TUGAS MASUK 3

# 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

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

# Pengecekan Normalitas 
shapiro.test(data$data[data$tablet == "CDR"])

## 
##  Shapiro-Wilk normality test
## 
## data:  data$data[data$tablet == "CDR"]
## W = 0.89286, p-value = 0.3631

shapiro.test(data$data[data$tablet == "Redoxon"])

## 
##  Shapiro-Wilk normality test
## 
## data:  data$data[data$tablet == "Redoxon"]
## W = 0.99096, p-value = 0.8182

shapiro.test(data$data[data$tablet == "Enervon-C"])

## 
##  Shapiro-Wilk normality test
## 
## data:  data$data[data$tablet == "Enervon-C"]
## W = 0.80695, p-value = 0.1312

shapiro.test(data$data[data$pelarut == "Air Biasa"])

## 
##  Shapiro-Wilk normality test
## 
## data:  data$data[data$pelarut == "Air Biasa"]
## W = 0.89987, p-value = 0.3851

shapiro.test(data$data[data$pelarut == "Air Soda"])

## 
##  Shapiro-Wilk normality test
## 
## data:  data$data[data$pelarut == "Air Soda"]
## W = 0.87434, p-value = 0.3079

shapiro.test(data$data[data$pelarut == "Air Kelapa"])

## 
##  Shapiro-Wilk normality test
## 
## data:  data$data[data$pelarut == "Air Kelapa"]
## W = 0.79474, p-value = 0.102

#Homogenitas variansi# 
leveneTest(data~tablet,data=data,center=mean)

## Levene's Test for Homogeneity of Variance (center = mean)
##       Df F value Pr(>F)
## group  2  2.4902 0.1632
##        6

leveneTest(data~pelarut,data=data,center=mean)

## Levene's Test for Homogeneity of Variance (center = mean)
##       Df F value Pr(>F)
## group  2  1.9806 0.2185
##        6

Y <- matrix(c(46.18, 54.54, 56.63,
              57.69, 71.57, 81.51,
              48.61, 51.01, 80.02),
            nrow=3, byrow=TRUE,
            dimnames=list(Faktor_Baris=c("CDR","Redoxon","Enervon-C"),
                          Faktor_Kolom=c("Air Biasa","Air Soda","Air Kelapa")))

#ANAVA 2 ARAH TANPA INTERAKSI#
anava2arah_tanpa <- function(Y, alpha=0.05){
  a <- nrow(Y)
  b <- ncol(Y)
  N <- a*b
  FK <- sum(Y)^2/N
  JKT <- sum(Y^2) - FK
  JK_A <- sum(rowSums(Y)^2)/b - FK
  JK_B <- sum(colSums(Y)^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)
  Ft <- c(qf(1-alpha, db[1:2], db[3]), NA, NA)
  p <- c(pf(F[1:2], db[1:2], db[3], lower.tail=FALSE), NA, NA)
  hasil <- data.frame(Sumber=c("Faktor Baris","Faktor Kolom","Galat","Total"),
                      db, JK, KT, F_hit=F, F_tabel=Ft, p_value=p)
  print(hasil, digits=5, na.print="", row.names=FALSE)
}
anava2arah_tanpa(Y)

##        Sumber db      JK      KT  F_hit F_tabel  p_value
##  Faktor Baris  2  479.96 239.979 4.2696  6.9443 0.101762
##  Faktor Kolom  2  733.92 366.960 6.5287  6.9443 0.054991
##         Galat  4  224.83  56.207     NA      NA       NA
##         Total  8 1438.70 179.838     NA      NA       NA

#PEMBANDING DENGAN FUNGSI BAWAAN R#
data <- as.data.frame(as.table(Y))
anova(aov(Freq ~ Faktor_Baris + Faktor_Kolom, data=data))

## Analysis of Variance Table
## 
## Response: Freq
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## Faktor_Baris  2 479.96  239.98  4.2696 0.10176  
## Faktor_Kolom  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