B C A:B A:C
## 40.875 0.625 6.375 4.875 -0.875 -6.875
## B:C A:B:C
## -2.625 -3.375
halfnormal(Model)
## no significant effects
As per the above half-normal plot, none of the factors seem to be significant.
Also, as from the plot none of effects are significant thus, our blocks also don’t seem to be significant.