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(1234)
n <- 100
u <- runif(n)
x1 <- round(60*(-(log(1-u)/12)))
x1## [1] 1 5 5 5 10 5 0 1 5 4 6 4 2 13 2 9 2 2 1 1 2 2 1 0 1
## [26] 8 4 12 9 0 3 2 2 4 1 7 1 1 24 8 4 5 2 5 2 3 6 3 1 7
## [51] 0 2 6 4 1 4 3 7 1 9 10 0 2 0 1 6 2 4 0 4 1 11 0 8 0
## [76] 4 2 0 2 6 13 3 1 4 1 11 2 2 1 11 1 12 1 1 1 4 2 0 2 7X2 : Status pekerjaan Keterangan yang digunakan (0=Tidak Bekerja) dan (1=Bekerja)
set.seed(12345)
x2 <- round(runif(n))
x2## [1] 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1
## [38] 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 1 0 1 1 1 0 1 0 1 1 1 1 0 0
## [75] 0 1 1 1 1 0 1 1 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 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(222)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.74 3.00 3.69 2.81 3.09 2.88 2.39 3.78 3.21 2.40 3.53 2.35 2.65 3.30 2.90
## [16] 2.41 2.00 3.00 3.26 2.63 3.36 3.36 2.67 3.75 2.28 1.92 3.20 2.80 2.85 3.67
## [31] 2.59 3.34 2.89 2.94 2.90 3.20 3.33 3.05 2.91 3.47 3.10 3.25 2.72 3.56 4.10
## [46] 3.16 2.53 3.41 2.81 3.17 3.30 3.26 2.52 2.39 2.90 3.53 3.19 3.62 3.16 2.48
## [61] 2.43 3.62 3.39 3.37 3.03 3.42 3.10 3.73 2.77 1.61 3.03 2.97 2.41 1.74 3.41
## [76] 3.13 2.97 3.34 3.01 3.27 3.34 2.40 2.38 3.11 2.27 2.94 3.27 3.36 2.21 3.55
## [91] 2.83 3.31 3.25 3.84 3.19 3.12 3.21 2.41 2.68 3.03b0 <- -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] 1.228 16.300 15.118 15.882 33.498 12.836 -3.042 4.016 16.762 8.780
## [11] 20.466 8.170 5.030 44.460 2.380 28.502 0.400 2.600 -0.328 1.486
## [21] 6.092 6.092 1.574 0.450 0.716 23.924 13.240 40.360 26.770 -2.926
## [31] 8.398 6.048 5.058 12.668 -1.120 20.540 3.026 -0.290 79.902 24.634
## [41] 10.320 13.650 2.484 14.832 5.020 6.452 15.566 7.002 -1.318 23.674
## [51] -3.240 3.672 18.244 8.258 2.080 10.766 7.018 24.164 2.152 25.956
## [61] 32.546 -3.036 3.958 -3.086 2.366 17.524 6.020 13.906 -1.706 7.042
## [71] 2.366 37.234 -2.998 20.828 -3.498 10.386 3.034 -0.452 3.122 17.194
## [81] 42.348 7.980 -2.264 12.542 -2.506 33.968 6.394 6.592 0.562 35.310
## [91] -0.774 41.482 -0.350 4.148 0.018 10.364 6.262 -5.198 1.896 22.866p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.773468336 0.999999917 0.999999728 0.999999873 1.000000000 0.999997337
## [7] 0.045564116 0.982294225 0.999999947 0.999846246 0.999999999 0.999717062
## [13] 0.993503668 1.000000000 0.915289434 1.000000000 0.598687660 0.930861580
## [19] 0.418727333 0.815477135 0.997744217 0.997744217 0.828353092 0.610639234
## [25] 0.671725582 1.000000000 0.999998222 1.000000000 1.000000000 0.050883154
## [31] 0.999774733 0.997642986 0.993681909 0.999996850 0.246011284 0.999999999
## [37] 0.953734995 0.428003867 1.000000000 1.000000000 0.999967034 0.999998820
## [43] 0.923012521 0.999999638 0.993438807 0.998425120 0.999999826 0.999090767
## [49] 0.211151234 1.000000000 0.037687891 0.975204863 0.999999988 0.999740890
## [55] 0.888944033 0.999978895 0.999105186 1.000000000 0.895855521 