\[Y_{ijk}=\mu + \alpha_i+\beta_{j(i)} + \epsilon_{ijk}\] where,
\(\alpha_i\)= Factor A(Machine) with 3 levels “i”
\(\beta_{j(i)}\)= Factor B(Spindle) with 2 eves “j”
\(\epsilon_{ijk}\)= Standard Error Term
library(GAD)
## Loading required package: matrixStats
## Loading required package: R.methodsS3
## R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.
Mchn <- c(rep(1,8),rep(2,8),rep(3,8))
Spndl <- rep(c(rep(1,4),rep(2,4)),3)
obs <- c(12,9,11,12,8,9,10,8,14,15,13,14,12,10,11,13,14,10,12,11,16,15,15,14)
dat <- data.frame(Mchn,Spndl,obs)
dat
## Mchn Spndl obs
## 1 1 1 12
## 2 1 1 9
## 3 1 1 11
## 4 1 1 12
## 5 1 2 8
## 6 1 2 9
## 7 1 2 10
## 8 1 2 8
## 9 2 1 14
## 10 2 1 15
## 11 2 1 13
## 12 2 1 14
## 13 2 2 12
## 14 2 2 10
## 15 2 2 11
## 16 2 2 13
## 17 3 1 14
## 18 3 1 10
## 19 3 1 12
## 20 3 1 11
## 21 3 2 16
## 22 3 2 15
## 23 3 2 15
## 24 3 2 14
Null Hypothesis: \[H_o:{\sigma_\beta}^2=0\]
Alternate Hypothesis: \[H_a:{\sigma_\beta}^2\not=0\]
Null Hypothesis: \[\alpha_i=0\]
Alternate Hypothesis: \[\alpha_i\not=0\]
Mchn <- as.fixed(Mchn)
Spndl <- as.random(Spndl)
Test<- lm(obs~Mchn+Spndl%in%Mchn)
gad(Test)
## Analysis of Variance Table
##
## Response: obs
## Df Sum Sq Mean Sq F value Pr(>F)
## Mchn 2 55.75 27.8750 1.9114 0.2915630
## Mchn:Spndl 3 43.75 14.5833 9.9057 0.0004428 ***
## Residual 18 26.50 1.4722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(Test)