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dataset.df <- read.csv("~/Downloads/insurance.csv")
head(dataset.df)
##   age    sex    bmi children smoker    region   charges
## 1  19 female 27.900        0    yes southwest 16884.924
## 2  18   male 33.770        1     no southeast  1725.552
## 3  28   male 33.000        3     no southeast  4449.462
## 4  33   male 22.705        0     no northwest 21984.471
## 5  32   male 28.880        0     no northwest  3866.855
## 6  31 female 25.740        0     no southeast  3756.622
dataset.df <- dataset.df[, c("age", "sex", "bmi")]
dataset.df$baru <- ""
head(dataset.df)
##   age    sex    bmi baru
## 1  19 female 27.900     
## 2  18   male 33.770     
## 3  28   male 33.000     
## 4  33   male 22.705     
## 5  32   male 28.880     
## 6  31 female 25.740
dataset.df["bmi.category"] <- ""
head(dataset.df)
##   age    sex    bmi baru bmi.category
## 1  19 female 27.900                  
## 2  18   male 33.770                  
## 3  28   male 33.000                  
## 4  33   male 22.705                  
## 5  32   male 28.880                  
## 6  31 female 25.740
dataset.df["bmi.category"][dataset.df["bmi"]<18.5] <-"underweight"
dataset.df["bmi.category"][dataset.df["bmi"]>25] <-"overweight"
dataset.df["bmi.category"][dataset.df["bmi.category"]==""] <-"normal"
head(dataset.df)
##   age    sex    bmi baru bmi.category
## 1  19 female 27.900        overweight
## 2  18   male 33.770        overweight
## 3  28   male 33.000        overweight
## 4  33   male 22.705            normal
## 5  32   male 28.880        overweight
## 6  31 female 25.740        overweight
# Seed NRP
set.seed(1401201013)

# Fungsi transformasi invers
inverse_transform <- function(u) {
  x <- sqrt(u) / 2  # Transformasi invers untuk distribusi
  return(x)
}

# Membangkitkan 100 bilangan acak dari distribusi yang diberikan
n <- 100
u <- runif(n)  # Menghasilkan bilangan acak dari distribusi uniform antara 0 dan 1
x <- inverse_transform(u)  # Menggunakan fungsi transformasi invers

# Menampilkan hasil
print(x)
##   [1] 0.23837782 0.22408760 0.41743818 0.46394599 0.24456634 0.20877029
##   [7] 0.40260467 0.40731262 0.48611461 0.34946510 0.37071738 0.48152118
##  [13] 0.47026279 0.48348461 0.45990564 0.42146701 0.43050028 0.10635673
##  [19] 0.09021058 0.12438700 0.34204711 0.36538578 0.35569679 0.25891932
##  [25] 0.36611472 0.38702364 0.41015091 0.28336083 0.16968212 0.22642998
##  [31] 0.44920482 0.25887918 0.39053169 0.39065805 0.41584031 0.18138768
##  [37] 0.13771744 0.39511760 0.43873135 0.15161733 0.47509139 0.25957338
##  [43] 0.44032754 0.35201947 0.16749887 0.49311016 0.20802335 0.33102195
##  [49] 0.14068956 0.24250389 0.23516928 0.13003087 0.38980098 0.43776503
##  [55] 0.35437419 0.32502097 0.10780763 0.37784595 0.48691775 0.33329417
##  [61] 0.32455020 0.44971666 0.48312020 0.37936080 0.38261042 0.39907373
##  [67] 0.35145617 0.47561225 0.45271416 0.28697602 0.27101216 0.28856327
##  [73] 0.47590908 0.33552513 0.34119980 0.46643986 0.35574033 0.30065685
##  [79] 0.29548165 0.47794518 0.45755151 0.30029124 0.48479847 0.35119158
##  [85] 0.25964965 0.10781457 0.44483256 0.46098567 0.41610944 0.43146407
##  [91] 0.40027459 0.25173810 0.37839949 0.24394049 0.43427865 0.48747257
##  [97] 0.43745916 0.47654470 0.39203360 0.30490075
# Membuat grafik distribusi empiris
hist(x, breaks = 20, prob = TRUE, main = "Distribusi Empiris", xlab = "Nilai X")
curve(12*x^2*(1-x), add = TRUE, col = "red", lwd = 2)