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: Lulusan 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(1)
n <- 100
u <- runif(n)
x1 <- round(60*(-(log(1-u)/12)))
x1## [1] 2 2 4 12 1 11 14 5 5 0 1 1 6 2 7 3 6 24 2 8 14 1 5 1 2
## [26] 2 0 2 10 2 3 5 3 1 9 6 8 1 6 3 9 5 8 4 4 8 0 3 7 6
## [51] 3 10 3 1 0 1 2 4 5 3 12 2 3 2 5 1 3 7 0 10 2 9 2 2 3
## [76] 11 10 2 8 16 3 6 3 2 7 1 6 1 1 1 1 0 5 10 8 8 3 3 8 5X2 : Status pekerjaan Keterangan yang digunakan (0= Bekerja) dan (1= Tidak bekerja)
set.seed(12)
x2 <- round(runif(n))
x2## [1] 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1
## [38] 1 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 0
## [75] 0 1 1 1 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0X3 : Tingkat pendidikan Keterangan yang digunakan (0= lulus SMA/Tidak kuliah) dan (1= lulus kuliah)
set.seed(123)
x3 <- round(runif(n))
x3## [1] 0 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1
## [38] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 1 0
## [75] 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1X4 adalah data IPK Pelamar dengan skala 4
set.seed(1234)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 2.40 3.14 3.54 1.83 3.21 3.25 2.71 2.73 2.72 2.55 2.76 2.50 2.61 3.03 3.48
## [16] 2.94 2.74 2.54 2.58 4.21 3.07 2.75 2.78 3.23 2.65 2.28 3.29 2.49 2.99 2.53
## [31] 3.55 2.76 2.65 2.75 2.19 2.42 1.91 2.33 2.85 2.77 3.72 2.47 2.57 2.86 2.50
## [46] 2.52 2.45 2.37 2.74 2.75 2.10 2.71 2.45 2.49 2.92 3.28 3.82 2.61 3.80 2.42
## [61] 3.33 4.27 2.98 2.67 3.00 3.89 2.43 3.68 3.66 3.17 3.00 2.77 2.82 3.32 4.04
## [76] 2.92 2.30 2.64 3.13 2.84 2.91 2.92 2.31 2.91 3.43 3.35 3.27 2.80 2.90 2.40
## [91] 2.97 3.13 3.85 3.50 2.75 3.18 2.43 3.44 3.49 4.06b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 2.2set.seed(12345)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 1.280 6.108 11.288 37.726 2.262 34.650 46.662 15.706 15.184 -5.390
## [11] 1.272 -1.500 18.442 5.366 21.156 8.668 16.028 79.088 2.176 28.962
## [21] 47.454 1.750 15.316 2.806 4.530 3.716 -0.562 4.178 30.578 2.066
## [31] 10.010 15.772 8.530 1.750 25.818 15.824 24.402 -1.874 16.270 5.594
## [41] 29.184 12.434 23.154 9.292 9.000 22.544 -5.610 4.714 19.528 19.250
## [51] 4.120 29.962 7.590 -2.022 -1.376 0.216 4.904 11.942 18.060 5.324
## [61] 41.526 5.394 6.556 2.374 15.800 1.058 7.546 24.796 -0.248 31.474
## [71] 5.300 29.794 5.404 3.304 8.388 34.424 29.560 5.008 23.886 51.748
## [81] 6.402 19.624 4.582 5.102 21.546 -0.130 19.894 1.860 1.580 -2.220
## [91] -0.466 -1.414 14.970 34.400 23.550 23.996 8.046 7.068 24.678 18.132p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.782449776 0.997779943 0.999987478 1.000000000 0.905680616 1.000000000
## [7] 1.000000000 0.999999849 0.999999746 0.004541256 0.781084923 0.182425524
## [13] 0.999999990 0.995348948 0.999999999 0.999828027 0.999999891 1.000000000
## [19] 0.898073505 1.000000000 1.000000000 0.851952802 0.999999777 0.942999193
## [25] 0.989334307 0.976246843 0.363084825 0.984902299 1.000000000 0.887554373
## [31] 0.999955054 0.999999859 0.999802584 0.851952802 1.000000000 0.999999866
## [37] 1.000000000 0.133079567 0.999999914 0.996293670 1.000000000 0.999996019
## [43] 1.000000000 0.999907850 0.999876605 