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

3. REGRESI DIPERIKSA INTERAKSI
lm(formula = y1 ~ x1 * g, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) e-11 ***
x < 2e-16 ***
g[T.P]
x1:g[T.P]
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 56 degrees of freedom
Multiple R-squared: 0.998, Adjusted R-squared:
F-statistic: on 3 and 56 DF, p-value: < 2.2e-16

4. REGRESI DIPISAH lm(formula = y1 ~ g/x1 - 1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
gL e-11 ***
gP e-09 ***
gL:x < 2e-16 ***
gP:x < 2e-16 ***
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 56 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared:
F-statistic: 2.864e+05 on 4 and 56 DF, p-value: < 2.2e-16

5. REGRESI DIGABUNG lm(formula = y1 ~ x1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) e-15 ***
x < 2e-16 ***
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 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

6. FAKTOR BERPENGARUH TANPA INTERAKSI

7. PERIKSA INTERAKSI lm(formula = y2 ~ x1 * g, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) <2e-16 ***
x <2e-16 ***
g[T.P] <2e-16 ***
x1:g[T.P]
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 56 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: 1.28e+04 on 3 and 56 DF, p-value: < 2.2e-16

8. PAKSA REGRESI BERBEDA lm(formula = y2 ~ g/x1 - 1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
gL <2e-16 ***
gP <2e-16 ***
gL:x <2e-16 ***
gP:x <2e-16 ***
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 56 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: 8.923e+04 on 4 and 56 DF, p-value: < 2.2e-16

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

10. REGRESI DIGABUNG lm(formula = y2 ~ x1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)
x **
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 58 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 1 and 58 DF, p-value:

11. FAKTOR BERINTERAKSI

12. MEMERIKSA INTERAKSI lm(formula = y3 ~ g * x1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) <2e-16 ***
g[T.P] <2e-16 ***
x <2e-16 ***
g[T.P]:x <2e-16 ***
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 56 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 3 and 56 DF, p-value: < 2.2e-16

13. REGRESI DIPISAH lm(formula = y3 ~ g/x1 - 1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
gL <2e-16 ***
gP <2e-16 ***
gL:x <2e-16 ***
gP:x <2e-16 ***
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 56 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 4 and 56 DF, p-value: < 2.2e-16

14. REGRESI PARALEL lm(formula = y3 ~ g + x1 - 1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
gL *
gP ** x
---
Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: on 57 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 3 and 57 DF, p-value: < 2.2e-16

15. REGRESI DIGABUNG lm(formula = y3 ~ x1, data = DataSimReg)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) x
Residual standard error: on 58 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 1 and 58 DF, p-value: