TM.KomstatUAP_Noor kalila Putri Tarna

#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("AirBiasa","AirSoda","AirKelapa"))
data

##        tablet   pelarut  Data
## 1         CDR  AirBiasa 46.18
## 2         CDR   AirSoda 54.54
## 3         CDR AirKelapa 56.63
## 4     Redoxon  AirBiasa 57.69
## 5     Redoxon   AirSoda 71.57
## 6     Redoxon AirKelapa 81.51
## 7 Enervon-C    AirBiasa 48.61
## 8 Enervon-C     AirSoda 51.01
## 9 Enervon-C   AirKelapa 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: AirBiasa 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$pelarut == j]
## W = 0.89987, p-value = 0.3851
## 
## 
## Uji Normalitas untuk pelarut: AirSoda 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$pelarut == j]
## W = 0.87434, p-value = 0.3079
## 
## 
## Uji Normalitas untuk pelarut: AirKelapa 
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Data[data$pelarut == j]
## W = 0.79474, p-value = 0.102

#Homogenitas variansi#
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

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

# 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$Data, A = data$tablet, B = data$pelarut)

##   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 ~ tablet + pelarut, data = data) 
anova(uji_anava_ti)

## 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