Answer:
See the explanation for the answer
Step-by-step explanation:
We shall analyse this in the open source statisitcal packageR , the complete R snippet is as follows
# read the data into R dataframe
data.df<- read.csv("C:\\Users\\586645\\Downloads\\Chegg\\syn.csv",header=TRUE)
str(data.df)
# perform anova analysis
a<- aov(lm(time~type*vis,data=data.df))
#summarise the results
summary(a)
colr<-c("salmon3" , "plum2","coral1","palegreen1" ,"orangered" ,"magenta4" )
# plots
boxplot(time~type*vis, data=data.df,ylab="Values",
main="Boxplots of the Data",col=colr,horizontal=TRUE)
attach(data.df)
interaction.plot(type,vis,time, type="b", col=c(2:6),
leg.bty="o", leg.bg="beige", lwd=2, pch=c(18,24,22),
xlab="Type",
ylab="Value",
main="Interaction Plot")
The results are
> summary(a)
Df Sum Sq Mean Sq F value Pr(>F)
type 1 121278 121278 478.18 2.59e-05 *** ### signficant as the p value is less than 0.05
vis 1 9730 9730 38.36 0.00345 ** ### signficant as the p value is less than 0.05
type:vis 1 9316 9316 36.73 0.00374 ** ### signficant as the p value is less than 0.05 , hence the interaction effect is significant . The p values are highlighted
Residuals 4 1015 254
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
