#Uji Fisher
data <- matrix(c(8, 2, 3, 7), nrow = 2, byrow = TRUE)
dimnames(data) <- list(Kebiasaan_Belajar = c("Belajar Kelompok", "Belajar Sendiri"), Kelulusan = c("Lulus", "Tidak Lulus"))
data
## Kelulusan
## Kebiasaan_Belajar Lulus Tidak Lulus
## Belajar Kelompok 8 2
## Belajar Sendiri 3 7
fisher.test(data)
##
## Fisher's Exact Test for Count Data
##
## data: data
## p-value = 0.06978
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.8821175 127.0558418
## sample estimates:
## odds ratio
## 8.153063
#Uji Chi-Square
data <- matrix(c(20, 10, 10, 30), nrow = 2, byrow = TRUE)
dimnames(data) <- list(JenisKelamin= c("Laki-laki", "Perempuan"), TempatBelajar=c("Perpustakaan", "Kafe"))
data
## TempatBelajar
## JenisKelamin Perpustakaan Kafe
## Laki-laki 20 10
## Perempuan 10 30
chisq.test(data)
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: data
## X-squared = 10.511, df = 1, p-value = 0.001187
#Uji Median
library(agricolae)
## Warning: package 'agricolae' was built under R version 4.5.3
data = read.table(file.choose(), header = TRUE)
data
## Nilai Kelompok
## 1 70 A
## 2 75 A
## 3 78 A
## 4 80 A
## 5 82 A
## 6 85 A
## 7 88 A
## 8 90 A
## 9 78 A
## 10 84 A
## 11 75 B
## 12 80 B
## 13 82 B
## 14 85 B
## 15 84 B
## 16 88 B
## 17 90 B
## 18 92 B
## 19 80 B
## 20 86 B
Median.test(data$Nilai, data$Kelompok, alpha=0.05, simulate.p.value = TRUE)
##
## The Median Test for data$Nilai ~ data$Kelompok
##
## Chi Square = 0.2 DF = 1 P.Value 0.6547208
## Median = 83
##
## Median r Min Max Q25 Q75
## A 81.0 10 70 90 78.0 84.75
## B 84.5 10 75 92 80.5 87.50
##
## Post Hoc Analysis
##
## Groups according to probability of treatment differences and alpha level.
##
## Treatments with the same letter are not significantly different.
##
## data$Nilai groups
## B 84.5 a
## A 81.0 a