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Uts No 3

1. The document presents the results of a multiple linear regression analysis with variables Y, X1, and X2. 2. Significance tests were conducted to analyze the relationships between the individual variables and their combined effect on Y. The tests showed that X1 and X2 significantly influence Y both individually and together. 3. Coefficients for the regression equation were estimated, and significance tests found that both X1 and X2 significantly contribute to the regression model in predicting Y.

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Chyta Anindhyta
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44 views3 pages

Uts No 3

1. The document presents the results of a multiple linear regression analysis with variables Y, X1, and X2. 2. Significance tests were conducted to analyze the relationships between the individual variables and their combined effect on Y. The tests showed that X1 and X2 significantly influence Y both individually and together. 3. Coefficients for the regression equation were estimated, and significance tests found that both X1 and X2 significantly contribute to the regression model in predicting Y.

Uploaded by

Chyta Anindhyta
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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1.

Estimasi persamaan regresi ganda


HASIL DENGAN EXCEL
Coefficie Standard Lower Upper Lower Upper
  nts Error t Stat P-value 95% 95% 95,0% 95,0%
3,2101 0,0017 2,02434 8,58154
Intercept 5,302945 1,65192 71 99 2 9 2,024342 8,581549
X
Variable 2,1088 0,0375 0,00869 0,28693
1 0,147816 0,070094 21 36 9 4 0,008699 0,286934
X
Variable 7,0903 2,17E- 0,35837 0,63699
2 0,497687 0,070193 09 10 4 9 0,358374 0,636999

0,1478 0,49768
Ŷ = 5,302945 + 16 X1 + 7 X2

HASIL DENGAN SPSS


Coefficientsa

Unstandardized Standardized 95,0% Confidence


Coefficients Coefficients Interval for B Correlations

Std. Lower Upper Zero-


Model B Error Beta t Sig. Bound Bound order Partial Part

1 (Constant) 5.303 1.652 3.210 .002 2.024 8.582

X1 .148 .070 .171 2.109 .038 .009 .287 .333 .209 .164

X2 .498 .070 .574 7.090 .000 .358 .637 .623 .584 .551

a. Dependent Variable: Y

Ŷ = 5,303 + 0,148 X1 + 0,498 X2

2. Uji signifikansi regresi ganda Y atas X1, X2, dan X1

Y atas X1

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 104.698 1 104.698 12.243 .001b

Residual 838.052 98 8.552

Total 942.750 99

a. Dependent Variable: Y
b. Predictors: (Constant), X1
Y atas X2

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 365.467 1 365.467 62.042 .000b

Residual 577.283 98 5.891

Total 942.750 99

a. Dependent Variable: Y
b. Predictors: (Constant), X2

X1 atas X2

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 100.710 1 100.710 8.521 .004b

Residual 1158.200 98 11.818

Total 1258.910 99

a. Dependent Variable: X1
b. Predictors: (Constant), X2

3. Uji signifikansi koefisien korelasi persamaan regresi ganda

Model Summary

Std. Error of Change Statistics

R Adjusted R the R Square F Sig. F


Model R Square Square Estimate Change Change df1 df2 Change

1 .644a .415 .402 2.385 .415 34.336 2 97 .000

a. Predictors: (Constant), X2, X1

Nilai signifikan < 0,000, sedangkan r = 0,644 (korelasi kuat), artinya terdapat korelasi
antar X1 dan X2 secara bersama-sama terhadap Y
4. Uji signifikansi koefisien persamaan regresi ganda

Coefficientsa

Unstandardized Standardized 95,0% Confidence


Coefficients Coefficients Interval for B Correlations

Std. Lower Upper Zero-


Model B Error Beta t Sig. Bound Bound order Partial Part

1 (Constant) 5.303 1.652 3.210 .002 2.024 8.582

X1 .148 .070 .171 2.109 .038 .009 .287 .333 .209 .164

X2 .498 .070 .574 7.090 .000 .358 .637 .623 .584 .551

a. Dependent Variable: Y

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