1.000000000 0.003647715 0.991110894
## [49] 0.999999997 0.999999996 0.984015152 1.000000000 0.999494774 0.116912345
## [55] 0.201652186 0.553791023 0.992637749 0.999993489 0.999999986 0.995150411
## [61] 1.000000000 0.995476790 0.998580457 0.914823065 0.999999863 0.742308157
## [67] 0.999472060 1.000000000 0.438315828 1.000000000 0.995033198 1.000000000
## [73] 0.995521596 0.964565780 0.999772470 1.000000000 1.000000000 0.993360124
## [79] 1.000000000 1.000000000 0.998344508 0.999999997 0.989869275 0.993952233
## [85] 1.000000000 0.467545694 0.999999998 0.865296948 0.829204518 0.097968804
## [91] 0.385563426 0.195603918 0.999999685 1.000000000 1.000000000 1.000000000
## [97] 0.999679722 0.999148790 1.000000000 0.999999987set.seed(123456)
y <- rbinom(n,1,p)
y## [1] 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1
## [38] 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 0 2 0 0 2.40
## 2 1 2 1 1 3.14
## 3 1 4 1 0 3.54
## 4 1 12 0 1 1.83
## 5 1 1 0 1 3.21
## 6 1 11 0 0 3.25
## 7 1 14 0 1 2.71
## 8 1 5 1 1 2.73
## 9 1 5 0 1 2.72
## 10 0 0 0 0 2.55
## 11 0 1 0 1 2.76
## 12 0 1 1 0 2.50
## 13 1 6 0 1 2.61
## 14 1 2 0 1 3.03
## 15 1 7 0 0 3.48
## 16 1 3 0 1 2.94
## 17 1 6 0 0 2.74
## 18 1 24 1 0 2.54
## 19 1 2 1 0 2.58
## 20 1 8 0 1 4.21
## 21 1 14 0 1 3.07
## 22 1 1 1 1 2.75
## 23 1 5 0 1 2.78
## 24 1 1 1 1 3.23
## 25 1 2 0 1 2.65
## 26 1 2 0 1 2.28
## 27 0 0 1 1 3.29
## 28 1 2 0 1 2.49
## 29 1 10 0 0 2.99
## 30 1 2 1 0 2.53
## 31 1 3 0 1 3.55
## 32 1 5 1 1 2.76
## 33 1 3 1 1 2.65
## 34 1 1 1 1 2.75
## 35 1 9 1 0 2.19
## 36 1 6 1 0 2.42
## 37 1 8 1 1 1.91
## 38 0 1 1 0 2.33
## 39 1 6 0 0 2.85
## 40 1 3 0 0 2.77
## 41 1 9 1 0 3.72
## 42 1 5 1 0 2.47
## 43 1 8 1 0 2.57
## 44 1 4 0 0 2.86
## 45 1 4 1 0 2.50
## 46 1 8 0 0 2.52
## 47 0 0 0 0 2.45
## 48 1 3 0 0 2.37
## 49 1 7 0 0 2.74
## 50 1 6 1 1 2.75
## 51 1 3 0 0 2.10
## 52 1 10 0 0 2.71
## 53 1 3 0 1 2.45
## 54 0 1 0 0 2.49
## 55 0 0 1 1 2.92
## 56 0 1 1 0 3.28
## 57 1 2 1 0 3.82
## 58 1 4 1 1 2.61
## 59 1 5 1 1 3.80
## 60 1 3 1 0 2.42
## 61 1 12 1 1 3.33
## 62 1 2 0 0 4.27
## 63 1 3 1 0 2.98
## 64 1 2 1 0 2.67
## 65 1 5 0 1 3.00
## 66 1 1 0 0 3.89
## 67 1 3 0 1 2.43
## 68 1 7 1 1 3.68
## 69 0 0 0 1 3.66
## 70 1 10 1 0 3.17
## 71 1 2 0 1 3.00
## 72 1 9 1 1 2.77
## 73 1 2 1 1 2.82
## 74 1 2 0 0 3.32
## 75 1 3 0 0 4.04
## 76 1 11 1 0 2.92
## 77 1 10 1 0 2.30
## 78 1 2 1 1 2.64
## 79 1 8 0 0 3.13
## 80 1 16 1 0 2.84
## 81 1 3 1 0 2.91
## 82 1 6 1 1 2.92
## 83 1 3 0 0 2.31
## 84 1 2 0 1 2.91
## 85 1 7 1 0 3.43
## 86 1 1 0 0 3.35
## 87 1 6 0 1 3.27
## 88 1 1 1 1 2.80
## 89 0 1 0 1 2.90
## 90 1 1 0 0 2.40
## 91 0 1 1 0 2.97
## 92 0 0 0 1 3.13
## 93 1 5 0 0 3.85
## 94 1 10 0 1 3.50
## 95 1 8 1 0 2.75
## 96 1 8 0 0 3.18
## 97 1 3 1 1 2.43
## 98 1 3 0 0 3.44
## 99 1 8 0 0 3.49
## 100 1 5 0 1 4.06modelreglog <- 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) -14.2927 6.7321 -2.123 0.03375 *
## x1 4.2842 1.4175 3.022 0.00251 **
## x2 0.7724 1.1423 0.676 0.49894
## x3 1.9310 1.2230 1.579 0.11435
## x4 2.9125 1.7503 1.664 0.09611 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 80.993 on 99 degrees of freedom
## Residual deviance: 22.213 on 95 degrees of freedom
## AIC: 32.213
##
## Number of Fisher Scoring iterations: 10