#PACKAGES
library(car)
## Loading required package: carData
library(agricolae)
#Memanggil Data
data = read.table(file.choose(), header = T)
data
## MerekTablet JenisPelarut Data
## 1 1 1 46.18
## 2 1 2 54.54
## 3 1 3 56.63
## 4 2 1 57.69
## 5 2 2 71.57
## 6 2 3 81.51
## 7 3 1 48.61
## 8 3 2 51.01
## 9 3 3 80.02
#Pendefinisian Variabel
data$MerekTablet = factor(data$MerekTablet, levels = 1:3, labels = c("CDR", "Redoxon", "Enervon-C"))
data$JenisPelarut = factor(data$JenisPelarut, levels = 1:3, labels = c("Air Biasa", "Air Soda", "Air Kelapa"))
data
## MerekTablet JenisPelarut Data
## 1 CDR Air Biasa 46.18
## 2 CDR Air Soda 54.54
## 3 CDR Air Kelapa 56.63
## 4 Redoxon Air Biasa 57.69
## 5 Redoxon Air Soda 71.57
## 6 Redoxon Air Kelapa 81.51
## 7 Enervon-C Air Biasa 48.61
## 8 Enervon-C Air Soda 51.01
## 9 Enervon-C Air Kelapa 80.02
# Normalitas per JenisPelarut
for(j in levels(data$JenisPelarut)){
cat("\nUji Normalitas untuk Jenis Pelarut:", j, "\n")
print(shapiro.test(data$Data[data$JenisPelarut == j]))
}
##
## Uji Normalitas untuk Jenis Pelarut: Air Biasa
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$JenisPelarut == j]
## W = 0.89987, p-value = 0.3851
##
##
## Uji Normalitas untuk Jenis Pelarut: Air Soda
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$JenisPelarut == j]
## W = 0.87434, p-value = 0.3079
##
##
## Uji Normalitas untuk Jenis Pelarut: Air Kelapa
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$JenisPelarut == j]
## W = 0.79474, p-value = 0.102
# Normalitas per MerekTablet
for(i in levels(data$MerekTablet)){
cat("\nUji Normalitas untuk Merek Tablet:", i, "\n")
print(shapiro.test(data$Data[data$MerekTablet == i]))
}
##
## Uji Normalitas untuk Merek Tablet: CDR
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$MerekTablet == i]
## W = 0.89286, p-value = 0.3631
##
##
## Uji Normalitas untuk Merek Tablet: Redoxon
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$MerekTablet == i]
## W = 0.99096, p-value = 0.8182
##
##
## Uji Normalitas untuk Merek Tablet: Enervon-C
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$MerekTablet == i]
## W = 0.80695, p-value = 0.1312
#Homogenitas variansi#
leveneTest(Data ~ JenisPelarut, center = mean, data = data)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 2 1.9806 0.2185
## 6
leveneTest(Data ~ MerekTablet, center = mean, data = data)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 2 2.4902 0.1632
## 6
# Uji Anava 2 Arah Tanpa Interaksi
anava2arah_tanpa_interaksi <- function(y, A, B){
a <- nlevels(A)
b <- nlevels(B)
N <- length(y)
n <- N / (a * b)
FK <- sum(y)^2 / N
JKT <- sum(y^2) - FK
JK_A <- sum(tapply(y, A, sum)^2) / (b * n) - FK
JK_B <- sum(tapply(y, B, sum)^2) / (a * n) - FK
JKG <- JKT - JK_A - JK_B
db_A <- a - 1
db_B <- b - 1
db_G <- (a - 1) * (b - 1)
db_T <- N - 1
db <- c(db_A, db_B, db_G, db_T)
JK <- c(JK_A, JK_B, JKG, JKT)
KT <- JK[1:3] / db[1:3]
F_A <- KT[1] / KT[3]
F_B <- KT[2] / KT[3]
F <- c(F_A, F_B, NA, NA)
p_A <- pf(F_A, db[1], db[3], lower.tail = FALSE)
p_B <- pf(F_B, db[2], db[3], lower.tail = FALSE)
p <- c(p_A, p_B, NA, NA)
data.frame(
Sumber_Keragaman = c("Merek Tablet", "Jenis Pelarut", "Galat", "Total"),
db = db,
Jumlah_Kuadrat = JK,
Kuadrat_Tengah = c(KT, NA),
F_hitung = F,
p_value = p
)
}
anava2arah_tanpa_interaksi(y = data$Data, A = data$MerekTablet, B = data$JenisPelarut)
## Sumber_Keragaman db Jumlah_Kuadrat Kuadrat_Tengah F_hitung p_value
## 1 Merek Tablet 2 479.9575 239.97874 4.269572 0.10176166
## 2 Jenis Pelarut 2 733.9193 366.95964 6.528748 0.05499073
## 3 Galat 4 224.8270 56.20674 NA NA
## 4 Total 8 1438.7038 NA NA NA
uji_anava_ti = aov(Data ~ MerekTablet + JenisPelarut, data = data)
anova(uji_anava_ti)
## Analysis of Variance Table
##
## Response: Data
## Df Sum Sq Mean Sq F value Pr(>F)
## MerekTablet 2 479.96 239.98 4.2696 0.10176
## JenisPelarut 2 733.92 366.96 6.5287 0.05499 .
## Residuals 4 224.83 56.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1