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Created by : masagoes82@gmail.com
ESTIMASI PERSAMAAN REGRESI DUA VARIABEL DENGAN SPSS
Langkah-langkahnya sebagai berikut :
1. Buka Program SPSS
2. Masukkan data yang mau dianalisis seperti pada gambar
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3. Klik Tools bar Analyze lalu pilih Regression → Curve Estimation
4. Masukkan variabel Dependen dan Independen, dan Cek list model Linear, Growth dan Exponential
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5. Lihat Output hasil SPSS
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MEMBACA DATA HASIL OUTPUT SPSS
* Curve Estimation.
TSET NEWVAR=NONE.
CURVEFIT
/VARIABLES=Kecemasan WITH Kecerdasan
/CONSTANT
/MODEL=LINEAR GROWTH EXPONENTIAL
/PRINT ANOVA
/PLOT FIT.
Curve Fit
Notes
Output Created 25-Oct-2012 09:03:02
Comments
Input Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 15
Missing Value Handling Definition of Missing User-defined missing values are treated
as missing.
Cases Used Cases with a missing value in any
variable are not used in the analysis.
Syntax CURVEFIT
/VARIABLES=Kecemasan WITH
Kecerdasan
/CONSTANT
/MODEL=LINEAR GROWTH
EXPONENTIAL
/PRINT ANOVA
/PLOT FIT.
Resources Processor Time 00:00:01.279
Elapsed Time 00:00:01.404
Use From First observation
To Last observation
Predict From First Observation following the use period
To Last observation
Time Series Settings (TSET) Amount of Output PRINT = DEFAULT
Saving New Variables NEWVAR = NONE
Maximum Number of Lags in
Autocorrelation or Partial
Autocorrelation Plots
MXAUTO = 16
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Maximum Number of Lags Per
Cross-Correlation Plots MXCROSS = 7
Maximum Number of New
Variables Generated Per
Procedure
MXNEWVAR = 60
Maximum Number of New
Cases Per Procedure MXPREDICT = 1000
Treatment of User-Missing
Values MISSING = EXCLUDE
Confidence Interval
Percentage Value CIN = 95
Tolerance for Entering
Variables in Regression
Equations
TOLER = .0001
Maximum Iterative Parameter
Change CNVERGE = .001
Method of Calculating Std.
Errors for Autocorrelations ACFSE = IND
Length of Seasonal Period Unspecified
Variable Whose Values Label
Observations in Plots Unspecified
Equations Include CONSTANT
[DataSet0]
Model Description
Model Name MOD_1
Dependent Variable 1 Kecemasan
Equation 1 Linear
2 Growtha
3 Exponentiala
Independent Variable Kecerdasan
Constant Included
Variable Whose Values Label Observations in Plots Unspecified
a. The model requires all non-missing values to be positive.
Tabel model Description menggambarkan metode estimasi yang dipakai , yaitu Linear, Growth dan
Exponential.
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Case Processing Summary
N
Total Cases 15
Excluded Casesa 0
Forecasted Cases 0
Newly Created Cases 0
a. Cases with a missing value in any
variable are excluded from the
analysis.
Tabel case processing summary menggambarkan jumlah case atau sampel pengamatan (ada 15 case).
Variable Processing Summary
Variables
Dependent Independent
Kecemasan Kecerdasan
Number of Positive Values 15 15
Number of Zeros 0 0
Number of Negative Values 0 0
Number of Missing Values User-Missing 0 0
System-Missing 0 0
Kecemasan
Model Linear
Model Summary
R R Square
Adjusted R
Square
Std. Error of the
Estimate
.896 .803 .788 2.206
The independent variable is Kecerdasan.
ANOVA
Sum of Squares df Mean Square F Sig.
Regression 257.654 1 257.654 52.932 .000
Residual 63.280 13 4.868
Total 320.933 14
The independent variable is Kecerdasan.
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Coefficients
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
Kecerdasan .560 .077 .896 7.275 .000
(Constant) 33.718 4.846 6.957 .000
Estimasi dengan model linear menunjukkan nilai koeffisien korelasi 0.896 , uji kelinearan nilai Sig (0.000)
< α (0.05) sehingga H0 ditolak. Jadi model linear signifikan. Uji konstanta dan koefisien, koefisien b
memiliki nilai Sig (0.000) < α dan konstanta a memiliki nilai Sig (0.000) < α , sehingga H0 ditolak, jadi baik
konstanta a maupun koefisien b adalah signifikan. Tingkat kepercayaan yang dipakai 0.95 atau α = 0.05
Persamaan model yang terbentuk, Y = 6.957 + 7.275 X
Model Growth
Model Summary
R R Square
Adjusted R
Square
Std. Error of the
Estimate
.888 .789 .773 .034
The independent variable is Kecerdasan.
ANOVA
Sum of Squares df Mean Square F Sig.
Regression .055 1 .055 48.582 .000
Residual .015 13 .001
Total .069 14
The independent variable is Kecerdasan.
Coefficients
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
Kecerdasan .008 .001 .888 6.970 .000
(Constant) 3.718 .074 50.416 .000
The dependent variable is ln(Kecemasan).
Estimasi model linear dengan model Growth menunjukkan nilai koeffisien korelasi 0.888 , uji kelinearan
nilai Sig (0.000) < α (0.05) sehingga H0 ditolak. Jadi model linear signifikan. Uji konstanta dan koefisien,
koefisien b memiliki nilai Sig (0.000) < α dan konstanta a memiliki nilai Sig (0.000) < α , sehingga H0
ditolak, jadi baik konstanta a maupun koefisien b adalah signifikan. Tingkat kepercayaan yang dipakai
0.95 atau α = 0.05
Persamaan model yang terbentuk, ln Y = 50.416+ 6.970 X
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Model Exponential
Model Summary
R R Square
Adjusted R
Square
Std. Error of the
Estimate
.888 .789 .773 .034
The independent variable is Kecerdasan.
ANOVA
Sum of Squares df Mean Square F Sig.
Regression .055 1 .055 48.582 .000
Residual .015 13 .001
Total .069 14
The independent variable is Kecerdasan.
Coefficients
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
Kecerdasan .008 .001 .888 6.970 .000
(Constant) 41.163 3.035 13.562 .000
The dependent variable is ln(Kecemasan).
Estimasi model linear dengan model Exponential menunjukkan nilai koeffisien korelasi 0.888 , uji
kelinearan nilai Sig (0.000) < α (0.05) sehingga H0 ditolak. Jadi model linear signifikan. Uji konstanta dan
koefisien, koefisien b memiliki nilai Sig (0.000) < α dan konstanta a memiliki nilai Sig (0.000) < α ,
sehingga H0 ditolak, jadi baik konstanta a maupun koefisien b adalah signifikan. Tingkat kepercayaan
yang dipakai 0.95 atau α = 0.05
Persamaan model yang terbentuk, ln Y = ln 13.562+ 6.970 X
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Melihat pemaparan ketiga model grafik : Grafik model linear, Model Growth, Model Exponential semua
sesuai untuk digunakan sebagai model estimasi peningkatan kecemasan dalam belajar.
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