t tabel 0331 1833 there are negative effect of family size X 2 toward

T tabel 0331 1833 there are negative effect of family

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≤ t tabel 0,331 ≤ 1,833 there are negative effect of family size (X 2 ) toward expenditure (Y) Ho: Regression model is a good of fit , if F stat > F tabel 2=1 − (∑ ) (?−? ̂ ^2 )/(∑ ̅) (?−? ^2 ) ? ^2=1 − 286,233 / 1664,917 ? ^2= "0,8280799 " ?? =√((∑ ) (?−? ̂ ^2 )/ )) (?−? ?? =√(("286 " ,233 )/(12−3))= 5,64 ?? =√( 〖〗 ?? ^2/ (??? [?] ) )) (??? ?? =√( (5,64) ^2/ 464812 ( 1289732 ))= 9,394 ??2 =√( 5,64 ^2/ 464812 ( 24611 ) )= 1,298 ?? 1=√( 5,64 ^2/464812 (275) )=0,137 ? _ ℎ?????=??/??? ? 1= _? 0,799 / 0,137=5,832 ?_?2=0,430/1,298= 0,331
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Name : Bagas Aji P NIM : C1H018030 Econometrics Assignment Multiple Regression Because of F stat (21,68) > F table (4,256) so equation of regression is good. SUMMARY OUTPUT Regression Statistics Multiple R 0.910087 R Square 0.828258 Adjusted R 0.790093 Standard E 5.63655 Observatio 12 ANOVA df SS MS F ignificance F Regression 2 1378.98 689.4902 21.70208 0.000361 Residual 9 285.9363 31.7707 Total 11 1664.917 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95% Lower 95,0% Upper 95,0% Intercept 8.85657 9.38911 0.943281 0.370168 -12.38307 30.09621 -12.38307 30.09621 X Variable 0.799865 0.137101 5.834126 0.000249 0.489721 1.11001 0.489721 1.11001 X Variable 0.430352 1.296998 0.331807 0.747632 -2.503661 3.364366 -2.503661 3.364366 Ha: Regression model is a bad of fit , if F stat ≤ F tabel ? =(0,8 28 /(3−1))/(1−0, 828 /(12−3)) =21,68 ? = ^2/ −1))/(1 ^2/ )) (? (? −? (?−?
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Name : Bagas Aji P NIM : C1H018030 Econometrics Assignment Multiple Regression Model Method 1 Enter Model Summary Model R R Square F Change df1 df2 Sig. F Change 1 0.828 0.790 5.63655 0.828 21.702 2 9 0.000 Model df Mean Square F Sig. 1 Regression 1378.980 2 689.490 21.702 Residual 285.936 9 31.771 Total 1664.917 11 Variables Entered/Removed a Variables Entered Variables Removed FamilySize, Income b a. Dependent Variable: Expenditure b. All requested variables entered. Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change .910 a a. Predictors: (Constant), FamilySize, Income ANOVA a Sum of Squares .000 b a. Dependent Variable: Expenditure b. Predictors: (Constant), FamilySize, Income
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Name : Bagas Aji P NIM : C1H018030 Econometrics Assignment Multiple Regression Model t Sig. Correlations Beta Lower Bound Upper Bound Zero-order Partial Part 1 (Constant) 8.857 9.389 0.943 0.370 -12.383 30.096 Income 0.800 0.137 0.888 5.834 0.000 0.490 1.110 0.909 0.889 0.806 FamilySize 0.430 1.297 0.050 0.332 0.748 -2.504 3.364 0.423 0.110 0.046 Coefficients a Standardized Coefficients 95,0% Confidence Interval for B a. Dependent Variable: Expenditure
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