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REGRESI DENGAN VARABEL FAKTOR/ KUALLTATIF

REGRESI DENGAN VARABEL FAKTOR/ KUALLTATIF. KASUS I: FAKTOR TIDAK BERPENGARUH. REGRESI DIPERIKSA INTERAKSI. lm(formula = y1 ~ x1 * g, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.00000 1.23576 8.092 5.45e-11 ***

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REGRESI DENGAN VARABEL FAKTOR/ KUALLTATIF

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  1. REGRESI DENGAN VARABEL FAKTOR/ KUALLTATIF KASUS I: FAKTOR TIDAK BERPENGARUH

  2. REGRESI DIPERIKSA INTERAKSI lm(formula = y1 ~ x1 * g, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.00000 1.23576 8.092 5.45e-11 *** x1 3.00000 0.02475 121.230 < 2e-16 *** g[T.P] -0.65146 1.85766 -0.351 0.727 x1:g[T.P] 0.01779 0.03622 0.491 0.625 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.184 on 56 degrees of freedom Multiple R-squared: 0.998, Adjusted R-squared: 0.9979 F-statistic: 9476 on 3 and 56 DF, p-value: < 2.2e-16

  3. REGRESI DIPISAH lm(formula = y1 ~ g/x1 - 1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) gL 10.00000 1.23576 8.092 5.45e-11 *** gP 9.34854 1.38701 6.740 9.30e-09 *** gL:x1 3.00000 0.02475 121.230 < 2e-16 *** gP:x1 3.01779 0.02645 114.099 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.184 on 56 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 0.9999 F-statistic: 2.864e+05 on 4 and 56 DF, p-value: < 2.2e-16

  4. REGRESI DIGABUNG lm(formula = y1 ~ x1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.60337 0.90543 10.61 3.35e-15 *** x1 3.01053 0.01768 170.25 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.173 on 58 degrees of freedom Multiple R-squared: 0.998, Adjusted R-squared: 0.998 F-statistic: 2.899e+04 on 1 and 58 DF, p-value: < 2.2e-16

  5. FAKTOR BERPENGARUH TANPA INTERAKSI

  6. PERIKSA INTERAKSI lm(formula = y2 ~ x1 * g, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 52.38491 2.16798 24.163 <2e-16 *** x1 2.95164 0.04341 67.988 <2e-16 *** g[T.P] -102.82667 3.25903 -31.551 <2e-16 *** x1:g[T.P] 0.05375 0.06354 0.846 0.401 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.078 on 56 degrees of freedom Multiple R-squared: 0.9985, Adjusted R-squared: 0.9985 F-statistic: 1.28e+04 on 3 and 56 DF, p-value: < 2.2e-16

  7. PAKSA REGRESI BERBEDA lm(formula = y2 ~ g/x1 - 1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) gL 52.38491 2.16798 24.16 <2e-16 *** gP -50.44177 2.43334 -20.73 <2e-16 *** gL:x1 2.95164 0.04341 67.99 <2e-16 *** gP:x1 3.00539 0.04640 64.77 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.078 on 56 degrees of freedom Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998 F-statistic: 8.923e+04 on 4 and 56 DF, p-value: < 2.2e-16

  8. REGRESI PARALEL lm(formula = y2 ~ g + x1 - 1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) gL 51.15128 1.60017 31.97 <2e-16 *** gP -48.95706 1.68119 -29.12 <2e-16 *** x1 2.97673 0.03162 94.13 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.072 on 57 degrees of freedom Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998 F-statistic: 1.196e+05 on 3 and 57 DF, p-value: < 2.2e-16

  9. REGRESI DIGABUNG lm(formula = y2 ~ x1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 46.4770 38.8685 1.196 0.23666 x1 2.0778 0.7591 2.737 0.00821 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 50.35 on 58 degrees of freedom Multiple R-squared: 0.1144, Adjusted R-squared: 0.09914 F-statistic: 7.493 on 1 and 58 DF, p-value: 0.008211

  10. FAKTOR BERINTERAKSI

  11. MEMERIKSA INTERAKSI lm(formula = y3 ~ g * x1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 59.16291 2.08027 28.44 <2e-16 *** g[T.P] -125.53198 3.12719 -40.14 <2e-16 *** x1 -1.48226 0.04166 -35.58 <2e-16 *** g[T.P]:x1 3.90656 0.06097 64.07 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.994 on 56 degrees of freedom Multiple R-squared: 0.9977, Adjusted R-squared: 0.9976 F-statistic: 8101 on 3 and 56 DF, p-value: < 2.2e-16

  12. REGRESI DIPISAH lm(formula = y3 ~ g/x1 - 1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) gL 59.16291 2.08027 28.44 <2e-16 *** gP -66.36907 2.33490 -28.43 <2e-16 *** gL:x1 -1.48226 0.04166 -35.58 <2e-16 *** gP:x1 2.42430 0.04452 54.45 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.994 on 56 degrees of freedom Multiple R-squared: 0.9983, Adjusted R-squared: 0.9981 F-statistic: 8028 on 4 and 56 DF, p-value: < 2.2e-16

  13. REGRESI PARALEL lm(formula = y3 ~ g + x1 - 1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) gL -30.4926 13.1515 -2.319 0.02403 * gP 41.5335 13.8174 3.006 0.00393 ** x1 0.3412 0.2599 1.313 0.19446 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 17.03 on 57 degrees of freedom Multiple R-squared: 0.8707, Adjusted R-squared: 0.8639 F-statistic: 127.9 on 3 and 57 DF, p-value: < 2.2e-16

  14. REGRESI DIGABUNG lm(formula = y3 ~ x1, data = DataSimReg) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -27.1295 30.8332 -0.880 0.383 x1 0.9880 0.6022 1.641 0.106 Residual standard error: 39.94 on 58 degrees of freedom Multiple R-squared: 0.04436, Adjusted R-squared: 0.02788 F-statistic: 2.692 on 1 and 58 DF, p-value: 0.1063

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