Email             :
RPubs            : https://rpubs.com/Calvinriswandy/
Jurusan          : Statistika
Address         : ARA Center, Matana University Tower
                         Jl. CBD Barat Kav, RT.1, Curug Sangereng, Kelapa Dua, Tangerang, Banten 15810.


1 Integral Tak Tentu dan Tentu

1.1 Integral Tentu

F <- function(x) x^2 + 3*x + 4
integrate(F,1,3)
## 28.66667 with absolute error < 3.2e-13

1.2 Integral Tak Tentu

library(mosaicCalc)
## Loading required package: mosaicCore
## Registered S3 method overwritten by 'mosaic':
##   method                           from   
##   fortify.SpatialPolygonsDataFrame ggplot2
## 
## Attaching package: 'mosaicCalc'
## The following object is masked from 'package:stats':
## 
##     D
F = antiD(x^2 + 3*x + 4 ~ x)
F
## function (x, C = 0) 
## 1/3 * x^3 + 3/2 * x^2 + 4 * x + C

2 Luas Lingkaran, Keliling Lingkaran, dan, Volume Bola.

2.1 Luas Lingkaran

Luas_Lingkaran <- function(pi,r)                # Nama fungsi dan argumen
{                                               # Pembukaan fungsi
  Luas = pi*r^2                                 # Menghitung luas lingkaran
  return(cat("Luas:", Luas))
}                                               # Penutupan fungsi

Luas_Lingkaran(22/7,14)                          # Menggunakan fungsi
## Luas: 616

2.2 Keliling Lingkaran

Keliling_Lingkaran <- function(pi,r)            # Nama fungsi dan argumen
{                                               # Pembukaan fungsi
  Keliling = 2*pi*r                              # Menghitung keliling lingkaran
  return(cat("Keliling:", Keliling))
}                                               # Penutupan fungsi

Keliling_Lingkaran(22/7,14)                      # Menggunakan fungsi
## Keliling: 88

2.3 Volume Bola

Volume_bola <- function(pi,r)                   # Nama fungsi dan argumen
{                                               # Pembukaan fungsi
  Volume = round(4/3*pi*r^3 , digits =2)        # Menghitung volume lingkaran
  return(cat("Volume:", Volume))
}                                               # Penutupan fungsi

Volume_bola(22/7,14)                             # Menggunakan fungsi
## Volume: 11498.67

3 Nilai Maksimum, Minimum, Rata-rata, Median, Mode, Variansi, Standard Deviasi pada data berfrekuensi.

# Membuat tabel frekuensi
Tinggi = c(140,142,143,144,145,146,147)

Fr = c(12,5,2,3,4,2,5)
frekuensi = round(Fr, digit = 0)

# Convert tabel ke dataframe
Im = data.frame(Tinggi,
                frekuensi)

Im
##   Tinggi frekuensi
## 1    140        12
## 2    142         5
## 3    143         2
## 4    144         3
## 5    145         4
## 6    146         2
## 7    147         5

3.1 nilai maksimum

data = function(x, Fr)
{
  min = min(x)
  max = max(x)
  average = round(sum(x*Fr)/sum(Fr), digits = 1)
  
  return(cat(c("maksimum =", max)))
}

data(Im$Tinggi, Im$frekuensi)
## maksimum = 147

3.2 nilai minimum

data = function(x, Fr)
{
  min = min(x)
  max = max(x)
  average = round(sum(x*Fr)/sum(Fr), digits = 1)
  
  return(cat(c("manimum =", min)))
}

data(Im$Tinggi, Im$frekuensi)
## manimum = 140

3.3 average

data = function(x, Fr)
{
  min = min(x)
  max = max(x)
  average = round(sum(x*Fr)/sum(Fr), digits = 1)
  
  return(cat(c("average =", average)))
}

data(Im$Tinggi, Im$frekuensi)
## average = 142.9

3.4 Median

data = function(x, Fr)
{
  median = median(x)
  
  return(cat(c("median =", median)))
}

data(Im$Tinggi, Im$frekuensi)
## median = 144

3.5 modus

data = function(x, Fr)
{
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}

data(Im$Tinggi, Im$Frekuensi)
## [1] 140

3.6 Variansi dan Standar Deviasi

vari = sample(c(140:147, replace = TRUE))
table(vari)
## vari
##   1 140 141 142 143 144 145 146 147 
##   1   1   1   1   1   1   1   1   1
var(vari)
## [1] 2261.5
sd(vari)
## [1] 47.55523
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