Y : Keputusan menolak/menerima pelamar kerja pada PT A posisi B X1 : Lama pengalaman kerja sebelumnya (bulan) X2 : Status pekerjaan saat ini (0: Bekerja, 1: Tidak bekerja) X3 : Tingkat pendidikan (0: Lulusah Sekolah Menengah, 1: Lulusan Perguruan Tinggi) X4 : IPK (skala 4)
X1 : Lama pengalaman kerja sebelumnya (bulan) Membangkitkan variabel X1 dengan lama pekerjaan 0-60 bulan dengan nilai tengah 12 dan banyak pelamar adalah 100
set.seed(447)
n <- 100
u <- runif(n)
x1 <- round(60*(-(log(1-u)/12)))
x1## [1] 2 6 5 6 4 3 1 1 1 19 8 9 1 2 5 0 6 1 4 6 2 1 6 1 1
## [26] 0 0 4 2 7 8 2 2 3 11 1 11 2 1 2 1 2 4 10 5 7 10 7 5 1
## [51] 2 28 1 4 12 17 6 0 7 7 4 7 5 1 1 5 12 0 7 0 2 1 1 1 1
## [76] 7 3 2 11 2 6 3 5 3 8 1 2 7 5 4 5 13 3 1 3 1 7 3 3 3X2 : Status pekerjaan Keterangan yang digunakan (0=Tidak Bekerja) dan (1=Bekerja)
set.seed(217)
x2 <- round(runif(n))
x2## [1] 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1
## [38] 0 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 1 0 0
## [75] 0 0 0 0 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 0X3 : Tingkat pendidikan Keterangan yang digunakan (0=lulus SMA/Tidak kuliah) dan (1=lulus kuliah)
set.seed(427)
x3 <- round(runif(n))
x3## [1] 0 1 0 0 1 1 1 0 0 0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1
## [38] 1 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 1 1 1
## [75] 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0X4 adalah data IPK Pelamar dengan skala 4
set.seed(123)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 2.72 2.88 3.78 3.04 3.06 3.86 3.23 2.37 2.66 2.78 3.61 3.18 3.20 3.06 2.72
## [16] 3.89 3.25 2.02 3.35 2.76 2.47 2.89 2.49 2.64 2.69 2.16 3.42 3.08 2.43 3.63
## [31] 3.21 2.85 3.45 3.44 3.41 3.34 3.28 2.97 2.85 2.81 2.65 2.90 2.37 4.08 3.60
## [46] 2.44 2.80 2.77 3.39 2.96 3.13 2.99 2.98 3.68 2.89 3.76 2.23 3.29 3.06 3.11
## [61] 3.19 2.75 2.83 2.49 2.46 3.15 3.22 3.03 3.46 4.03 2.75 1.85 3.50 2.65 2.66
## [76] 3.51 2.86 2.39 3.09 2.93 3.00 3.19 2.81 3.32 2.89 3.17 3.55 3.22 2.84 3.57
## [91] 3.50 3.27 3.12 2.69 3.68 2.70 4.09 3.77 2.88 2.49set.seed(222)
x44 <- round(rnorm(n,2.7,0.5),2)
x44## [1] 3.44 2.70 3.39 2.51 2.79 2.58 2.09 3.48 2.91 2.10 3.23 2.05 2.35 3.00 2.60
