PRAKTIKUM KOMPUTASI STATISTIKA PERT 4

#DISTRIBUSI PELUANG KONTINU

norm<-function(x, miu, sigma){1/(sigma*sqrt(2*pi))*exp(-1/2*((x-miu)/sigma)^2)}
norm(750, 800, 50)

## [1] 0.004839414

dnorm(750, mean=800, sd= 50)

## [1] 0.004839414

DISTRIBUSI EKSPONENSIAL

eks <- function(x, lambda) {
  lambda * exp((-1) * lambda * x)
}

eks(15, 0.05)

## [1] 0.02361833

# Untuk P(X < 15)
cdfexp <- function(x, lambda = 0.05) {
  pdf <- function(t) {
    lambda * exp(-lambda * t)
  }
  
  hasil <- integrate(pdf, lower = 0, upper = x)
  return(hasil$value)
}

cdfexp(15, 0.05)

## [1] 0.5276334

#DISTRIBUSI UNIFORM

uniform<-function(x,a=0,b=1){1/(b-a)}
uniform(20,a=10, b=30)

## [1] 0.05

dunif(20, min=10,max=30)

## [1] 0.05

#POSTTEST #DISTRIBUSI EKPONENSIAL

eks <- function(x, lambda) {
  lambda * exp((-1) * lambda * x)
}

eks(4, 0.1667)

## [1] 0.08557522

# Peluang pelayanan selesai paling lama 4 menit
cdfexp <- function(x, lambda = 0.1667) {
  pdf <- function(t) {
    lambda * exp(-lambda * t)
  }
  
  hasil <- integrate(pdf, lower = 0, upper = x)
  return(hasil$value)
}

cdfexp(4, 0.1667)

## [1] 0.4866513

DISTRIBUSI UNIFORM

uniform <- function(x, a = 2, b = 10) {
  1/(b-a)
}

uniform(6, a = 2, b = 10)

## [1] 0.125

dunif(6, min = 2, max = 10)

## [1] 0.125

DISTRIBUSI PELUANG KONTINU

norm <- function(x, miu, sigma) {
  1/(sigma*sqrt(2*pi))*exp(-1/2*((x-miu)/sigma)^2)
}

norm(4, 5, 1)

## [1] 0.2419707

dnorm(4, mean = 5, sd = 1)

## [1] 0.2419707