# ==========================================================
# TUGAS BAB 4
# UKURAN GEJALA PUSAT, LETAK, PENCARAN,
# KEMIRINGAN DAN KERUNCINGAN
# KELOMPOK 8
# Nama Anggota Kelompok :
#-Fauzi Hanafi (2502056004)
#-Alvin Darul K. (2502056014)
#-M. Azril A. (2502056038)
#-M. Bilal Kenjiro (2502056044)
#-M. Adrianor (2502056047)
# ==========================================================
# ==========================================================
# 1. UKURAN GEJALA PUSAT
# ==========================================================
data1=c(1,2,3,4,5,6,7,8,9,10,11,11,12,13,14,15,16,17,18)
# Jumlah Keseluruhan Nilai
sum(data1)
## [1] 182
# Rata-Rata Aritmatik
mean(data1)
## [1] 9.578947
# Modus
table(data1)
## data1
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
# Median
median(data1)
## [1] 10
# ==========================================================
# 2. UKURAN LETAK
# ==========================================================
# Kuartil
quantile(data1)
## 0% 25% 50% 75% 100%
## 1.0 5.5 10.0 13.5 18.0
# Desil
quantile(data1,
probs=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))
## 10% 20% 30% 40% 50% 60% 70% 80% 90%
## 2.8 4.6 6.4 8.2 10.0 11.0 12.6 14.4 16.2
# ==========================================================
# 3. UKURAN PENCARAN ATAU DISPERSI ATAU SEBARAN
# ==========================================================
data2=c(
10,20,30,40,50,
10,30,30,40,50,
10,30,30,40,50,
20,30,30,50
)
data2
## [1] 10 20 30 40 50 10 30 30 40 50 10 30 30 40 50 20 30 30 50
# Nilai Minimum
min(data2)
## [1] 10
# Nilai Maksimum
max(data2)
## [1] 50
# Range
max(data2)-min(data2)
## [1] 40
# Variance
var(data2)
## [1] 180.7018
# Standar Deviasi
sd(data2)
## [1] 13.44254
# ==========================================================
# 4. UKURAN KEMIRINGAN (SKEWNESS)
# ==========================================================
data1=c(1,2,3,4,5,6,7,8,9,10,11,11,12,13,14,15,16,17,18)
data1
## [1] 1 2 3 4 5 6 7 8 9 10 11 11 12 13 14 15 16 17 18
# ==========================================================
# 5. UKURAN KERUNCINGAN (KURTOSIS)
# ==========================================================
# Perhitungan skewness dan kurtosis akan diperoleh
# pada bagian aplikasi R menggunakan package psych.
# ==========================================================
# APLIKASI DALAM R
# DATA 4.1
# ==========================================================
data4.1=data.frame(
data1=c(
1,2,3,4,5,6,7,8,9,10,
11,11,12,13,14,15,16,17,18
),
data2=c(
10,10,10,20,20,30,30,30,30,30,
30,40,40,40,40,50,50,50,50
),
data3=c(
1,2,2,3,3,3,3,3,4,4,
4,5,5,6,6,NA,NA,NA,NA
)
)
write.csv(data4.1,
"data4.1.csv",
row.names=FALSE)
simpan=read.table("data4.1.csv",
header=TRUE,
sep=",")
simpan
## data1 data2 data3
## 1 1 10 1
## 2 2 10 2
## 3 3 10 2
## 4 4 20 3
## 5 5 20 3
## 6 6 30 3
## 7 7 30 3
## 8 8 30 3
## 9 9 30 4
## 10 10 30 4
## 11 11 30 4
## 12 11 40 5
## 13 12 40 5
## 14 13 40 6
## 15 14 40 6
## 16 15 50 NA
## 17 16 50 NA
## 18 17 50 NA
## 19 18 50 NA
data_1=simpan$data1
data_11=na.omit(data_1)
data_2=simpan$data2
data_21=na.omit(data_2)
data_3=simpan$data3
data_31=na.omit(data_3)
data_1
## [1] 1 2 3 4 5 6 7 8 9 10 11 11 12 13 14 15 16 17 18
data_11
## [1] 1 2 3 4 5 6 7 8 9 10 11 11 12 13 14 15 16 17 18
data_2
## [1] 10 10 10 20 20 30 30 30 30 30 30 40 40 40 40 50 50 50 50
data_21
## [1] 10 10 10 20 20 30 30 30 30 30 30 40 40 40 40 50 50 50 50
data_3
## [1] 1 2 2 3 3 3 3 3 4 4 4 5 5 6 6 NA NA NA NA
data_31
## [1] 1 2 2 3 3 3 3 3 4 4 4 5 5 6 6
## attr(,"na.action")
## [1] 16 17 18 19
## attr(,"class")
## [1] "omit"
# ==========================================================
# MENGGUNAKAN PACKAGE PSYCH
# ==========================================================
library(psych)
describe(data_11)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 19 9.58 5.2 10 9.59 5.93 1 18 17 -0.04 -1.32 1.19
describe(data_21)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 19 32.11 13.57 30 32.35 14.83 10 50 40 -0.23 -1.16 3.11
describe(data_31)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 15 3.6 1.45 3 3.62 1.48 1 6 5 0.14 -1.01 0.38
# ==========================================================
# MENGGUNAKAN PACKAGE PASTECS
# ==========================================================
library(pastecs)
stat.desc(data_11)