## [16] 2.11 1.70 2.70 2.96 2.33 3.06 3.06 2.37 3.45 1.98 1.62 2.90 2.50 2.55 3.37
## [31] 2.29 3.04 2.59 2.64 2.60 2.90 3.03 2.75 2.61 3.17 2.80 2.95 2.42 3.26 3.80
## [46] 2.86 2.23 3.11 2.51 2.87 3.00 2.96 2.22 2.09 2.60 3.23 2.89 3.32 2.86 2.18
## [61] 2.13 3.32 3.09 3.07 2.73 3.12 2.80 3.43 2.47 1.31 2.73 2.67 2.11 1.44 3.11
## [76] 2.83 2.67 3.04 2.71 2.97 3.04 2.10 2.08 2.81 1.97 2.64 2.97 3.06 1.91 3.25
## [91] 2.53 3.01 2.95 3.54 2.89 2.82 2.91 2.11 2.38 2.73summary(x44)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.310 2.410 2.795 2.711 3.040 3.800b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 2.2set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 1.984 19.036 14.816 17.188 12.432 11.192 2.806 -1.786 -1.148 61.616
## [11] 27.642 27.996 2.740 5.432 15.184 -2.442 20.350 -0.356 13.070 19.272
## [21] 4.134 1.558 15.978 -1.192 1.618 -5.748 -3.476 10.276 1.346 21.986
## [31] 27.262 5.470 4.090 9.768 35.002 0.348 37.916 5.234 1.470 2.682
## [41] 1.530 5.580 10.914 36.176 17.620 19.368 33.360 19.594 14.458 -0.488
## [51] 5.586 96.778 2.256 11.096 37.858 59.972 18.106 -1.062 23.432 23.042
## [61] 10.518 22.750 13.226 0.678 -1.588 16.130 38.584 -4.334 21.612 0.566
## [71] 5.250 -0.230 2.900 1.030 -1.648 21.222 8.492 3.958 36.998 5.646
## [81] 19.300 6.518 15.382 7.304 26.558 2.674 6.510 20.584 13.248 10.854
## [91] 14.700 41.694 6.364 -1.082 10.796 1.140 22.498 10.494 6.336 4.978p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.879106919 0.999999995 0.999999632 0.999999966 0.999996011 0.999986216
## [7] 0.942999193 0.143563836 0.240854581 1.000000000 1.000000000 1.000000000
## [13] 0.939346097 0.995644713 0.999999746 0.080025546 0.999999999 0.411928197
## [19] 0.999997892 0.999999996 0.984233877 0.826066179 0.999999885 0.232901428
## [25] 0.834519121 0.003179014 0.030002872 0.999965551 0.793474908 1.000000000
## [31] 1.000000000 0.995806428 0.983536355 0.999942749 1.000000000 0.586132500
## [37] 1.000000000 0.994696127 0.813057386 0.935956113 0.822006314 0.996241613
## [43] 0.999981799 1.000000000 0.999999978 0.999999996 1.000000000 0.999999997
## [49] 0.999999474 0.380364830 0.996264012 1.000000000 0.905166828 0.999984827
## [55] 1.000000000 1.000000000 0.999999986 0.256927438 1.000000000 1.000000000
## [61] 0.999972956 1.000000000 0.999998197 0.663292172 0.169665469 0.999999901
## [67] 1.000000000 0.012945206 1.000000000 0.637839685 0.994779874 0.442752145
## [73] 0.947846437 0.736915896 0.161379439 0.999999999 0.999794939 0.981256742
## [79] 1.000000000 0.996480813 0.999999996 0.998525558 0.999999791 0.999327610
## [85] 1.000000000 0.935474899 0.998513733 0.999999999 0.999998236 0.999980673
## [91] 0.999999587 1.000000000 0.998280499 0.253127722 0.999979519 0.757679639
## [97] 1.000000000 0.999972299 0.998231759 0.993159293set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1
## [38] 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1
