> ss=paste(\'s\',1:15,sep=\'\') > iv=c(rep(\'A\',5),rep(\'B\',5),rep(\'c\',5)) >
ID: 2907537 • Letter: #
Question
> ss=paste('s',1:15,sep='')
> iv=c(rep('A',5),rep('B',5),rep('c',5))
> dv=c(2,4,3,2,4,6,4,5,5,7,8,6,8,9,7)
> d=data.frame(ss,iv,dv)
> d
ss iv dv
1 s1 A 2
2 s2 A 4
3 s3 A 3
4 s4 A 2
5 s5 A 4
6 s6 B 6
7 s7 B 4
8 s8 B 5
9 s9 B 5
10 s10 B 7
11 s11 c 8
12 s12 c 6
13 s13 c 8
14 s14 c 9
15 s15 c 7
> model=aov(dv~iv,d)
> summary(model)
Df Sum Sq Mean Sq F value Pr(>F)
iv 2 52.93 26.47 22.06 9.57e-05 ***
Residuals 12 14.40 1.20
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> model.tables(model,'mean')
Tables of means
Grand mean
5.333333
iv
iv
A B c
3.0 5.4 7.6
How many participants are represented by the data?
How many conditions were tested?
What conclusion can be made regarding the effect of the treatment condition on the measured outcome?
Explanation / Answer
How many participants are represented by the data?
df_total = n-1 = 2 +12 = 14
hence n = 15
total 15 participants
What conclusion can be made regarding the effect of the treatment condition on the measured outcome?
since p-value < 0.05
we reject the null and conclude that there is significant difference
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