A<-c(-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1)
B<-c(-1,-1,1,1,-1,-1,1,1,-1,-1,1,1,-1,-1,1,1)
C<-c(-1,-1,-1,-1,1,1,1,1,-1,-1,-1,-1,1,1,1,1)
D<-c(-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1)
obs<-c(12,18,13,20,17,25,15,25,10,24,13,24,19,21,17,23)
dat1<-data.frame(A,B,C,D,obs)
library(DoE.base)
## Warning: package 'DoE.base' was built under R version 4.2.2
## Loading required package: grid
## Loading required package: conf.design
## 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
model1<-lm(obs~A*B*C*D,data = dat1)

Part A

halfnormal(model1)
## 
## Significant effects (alpha=0.05, Lenth method):
## [1] A     C     A:C:D

Factor A, Factor C and Interaction ACD appear to be significant from the halfnormal plot.

Part B

Hypothesis:

Null Hypothesis: \((\alpha \beta \gamma \theta )_{ijkl} = 0\)

Alternate Hypothesis: \((\alpha \beta \gamma \theta )_{ijkl} \neq 0\)

model2<-aov(obs~A+C+A*C*D,data = dat1)
summary(model2)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## A            1 256.00  256.00 157.538 1.52e-06 ***
## C            1  49.00   49.00  30.154  0.00058 ***
## D            1   2.25    2.25   1.385  0.27314    
## A:C          1   9.00    9.00   5.538  0.04643 *  
## A:D          1   0.25    0.25   0.154  0.70513    
## C:D          1   6.25    6.25   3.846  0.08550 .  
## A:C:D        1  30.25   30.25  18.615  0.00256 ** 
## Residuals    8  13.00    1.63                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

From the analysis we see that D is not significant because its value is greater than alpha hence we reject our null hypothesis.