## [75] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 0 0 2.72
## 2 1 6 0 1 2.88
## 3 1 5 0 0 3.78
## 4 1 6 1 0 3.04
## 5 1 4 0 1 3.06
## 6 1 3 1 1 3.86
## 7 1 1 1 1 3.23
## 8 0 1 1 0 2.37
## 9 0 1 1 0 2.66
## 10 1 19 0 0 2.78
## 11 1 8 0 1 3.61
## 12 1 9 1 0 3.18
## 13 1 1 1 1 3.20
## 14 1 2 0 1 3.06
## 15 1 5 0 1 2.72
## 16 0 0 0 0 3.89
## 17 1 6 1 1 3.25
## 18 1 1 0 1 2.02
## 19 1 4 0 1 3.35
## 20 1 6 1 1 2.76
## 21 1 2 0 1 2.47
## 22 1 1 0 1 2.89
## 23 1 6 1 0 2.49
## 24 1 1 1 0 2.64
## 25 1 1 1 1 2.69
## 26 0 0 1 0 2.16
## 27 0 0 0 0 3.42
## 28 1 4 1 0 3.08
## 29 1 2 0 0 2.43
## 30 1 7 1 0 3.63
## 31 1 8 1 1 3.21
## 32 1 2 1 1 2.85
## 33 1 2 1 0 3.45
## 34 1 3 0 1 3.44
## 35 1 11 0 0 3.41
## 36 1 1 1 0 3.34
## 37 1 11 1 1 3.28
## 38 1 2 0 1 2.97
## 39 0 1 0 1 2.85
## 40 1 2 1 0 2.81
## 41 1 1 1 1 2.65
## 42 1 2 1 1 2.90
## 43 1 4 0 1 2.37
## 44 1 10 1 1 4.08
## 45 1 5 1 1 3.60
## 46 1 7 1 0 2.44
## 47 1 10 1 1 2.80
## 48 1 7 0 0 2.77
## 49 1 5 1 0 3.39
## 50 0 1 1 0 2.96
## 51 1 2 0 1 3.13
## 52 1 28 1 1 2.99
## 53 1 1 1 1 2.98
## 54 1 4 0 0 3.68
## 55 1 12 1 0 2.89
## 56 1 17 1 1 3.76
## 57 1 6 1 1 2.23
## 58 0 0 0 1 3.29
## 59 1 7 1 1 3.06
## 60 1 7 0 1 3.11
## 61 1 4 1 0 3.19
## 62 1 7 1 1 2.75
## 63 1 5 1 0 2.83
## 64 1 1 0 1 2.49
## 65 0 1 1 0 2.46
## 66 1 5 0 1 3.15
## 67 1 12 1 0 3.22
## 68 0 0 0 0 3.03
## 69 1 7 1 0 3.46
## 70 1 0 0 1 4.03
## 71 1 2 1 1 2.75
## 72 0 1 1 1 1.85
## 73 1 1 0 1 3.50
## 74 1 1 0 1 2.65
## 75 0 1 0 0 2.66
## 76 1 7 0 0 3.51
## 77 1 3 0 1 2.86
## 78 1 2 0 1 2.39
## 79 1 11 0 1 3.09
## 80 1 2 1 1 2.93
## 81 1 6 0 1 3.00
## 82 1 3 0 0 3.19
## 83 1 5 0 1 2.81
## 84 1 3 1 0 3.32
## 85 1 8 1 1 2.89
## 86 1 1 1 1 3.17
## 87 1 2 0 1 3.55
## 88 1 7 0 0 3.22
## 89 1 5 1 0 2.84
## 90 1 4 0 0 3.57
## 91 1 5 1 0 3.50
## 92 1 13 0 0 3.27
## 93 1 3 0 0 3.12
## 94 1 1 1 0 2.69
## 95 1 3 1 1 3.68
## 96 1 1 0 1 2.70
## 97 1 7 0 0 4.09
## 98 1 3 0 1 3.77
## 99 1 3 1 0 2.88
## 100 1 3 0 0 2.49modelreglog <- glm(y~x1+x2+x3+x4, family = binomial(link = "logit"), data=datagab)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(modelreglog)##
## Call:
## glm(formula = y ~ x1 + x2 + x3 + x4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -16.9249 6.8875 -2.457 0.0140 *
## x1 5.9382 2.5365 2.341 0.0192 *
## x2 0.6319 1.4786 0.427 0.6691
## x3 3.3549 1.5380 2.181 0.0292 *
## x4 3.6201 1.6520 2.191 0.0284 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 73.385 on 99 degrees of freedom
## Residual deviance: 19.478 on 95 degrees of freedom
## AIC: 29.478
##
## Number of Fisher Scoring iterations: 11