where factors= 3
levels = 2
Replicate run = 1
Effect ABC
Block 1 ( Negative ABC) - [(1), ab, ac, bc ]
Block 2 (Positive ABC) - [a, b, c , abc ]
If the Design is to be run in 4 blocks then the confounded blocks
will be
Effect AB , AC & BC.
Block #1 [(1), abc]
Block#2 [(a), bc]
Block#3 [(b), ac]
Block#4 [(c), ac]
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)
tl<-c(22,32,35,55,44,40,60,39)
Data<-data.frame(A,B,AB,C,AC,BC,ABC)
#Half Normal Plot:
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
mod <- lm(tl~A*B*C,data = Data)
coef(mod)
## (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(mod)
## no significant effects
#As per the half normal plot none of the factors appears to be
significant
at alpha = 0.05
# The cofficient interaction ABC is -3.375 and since abc is not significant the block is also not significant