Simulasi Monte Carlo dan Histogram Gabungan

Pemodelan Statistika dan Simulasi - Studi Simulasi Monte Carlo

set.seed(150)
data_asli <- rnorm(n = 1000, mean = 30, sd = 2.5)
rata_rata_sampel <- numeric(50)

for (i in 1:50) {
  sampel_acak <- sample(data_asli, size = 100, replace = FALSE)
  rata_rata_sampel[i] <- mean(sampel_acak)
}

print(rata_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

par(mfrow = c(1, 2))

hist(data_asli, 
     col = "lightblue", 
     border = "white",
     main = "Distribusi Data Asli\n(N = 1000)", 
     xlab = "Nilai Observasi", 
     ylab = "Frekuensi")

abline(v = mean(data_asli), col = "blue", lwd = 2, lty = 2)

hist(rata_rata_sampel, 
     col = "lightpink", 
     border = "white",
     main = "Distribusi Rata-Rata Sampel\n(N = 50)", 
     xlab = "Nilai Rata-Rata Sampel", 
     ylab = "Frekuensi")

abline(v = mean(rata_rata_sampel), col = "red", lwd = 2, lty = 2)

par(mfrow = c(1, 1))