uji regresi linier sederhana x1 terhadap y
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Uji Regresi Linier Sederhana X1 terhadap Y
• Cara kerja klik start > IBM 20, pada variabel view input data atau jika data sudah dimasukan dalam file makaketik file data open X1, Y klik Analyze > Regression > Linear,
• akan muncul Linear Regression maka pindahkanvaiabel Y ke dependent dan vaiabel X1 ke Indendent,
• Klik Statistics akan muncul Linear Regression Statistics, centang Estmates, Model Fit, R squared change > continiu dan centang lagi options akan muncul Linear regretion options centang use probability, centangInclude contant in equation > klik continiu > klik okemaka akan muncul data di bawah ini:
ps Chris Hukubun Uji Regrasi Linier Sederhana 1
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Mengisi data ke Excel untuk Jumlah
Setelah mengisi data ditekdari A-AC, Klik AutoNum
hasilnya
JumlahNilai
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Kliik VaribelView
Ketik VariabelY, X1, X2, X3
Decimal (0)
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Masukan data jumlah Variabel Y, X1, X2, X3 ke SPSS 20
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Klik Data View
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klik Analyze > Regression > Linear,
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Ctrl + A, variabel Y dipindahakan kekolom Dependent
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Variabel Y di kolom Dependent ListVariabel X1 di kolom Independent List
KlikOptions
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CentangTes for
Linearity
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KlikStatistics
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CentangEstimatesModel fit
Klik Continiue
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Klik Oke
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Hasilnya seperti ini:
Variables Entered/Removeda
ModelVariables
Entered
Variables Removed Method
1 X1b . Enter
a. Dependent Variable: Y
b. All requested variables entered.
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Model Summary
ModelR
R Square
Adjusted R Square
Std. Error of the
Estimate
Change Statistics
R Square Change
F Change
df1 df2 Sig. F Change
1 .855a .730 .729 6.056 .730 468.166 1 173 .000
a. Predictors: (Constant), X1
Nilai koefisien determinasi R2 = 730Nilai korelasi R = 855Adjusted R Square = 729
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ANOVAa
(Anlysis Of Variance)
Model Sum of Squares
df Mean Square F Sig.
1
Regression 17171.503 1 17171.503 468.166 .000b
Residual 6345.331 173 36.678
Total 23516.834 174
a. Dependent Variable: Y
b. Predictors: (Constant), X1
Nilai df regresi = 1, regresi sederhana = 0,000
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Coefficientsa
Model Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1(Constant) 35.567 3.575 9.950 .000
X1 2.054 .095 .855 21.637 .000
a. Dependent Variable: Y
Nilai B konstant 35,567Nilai Variabel X1 = 2,054Y = 35,567 + 2,054 X1
Nilai t = 9,950Nilai signifikan 0,000Nilai t X1 = 21,637Signifikan = 0,000
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