## nbr.val nbr.null nbr.na min max range
## 19.0000000 0.0000000 0.0000000 1.0000000 18.0000000 17.0000000
## sum median mean SE.mean CI.mean.0.95 var
## 182.0000000 10.0000000 9.5789474 1.1928535 2.5060921 27.0350877
## std.dev coef.var
## 5.1995276 0.5428078
stat.desc(data_21)
## nbr.val nbr.null nbr.na min max range
## 19.0000000 0.0000000 0.0000000 10.0000000 50.0000000 40.0000000
## sum median mean SE.mean CI.mean.0.95 var
## 610.0000000 30.0000000 32.1052632 3.1137262 6.5416960 184.2105263
## std.dev coef.var
## 13.5724179 0.4227474
stat.desc(data_31)
## x
## nbr.val 15.0000000
## nbr.null 0.0000000
## nbr.na 0.0000000
## min 1.0000000
## max 6.0000000
## range 5.0000000
## sum 54.0000000
## median 3.0000000
## mean 3.6000000
## SE.mean 0.3754363
## CI.mean.0.95 0.8052307
## var 2.1142857
## std.dev 1.4540584
## coef.var 0.4039051
# ==========================================================
# APLIKASI DALAM R (DATA BERKELOMPOK)
# DATA 4.2
# ==========================================================
data4.2=data.frame(
jurusan.angka=c(
1,1,1,1,1,1,
2,2,2,2,2,2
),
jurusan.label=c(
"matematika","matematika","matematika",
"matematika","matematika","matematika",
"statistika","statistika","statistika",
"statistika","statistika","statistika"
),
jenis.angka=c(
1,1,1,2,2,2,
1,1,1,2,2,2
),
jenis.label=c(
"laki-laki","laki-laki","laki-laki",
"perempuan","perempuan","perempuan",
"laki-laki","laki-laki","laki-laki",
"perempuan","perempuan","perempuan"
),
IQ=c(
101,104,102,111,101,105,
104,103,104,102,101,101
),
EQ=c(
102,104,104,105,106,102,
101,112,104,101,102,104
)
)
write.csv(data4.2,
"data4.2.csv",
row.names=FALSE)
simpan=read.table("data4.2.csv",
header=TRUE,
sep=",")
simpan
## jurusan.angka jurusan.label jenis.angka jenis.label IQ EQ
## 1 1 matematika 1 laki-laki 101 102
## 2 1 matematika 1 laki-laki 104 104
## 3 1 matematika 1 laki-laki 102 104
## 4 1 matematika 2 perempuan 111 105
## 5 1 matematika 2 perempuan 101 106
## 6 1 matematika 2 perempuan 105 102
## 7 2 statistika 1 laki-laki 104 101
## 8 2 statistika 1 laki-laki 103 112
## 9 2 statistika 1 laki-laki 104 104
## 10 2 statistika 2 perempuan 102 101
## 11 2 statistika 2 perempuan 101 102
## 12 2 statistika 2 perempuan 101 104
# ==========================================================
# MENGGUNAKAN PACKAGE PSYCH
# ==========================================================
library(psych)
describeBy(simpan$IQ,
simpan$jurusan.angka)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 104 3.79 103 104 2.97 101 111 10 0.86 -0.93 1.55
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 102.5 1.38 102.5 102.5 2.22 101 104 3 0 -2.06 0.56
describeBy(simpan$IQ,
simpan$jurusan.label)
##
## Descriptive statistics by group
## group: matematika
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 104 3.79 103 104 2.97 101 111 10 0.86 -0.93 1.55
## ------------------------------------------------------------
## group: statistika
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 102.5 1.38 102.5 102.5 2.22 101 104 3 0 -2.06 0.56
describeBy(simpan$EQ,
simpan$jurusan.angka)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 103.83 1.6 104 103.83 2.22 102 106 4 -0.02 -1.82 0.65
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 104 4.15 103 104 2.22 101 112 11 1.05 -0.59 1.69
describeBy(simpan$EQ,
simpan$jurusan.label)
##
## Descriptive statistics by group
## group: matematika
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 103.83 1.6 104 103.83 2.22 102 106 4 -0.02 -1.82 0.65
## ------------------------------------------------------------
## group: statistika
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 104 4.15 103 104 2.22 101 112 11 1.05 -0.59 1.69
# ==========================================================
# MENGGUNAKAN PACKAGE DOBY
# ==========================================================
library(doBy)
summaryBy(
IQ + EQ ~ jurusan.label + jenis.label,
data=simpan,
FUN=function(x) {
c(
ratarata=mean(x),
standar_deviasi=sd(x),
jumlah=sum(x)
)
}
)
## jurusan.label jenis.label IQ.ratarata IQ.standar_deviasi IQ.jumlah
## 1 matematika laki-laki 102.3333 1.5275252 307
## 2 matematika perempuan 105.6667 5.0332230 317
## 3 statistika laki-laki 103.6667 0.5773503 311
## 4 statistika perempuan 101.3333 0.5773503 304
## EQ.ratarata EQ.standar_deviasi EQ.jumlah
## 1 103.3333 1.154701 310
## 2 104.3333 2.081666 313
## 3 105.6667 5.686241 317
## 4 102.3333 1.527525 307