TM-KOMSTAT-4.R

#PACKAGES
library(car)

## Warning: package 'car' was built under R version 4.5.3

## Loading required package: carData

## Warning: package 'carData' was built under R version 4.5.3

library(agricolae)

## Warning: package 'agricolae' was built under R version 4.5.3

#Memanggil Data
data = read.table(file.choose(), header = T)
data

##   Tablet Pelarut  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$Tablet = factor(data$Tablet, levels = 1:3, labels = c("CDR","Redoxon","Enervon-C"))
data$Pelarut = factor(data$Pelarut, levels = 1:3, labels = c("Air Biasa","Air Soda","Air Kelapa"))
data

##      Tablet    Pelarut  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 Tablet
for(i in levels(data$Tablet)){
  cat("\nUji Normalitas untuk Tablet:", i, "\n")
  print(shapiro.test(data$Data[data$Tablet==i]))
}

## 
## Uji Normalitas untuk Tablet: CDR 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$Tablet == i]
## W = 0.89286, p-value = 0.3631
## 
## 
## Uji Normalitas untuk Tablet: Redoxon 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$Tablet == i]
## W = 0.99096, p-value = 0.8182
## 
## 
## Uji Normalitas untuk Tablet: Enervon-C 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$Tablet == i]
## W = 0.80695, p-value = 0.1312

#Normalitas per Pelarut
for(j in levels(data$Pelarut)){
  cat("\nUji Normalitas untuk Pelarut:", j, "\n")
  print(shapiro.test(data$Data[data$Pelarut==j]))
}

## 
## Uji Normalitas untuk Pelarut: Air Biasa 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$Pelarut == j]
## W = 0.89987, p-value = 0.3851
## 
## 
## Uji Normalitas untuk Pelarut: Air Soda 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$Pelarut == j]
## W = 0.87434, p-value = 0.3079
## 
## 
## Uji Normalitas untuk Pelarut: Air Kelapa 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$Pelarut == j]
## W = 0.79474, p-value = 0.102

#Homogenitas variansi
leveneTest(Data~Tablet, 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

leveneTest(Data~Pelarut, 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

#Uji ANOVA
anava2arah_tanpa <- function(y, A, B){
  a <- nlevels(A); b <- nlevels(B)
  N <- length(y)
  
  FK   <- sum(y)^2/N
  JKT  <- sum(y^2) - FK
  JK_A <- sum(tapply(y, A, sum)^2)/b - FK
  JK_B <- sum(tapply(y, B, sum)^2)/a - FK
  JKG  <- JKT - JK_A - JK_B
  
  db <- c(a-1, b-1, (a-1)*(b-1), N-1)
  JK <- c(JK_A, JK_B, JKG, JKT)
  KT <- JK/db
  F  <- c(KT[1:2]/KT[3], NA, NA)
  p  <- c(pf(F[1:2], db[1:2], db[3], lower.tail = FALSE), NA, NA)
  
  data.frame(Sumber = c("Tablet","Pelarut","Galat","Total"), db, JK, KT, F, p)
}
anava2arah_tanpa(data$Tablet, data$Pelarut, y = data$Data)

##    Sumber db        JK        KT        F          p
## 1  Tablet  2  479.9575 239.97874 4.269572 0.10176166
## 2 Pelarut  2  733.9193 366.95964 6.528748 0.05499073
## 3   Galat  4  224.8270  56.20674       NA         NA
## 4   Total  8 1438.7038 179.83797       NA         NA

uji_anava = aov(Data~Tablet + Pelarut, data = data)
anova(uji_anava)

## Analysis of Variance Table
## 
## Response: Data
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## Tablet     2 479.96  239.98  4.2696 0.10176  
## Pelarut    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