Data yang digunakan :
## # A tibble: 457 × 19
## `Nama Dosen` Y1 Y2 X1 X2 X3 X4 X5_1 `Jenis Kelamin` X6_1
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Prof. Dr. S… 68 4 61 37 10 23 0 1 0
## 2 Prof. Drs. S… 101 6 50 26 23 50 0 1 0
## 3 Prof.Dr. Dar… 109 5 56 29 37 88 0 1 0
## 4 Dr.rer.nat. … 92 5 50 26 18 53 0 1 1
## 5 Dr. Yono Had… 39 2 47 24 7 14 0 1 1
## 6 Dr. Mashuri,… 8 1 47 22 2 14 0 1 1
## 7 Prof.Dr. Bag… 6 2 54 29 9 12 0 1 0
## 8 Drs. Zaenal … 1 1 51 25 2 6 0 1 0
## 9 Dr. M. Zainu… 3 1 52 26 8 17 0 1 0
## 10 Drs. Yoyok C… 6 1 53 26 1 5 0 1 0
## # ℹ 447 more rows
## # ℹ 9 more variables: X6_2 <dbl>, X6_3 <dbl>, `Jabatan Fungsional` <chr>,
## # X7_1 <dbl>, `Pendidikan terakhir` <chr>, X8_1 <dbl>,
## # `Tempat pendidikan terakhir` <chr>, Jurusan <chr>, Fakultas <chr>
Hasil Regresi
summary (regresi)
##
## Call:
## lm(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.68 -20.16 -3.79 15.84 321.34
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -26.6168 17.1349 -1.553 0.121
## Y2 26.5627 1.1950 22.227 <2e-16 ***
## X1 0.5184 0.6079 0.853 0.394
## X2 -0.8324 0.6109 -1.363 0.174
## X3 -0.2081 0.3887 -0.535 0.593
## X4 0.2441 0.2870 0.851 0.395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.24 on 451 degrees of freedom
## Multiple R-squared: 0.6915, Adjusted R-squared: 0.6881
## F-statistic: 202.2 on 5 and 451 DF, p-value: < 2.2e-16
Hasil Regresi Kuantil
summary(k1)
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
##
## Call: rq(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, tau = c(0.05, 0.1,
## 0.25, 0.5, 0.75, 0.9), data = data)
##
## tau: [1] 0.05
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) -5.85682 -8.34470 0.67108
## Y2 5.94164 2.92192 8.99536
## X1 0.01952 -0.28194 0.10485
## X2 -0.03830 -0.12537 0.29563
## X3 0.10466 -0.42623 0.50319
## X4 0.05281 -0.04834 0.19517
##
## Call: rq(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, tau = c(0.05, 0.1,
## 0.25, 0.5, 0.75, 0.9), data = data)
##
## tau: [1] 0.1
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) -6.92716 -8.91074 -0.92155
## Y2 6.89140 4.20259 9.09762
## X1 0.02130 -0.22798 0.18381
## X2 -0.03366 -0.21020 0.18763
## X3 0.14383 -0.05607 0.52514
## X4 0.04759 -0.05603 0.11361
##
## Call: rq(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, tau = c(0.05, 0.1,
## 0.25, 0.5, 0.75, 0.9), data = data)
##
## tau: [1] 0.25
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) -7.01840 -12.52741 -1.20448
## Y2 7.96178 5.45742 10.08627
## X1 0.09200 -0.10584 0.27879
## X2 -0.18896 -0.41495 -0.00088
## X3 0.43949 -0.02047 0.67794
## X4 -0.00920 -0.10444 0.19154
##
## Call: rq(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, tau = c(0.05, 0.1,
## 0.25, 0.5, 0.75, 0.9), data = data)
##
## tau: [1] 0.5
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) -7.14985 -13.59491 3.27988
## Y2 10.37077 8.48625 12.89670
## X1 0.20181 -0.15516 0.36910
## X2 -0.23782 -0.44348 0.08730
## X3 0.94403 0.13761 1.19630
## X4 -0.21620 -0.46387 0.19465
##
## Call: rq(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, tau = c(0.05, 0.1,
## 0.25, 0.5, 0.75, 0.9), data = data)
##
## tau: [1] 0.75
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) -0.52604 -1.06703 -0.19888
## Y2 20.97117 18.75919 23.21102
## X1 0.01468 0.00248 0.03365
## X2 -0.01676 -0.03705 -0.00300
## X3 0.22190 0.01918 0.80380
## X4 -0.04404 -0.13498 0.00306
##
## Call: rq(formula = Y1 ~ Y2 + X1 + X2 + X3 + X4, tau = c(0.05, 0.1,
## 0.25, 0.5, 0.75, 0.9), data = data)
##
## tau: [1] 0.9
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 0.00000 0.00000 0.00000
## Y2 35.33333 31.73780 37.59672
## X1 0.00000 0.00000 0.00000
## X2 0.00000 0.00000 0.00000
## X3 0.00000 -0.92205 1.70846
## X4 0.00000 0.00000 0.00000