LATIHAN 1

# Menetapkan seed
set.seed(150)

# Membuat 1000 observasi distribusi normal
data_asli <- rnorm(
  n = 1000,
  mean = 30,
  sd = 2.5
)

# Menyimpan rata-rata dari 50 sampel
rata_sampel <- numeric(50)

# Mengambil sampel dan menghitung rata-rata
for(i in 1:50){
  
  sampel <- sample(data_asli,
                   size = 100,
                   replace = FALSE)
  
  rata_sampel[i] <- mean(sampel)
}

# Menampilkan hasil rata-rata sampel
rata_sampel
##  [1] 29.74008 30.16381 29.62791 29.66137 30.08495 30.01583 29.70671 30.05079
##  [9] 30.02960 30.37672 29.76709 30.01157 29.60805 29.72241 30.06345 29.88738
## [17] 30.16121 30.36253 29.16989 30.14413 29.75741 29.38544 29.96614 29.75158
## [25] 29.82847 30.01795 30.19496 30.07933 30.04874 30.26879 29.52677 29.65463
## [33] 30.07876 29.82691 29.49381 29.89400 30.11327 29.70978 29.89127 29.93924
## [41] 29.61428 29.67813 30.21050 29.81615 29.92829 29.97847 29.80795 29.41516
## [49] 30.31301 29.93768
# Statistik ringkas
summary(rata_sampel)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.17   29.71   29.91   29.89   30.07   30.38

LATIHAN 2

# Menampilkan dua histogram dalam satu grafik

par(mfrow = c(1,2))

# Histogram data asli
hist(data_asli,
     main = "Histogram 1000 Observasi",
     xlab = "Nilai",
     ylab = "Frekuensi")

# Histogram rata-rata sampel
hist(rata_sampel,
     main = "Histogram Rata-rata Sampel",
     xlab = "Rata-rata Sampel",
     ylab = "Frekuensi")

# Mengembalikan tampilan grafik
par(mfrow = c(1,1))