awsd

WEEK 6
# SOAL 1
data<-Nikkei
data
lowganjil <- numeric()
highganjil <- numeric()
closegenap <- numeric()
opengenap <- numeric()
a <- 1
b <- 1
for(i in 1:500){
  if(i%%2==0){
    closegenap[a] = Nikkei$Close[i]
    opengenap[a] = Nikkei$Open[i]
    a = a+1
  }else{
    lowganjil[b] = Nikkei$Low[i]
    highganjil[b] = Nikkei$High[i]
    b = b+1
  }
}
  # 1.1
t <- t.test(lowganjil, highganjil, sigma.x=sd(data$Low), sigma.y=sd(data$High))
t

  # 1.2
z <- z.test(closegenap, opengenap, sigma.x=sd(data$Close), sigma.y=sd(data$Open))
z



# SOAL 2
  # 2.1
install.packages(mtcars)
data2 <- mtcars
View(mtcars)
  # 2.3
dratprima <- numeric()
dratnonprima <- numeric()
wtprima <- numeric()
wtnonprima <- numeric()
a <- 1
b <- 1

for(i in 1:32){
    if(i == 1){
      dratnonprima[b] = data2$drat[i]
      wtnonprima[b] = data2$wt[i]
      b = b + 1
    }else{
      if( i %% 2 == 0 || i %% 3 == 0 || i %% 5 == 0 || i %% 7 == 0 ){
        if(i == 2 || i == 3 || i == 5 || i == 7){
          dratprima[a] = data2$drat[i]
          wtprima[a] = data2$wt[i]
          a = a + 1
        }else{
          dratnonprima[b] = data2$drat[i]
          wtnonprima[b] = data2$wt[i]
          b = b + 1
        }
      }
      else{
        dratprima[a] = data2$drat[i]
        wtprima[a] = data2$wt[i]
        a = a + 1
      }
    }
}

  # 2.2
dataframe21 <- data.frame(dratprima, wtprima)
dataframe21
dataframe22 <- data.frame(dratnonprima, wtnonprima)
dataframe22

  # 2.4
t <- t.test(dataframe21$dratprima, dataframe21$wtprima, sigma.x=sd(dataframe21$dratprima, sigma.y=sd(dataframe21$wtprima)))
t

z <- z.test(dataframe22$dratnonprima, dataframe22$wtnonprima, sigma.x=sd(dataframe22$dratnonprima), sigma.y=sd(dataframe22$wtnonprima))
z

# SOAL 3
data3 <- airquality
View(airquality)
a <- 1
b <- 1
ozone <- numeric()
solar <- numeric()
wind <- numeric()
temp <- numeric()
for(i in 1:153){
  if(i<=50){
    ozone[a]=data3$Ozone[i]
    solar[a]=data3$Solar.R[i]
    a = a+1
  } else if(i>=104){
    wind[b]=data3$Wind[i]
    temp[b]=data3$Temp[i]
    b=b+1
  }else{}
}
t.test(ozone, solar, sigma.x=sd(data3$Ozone), sigma.y=sd(data3$Solar.R))

z.test(wind, temp, sigma.x=sd(data3$Wind), sigma.y=sd(data3$Temp))

# SOAL 4
set.seed(500)
datarandom <- data.frame(replicate(1, sample(1:100, 500, rep = TRUE)))
datarandom
data4 <- datarandom[1:250, 1]
data4
data5 <- datarandom[251:500, 1]
data5
t.test(data4, data5, sigma.x=sd(data4), sigma.y=sd(data5))
z.test(data4, data5, sigma.x=sd(data4), sigma.y=sd(data5))

# SOAL 5
data6 <- 100
fibo <- numeric(data6)
fibo[1] <- 1
fibo[2] <- 1
for(i in 3:data6){
  fibo[i] <- fibo[i-1] + fibo[i-2]
}
set.seed(100)
datarandom2 <- data.frame(replicate(1, sample(1:100, 100, rep = TRUE)))
datarandom2
data7 <- data.frame(fibo, datarandom2)
data7
data8 <- data7[1:50, 1:2]
colnames(data8) <- c("fibonacci", "random")
data8
data9 <- data7[51:100, 1:2]
colnames(data9) <- c("fibonacci", "random")
data9

t.test(data8$fibonacci, data8$random, sigma.x=sd(data8$fibonacci), sigma.y=sd(data8$random))
z.test(data8$fibonacci, data8$random, sigma.x=sd(data8$fibonacci), sigma.y=sd(data8$random))
t.test(data9$fibonacci, data9$random, sigma.x=sd(data9$fibonacci), sigma.y=sd(data9$random))
z.test(data9$fibonacci, data9$random, sigma.x=sd(data9$fibonacci), sigma.y=sd(data9$random))










WEEK 5
a <- c(70,85,76,90,85,60,93,80)

