1. The document provides steps to test for multicollinearity in SPSS, including entering data into Excel, defining variables in SPSS, running a linear regression analysis, and interpreting the output. 2. It shows how to request estimates, covariance matrix, model fit, and collinearity diagnostics in the linear regression output. 3. The output includes an ANOVA table, coefficients table, and collinearity statistics, which can be used to check for multicollinearity between the independent variables.

1. Uji Multikolinearitas
Ikuti langkah berikut ini
With Pdt Christofel Hukubun

2. Mengisi data ke Excel untuk Jumlah
Setelah mengisi data ditek
dari A - AC, Klik AutoNum
hasilnya
Jumlah
Nilai

3. with Pdt Chris Hukubun Descriptives 3
Kliik Varibel
View
Ketik Variabel
Y, X1, X2, X3
Decimal (0)

4. Masukan data jumlah Variabel Y,
X1, X2, X3 ke SPSS 20
with Pdt Chris Hukubun Descriptives 4
Klik Data View

5. Klik : Analyze > Regretion > Linier

6. Akan muncul tabel Linier Regretion
Klik Statistic
Ctrl + A Pindahkan Y ke
Dependen, X1, X2, X3
ke Independen

7. Akan muncul
(centang Estimates, Covariance matrix, Mode Fit,
Collonearooty diagnostics)
Klik Continiue

8. Muncul
Klik Ok

9. Hasil
ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1
Regression 17200.761 3 5733.587 155.230 .000b
Residual 6316.073 171 36.936
Total 23516.834 174
a. Dependent Variable: Y
b. Predictors: (Constant), X3, X2, X1

10. Hasil
Coefficientsa
Model Unstandardized
Coefficients
Standardi
zed
Coefficien
ts
t Sig. Collinearity
Statistics
B Std. Error Beta Toleran
ce
VIF
1
(Constan
t) 35.464 3.636 9.752 .000
X1 2.614 4.553 1.088 .574 .567 .000 2285.3
31
X2 .990 1.226 .410 .808 .420 .006 164.02
7
X3 -1.548 4.372 -.642 -.354 .724 .000 2094.2
45
a. Dependent Variable: Y

11. Hasil
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 35.464 3.636 9.752 .000
X1 2.614 4.553 1.088 .574 .567 .000 2285.331
X2 .990 1.226 .410 .808 .420 .006 164.027
X3 -1.548 4.372 -.642 -.354 .724 .000 2094.245
a. Dependent Variable: Y