1.000000000
## [61] 1.000000000 0.045825756 0.981256742 0.043688449 0.914197619 0.999999975
## [67] 0.997576219 0.999999087 0.153683254 0.999126388 0.914197619 1.000000000
## [73] 0.047516308 0.999999999 0.029369190 0.999969139 0.954086713 0.388885353
## [79] 0.957791157 0.999999966 1.000000000 0.999657878 0.094148676 0.999996427
## [85] 0.075438627 1.000000000 0.998331233 0.998630583 0.636915175 1.000000000
## [91] 0.315614462 1.000000000 0.413382421 0.984449656 0.504499879 0.999968453
## [97] 0.998096204 0.005497222 0.869438134 1.000000000set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 1
## [75] 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 1 1 0 3.74
## 2 1 5 1 1 3.00
## 3 1 5 1 0 3.69
## 4 1 5 1 1 2.81
## 5 1 10 0 1 3.09
## 6 1 5 0 0 2.88
## 7 0 0 0 1 2.39
## 8 1 1 1 1 3.78
## 9 1 5 1 1 3.21
## 10 1 4 1 0 2.40
## 11 1 6 0 1 3.53
## 12 1 4 0 0 2.35
## 13 1 2 1 1 2.65
## 14 1 13 0 1 3.30
## 15 1 2 0 0 2.90
## 16 1 9 0 1 2.41
## 17 0 2 0 0 2.00
## 18 0 2 0 0 3.00
## 19 0 1 0 0 3.26
## 20 1 1 1 1 2.63
## 21 1 2 0 1 3.36
## 22 1 2 0 1 3.36
## 23 1 1 1 1 2.67
## 24 0 0 1 1 3.75
## 25 1 1 1 1 2.28
## 26 1 8 0 1 1.92
## 27 1 4 1 1 3.20
## 28 1 12 1 1 2.80
## 29 1 9 0 0 2.85
## 30 0 0 0 0 3.67
## 31 1 3 1 1 2.59
## 32 1 2 0 1 3.34
## 33 1 2 0 1 2.89
## 34 1 4 1 1 2.94
## 35 1 1 0 0 2.90
## 36 1 7 0 0 3.20
## 37 1 1 1 1 3.33
## 38 1 1 1 0 3.05
## 39 1 24 1 0 2.91
## 40 1 8 0 0 3.47
## 41 1 4 1 0 3.10
## 42 1 5 0 0 3.25
## 43 1 2 1 0 2.72
## 44 1 5 1 0 3.56
## 45 1 2 0 0 4.10
## 46 1 3 0 0 3.16
## 47 1 6 0 0 2.53
## 48 1 3 0 0 3.41
## 49 1 1 0 0 2.81
## 50 1 7 1 1 3.17
## 51 0 0 1 0 3.30
## 52 1 2 1 0 3.26
## 53 1 6 0 1 2.52
## 54 1 4 0 0 2.39
## 55 1 1 1 1 2.90
## 56 1 4 0 0 3.53
## 57 1 3 1 0 3.19
## 58 1 7 0 1 3.62
## 59 1 1 0 1 3.16
## 60 1 9 0 0 2.48
## 61 1 10 1 1 2.43
## 62 0 0 0 0 3.62
## 63 1 2 1 0 3.39
## 64 0 0 1 0 3.37
## 65 1 1 1 1 3.03
## 66 1 6 0 0 3.42
## 67 1 2 1 1 3.10
## 68 1 4 0 1 3.73
## 69 0 0 1 1 2.77
## 70 1 4 1 0 1.61
## 71 1 1 1 1 3.03
## 72 1 11 1 1 2.97
## 73 0 0 0 1 2.41
## 74 1 8 0 0 1.74
## 75 0 0 0 0 3.41
## 76 1 4 1 0 3.13
## 77 1 2 1 0 2.97
## 78 0 0 1 1 3.34
## 79 1 2 1 0 3.01
## 80 1 6 0 0 3.27
## 81 1 13 1 0 3.34
## 82 1 3 1 1 2.40
## 83 0 1 0 0 2.38
## 84 1 4 0 1 3.11
## 85 0 1 0 0 2.27
## 86 1 11 0 0 2.94
## 87 1 2 1 1 3.27
## 88 1 2 1 1 3.36
## 89 1 1 1 1 2.21
## 90 1 11 0 0 3.55
## 91 1 1 1 0 2.83
## 92 1 12 1 1 3.31
## 93 0 1 0 0 3.25
## 94 1 1 1 1 3.84
## 95 1 1 1 0 3.19
## 96 1 4 1 0 3.12
## 97 1 2 1 1 3.21
## 98 0 0 1 0 2.41
## 99 1 2 0 0 2.68
## 100 1 7 0 1 3.03modelreglog <- 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) -8.931 4.447 -2.008 0.044622 *
## x1 3.902 1.113 3.506 0.000455 ***
## x2 2.856 1.312 2.176 0.029548 *
## x3 1.570 1.192 1.317 0.187875
## x4 1.183 1.252 0.945 0.344705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.177 on 99 degrees of freedom
## Residual deviance: 24.711 on 95 degrees of freedom
## AIC: 34.711
##
## Number of Fisher Scoring iterations: 10