View(rivers)
data <- rivers



#T-Test no. 1
hasil <- (((mean(data)-600)*sqrt(141)/sd(data)))
hasil

View(Orange)
data1 <- Orange

mean(Orange$circumference)



#Z-Test no. 2
z <- ((mean(data1$circumference)-116)/(sd(data1$circumference)/sqrt(35)))
z

#Ho = hipotesa awal = <10.000
#Ha = hipotesa alternatif (ekspektasi) = >10.000

#Jika Alpha > Z-Test, maka Ha ditolak Ho diterima
#kesimpulan: Perusahaan dapat memakai bola lampu lebih dari 10.000 jam

#Jika Alpha < Z-Test, maka Ho ditolak Ha diterima
#kesimpulan: Perusahaan dapat memakai bola lampu kurang dari 10.000 jam



#No. 3
alpha <- qnorm(1-.05)
alpha
-alpha

ztest <- (9900-10000)/(120/30)
ztest

ztest <- (9900-10000)/(120/sqrt(30))
ztest

a = -z
a

ztest
#Jadi Ha ditolak, dan Ho diterima



#No. 4a
b <- c(170,156,183,156,187,167,163,179,187,167,174,156,
       174,179,159,179,187,179,183,170,179,156,170,174,179)
mean4 <- mean(b)
mean4

#T-Test
t4 <- ((mean(b)-170)*(sqrt(25)/sd(b)))
t4

#Z-Test
z4 <- ((mean(b)-170)-(sd(b)/sqrt(25)))
z4

pval <- 2*pt(-abs(t4), df = 25-1)
pval

#No. 4b
#tvalue = 1.71
#kesimpulan Ha diterima dan Ho ditolak



#no. 5
q <- qnorm(1-.05)
a
-a

x <- ((2.1-2)/(0.25/sqrt(35)))
pval1 <- pnorm(x)
pval1
x.alpha <- qnorm(1-.05)
x.alpha

w = -z
w
#Alpha < Z, maka Ha diterima, Ho ditolak












WEEK 4
mean1 <- mean(FilePendukung$Umur)

med1 <-median(FilePendukung$Umur)

modus1 <- getmode(FilePendukung$Umur)
getmode <- function(v) {
  uniqv <- unique(v)
  uniqv[which.max(tabulate(match(v, uniqv)))]
}


hasil <- getmode(FilePendukung$Umur)
hasil

max1 <- max(FilePendukung$Umur)

min1 <- min(FilePendukung$Umur)

range1 <- range(FilePendukung$Umur)

var1 <- var(FilePendukung$Umur)

sd1 <- sd(FilePendukung$Umur)

quan1 <- quantile(FilePendukung$Umur)

DataBar1 <- c(mean1, med1, modus1, max1, min1, range1, var1, sd1, quan1)
barplot(DataBar1, main = "Barplot Kolom Umur", col = c("tomato", "sienna1", "yellow2", "olivedrab2", "deepskyblue1"))





mean2 <- mean(FilePendukung$Suku)
mean3 <- mean(FilePendukung$Pendidikan)

med2 <- median(FilePendukung$Suku)
med3 <- median(FilePendukung$Pendidikan)

modus2 <- getmode(FilePendukung$Suku)
getmode <- function(v) {
  uniqv <- unique(v)
  uniqv[which.max(tabulate(match(v, uniqv)))]
}


hasill <- getmode(FilePendukung$Suku)
hasill
modus3 <- getmode(FilePendukung$Pendidikan)
getmode <- function(v) {
  uniqv <- unique(v)
  uniqv[which.max(tabulate(match(v, uniqv)))]
}


hasilll <- getmode(FilePendukung$Pendidikan)
hasilll


DataBar2 <- c(mean2, med2, modus2)
DataBar3 <- c(mean3, med3, modus3)
barplot(DataBar2, main = "Barplot Kolom Suku", col = c("orangered", "orange", "yellow2", "olivedrab3"))
barplot(DataBar3, main = "Barplot Kolom Pendidikan", col = c("gold", "goldenrod1", "goldenrod"))






a <- quantile(FilePendukung$JenisKelamin, c(.61))
b <- quantile(FilePendukung$Status, c(.61))
c <- quantile(FilePendukung$StatusRumah, c(.61))
d <- quantile(FilePendukung$Suku, c(.61))
e <- quantile(FilePendukung$Pendidikan, c(.61))
f <- quantile(FilePendukung$Pekerjaan, c(.61))
g <- quantile(FilePendukung$Umur, c(.61))
h <- quantile(FilePendukung$PengeluaranPerBulan, c(.61))
i <- quantile(FilePendukung$Minat, c(.61))

DataBar4 <- c(a, b, c, d, e, f, g, h, i)
plot(DataBar4, pch = c(1:9), main = "Plot Lines File Pendukung")
lines(DataBar4, col = "darkslateblue")