Classical Assumption Test for Regression Analysis

UJI ASUMSI KLASIK
Data :
Adapun data yang digunakan dalam laporan ini adalah data tentang
Pengaruh Hafalan, Bahasa Arab, Waktu, Umur, Jarak terhadap Nilai.
Nilai
(Y)
Hafalan
(X1)
90
100
75
75
75
90
67.5
80
75
70
80
91
86
95
75
85
70
90
100
80.2
98
85
85
80
80
90
90
88
90
90
9
10
10
10
10
7
2
3
3
3
10
8
10
30
10
21
15
2
30
3
30
30
10
30
15
30
6
5
30
1
Bahasa
Arab
(X2)
90
92
86
75
75
90
73.3
80
75
75
95
85
90
75
85
90
70
91
90
80.2
95
100
75
90
80
90
90
80
85
90
Waktu
(X3)
Umur
(X4)
Jarak
(X5)
7
3
6
6
3
6
6
2
3
3
4
2
2
4
2
6
4
3
2.8
3
1.5
3
2
3
3
2.5
3
3
2
6
21
19
22
20
21
21
19
22
23
21
23
21
22
21
21
20
22
20
22
22
20
21
20
20
19
22
21
22
20
20
80
300
175
32
5
9
50
95
120
275
10
30
2
150
12
1
230
100
25
70
3
56
153
372
50
38
300
153
185
235
ANALISIS PROGRAM DI R
A. Memanggil Data di R
> indah<-read.csv(file.choose())
> indah
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
nilai.Y. hafalan.X1. bahasaarab.X2. waktu.X3. umur.X4. jarak.X5.
90.0
9
90.0
7.0
21
80
100.0
10
92.0
3.0
19
300
75.0
10
86.0
6.0
22
175
75.0
10
75.0
6.0
20
32
75.0
10
75.0
3.0
21
5
90.0
7
90.0
6.0
21
9
67.5
2
73.3
6.0
19
50
80.0
3
80.0
2.0
22
95
75.0
3
75.0
3.0
23
120
70.0
3
75.0
3.0
21
275
80.0
10
95.0
4.0
23
10
91.0
8
85.0
2.0
21
30
86.0
10
90.0
2.0
22
2
95.0
30
75.0
4.0
21
150
75.0
10
85.0
2.0
21
12
85.0
21
90.0
6.0
20
1
70.0
15
70.0
4.0
22
230
90.0
2
91.0
3.0
20
100
100.0
30
90.0
2.8
22
25
80.2
3
80.2
3.0
22
70
98.0
30
95.0
1.5
20
3
85.0
30
100.0
3.0
21
56
85.0
10
75.0
2.0
20
153
80.0
30
90.0
3.0
20
372
80.0
15
80.0
3.0
19
50
90.0
30
90.0
2.5
22
38
90.0
6
90.0
3.0
21
300
88.0
5
80.0
3.0
22
153
90.0
30
85.0
2.0
20
185
90.0
1
90.0
6.0
20
235
B. Menentukan Model Regresi
> modelreg<-lm(nilai.Y. ~hafalan.X1. +bahasaarab.X2. +waktu.X3. +
umur.X4. +jarak.X5., data = indah)
> modelreg
Call:
lm(formula = nilai.Y. ~ hafalan.X1. + bahasaarab.X2. + waktu.X3.
+
umur.X4. + jarak.X5., data = indah)
Coefficients:
(Intercept)
umur.X4.
hafalan.X1.
bahasaarab.X2.
waktu.X3.
55.568338
-1.033905
jarak.X5.
0.002195
0.147689
0.605257
C. Menguji Asumsi Klasik
1.
Uji Normalitas
> library(stats)
> shapiro.test(modelreg$residuals)
Shapiro-Wilk normality test
data: modelreg$residuals
W = 0.98536, p-value = 0.9431
2.
Uji Heterokedastisitas
> library(lmtest)
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Warning messages:
1: package ‘lmtest’ was built under R version 3.5.2
2: package ‘zoo’ was built under R version 3.5.2
> bptest(modelreg, studentize = F, data = indah)
Breusch-Pagan test
data: modelreg
BP = 5.3234, df = 5, p-value = 0.3777
3.
Uji Multikolinearitas
> library(car)
Loading required package: carData
Warning messages:
1: package ‘car’ was built under R version 3.5.2
2: package ‘carData’ was built under R version 3.5.2
> vif(modelreg)
hafalan.X1. bahasaarab.X2.
waktu.X3.
1.213393
1.137083
1.097779
umur.X4.
jarak.X5.
1.077106
1.053889
-0.872823
4.
Uji Autokorelasi
> dwtest(modelreg)
Durbin-Watson test
data: modelreg
DW = 2.2654, p-value = 0.7967
alternative hypothesis: true autocorrelation is greater than 0