Y :Keputusan menolak / menerima pelamar kerja 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(100)
n <- 100
u <- runif(n)
X1 <- round (60*(-(log(1-u)/12)))
X1## [1] 2 1 4 0 3 3 8 2 4 1 5 11 2 3 7 6 1 2 2 6 4 6 4 7 3
## [26] 1 7 11 4 2 3 13 2 15 6 11 1 5 23 1 2 10 8 9 5 3 8 11 1 2
## [51] 2 1 1 2 4 1 1 1 5 1 3 5 16 6 3 2 3 3 1 6 3 2 4 17 5
## [76] 5 10 7 9 0 3 5 13 20 0 4 7 1 2 7 12 1 2 3 12 2 4 1 0 7X2 :Status pekerjaan Keterangan yang digunakan (0:Bekerja, 1:Tidak bekerja)
set.seed(123)
X2 <- round(runif(n))
X2## [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 1X3 :Tingkat Pendidikan Keterangan yang digunakan (0: Lulusan Sekolah Menengah, 1:Lulusan Perguruan Tinggi)
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 1X3 : 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 2.49menentukan koefisien
b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 1.2set.seed(123)
DataPendukung <- b0 + (b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
DataPendukung## [1] -0.736 -0.844 7.536 -4.152 6.372 4.132 24.076 2.044 9.392 -4.164
## [11] 14.032 31.316 3.040 6.372 16.764 17.868 -3.600 -1.576 0.020 16.512
## [21] 9.164 16.668 9.188 19.868 5.928 -1.708 20.804 34.396 5.916 0.356
## [31] 6.552 41.120 3.340 48.828 14.092 31.508 -0.364 10.064 72.920 -4.128
## [41] -0.820 27.480 19.844 25.396 10.820 2.428 20.360 30.824 -3.432 2.752
## [51] -0.244 -3.912 -0.724 0.416 9.668 -2.988 -4.824 -0.352 13.372 -3.768
## [61] 6.528 9.800 48.396 12.988 5.652 -0.220 6.564 6.336 -0.148 14.836
## [71] 6.000 1.420 10.400 51.680 9.692 10.712 27.432 19.568 24.208 -7.484
## [81] 3.100 13.528 37.872 66.184 -7.532 6.804 20.960 -0.436 2.608 17.784
## [91] 35.200 -0.376 -0.256 5.928 35.416 -0.760 11.108 -2.976 -7.544 19.688p <- exp(DataPendukung)/ (1 + exp(DataPendukung))
p## [1] 0.3238794522 0.3006930071 0.9994667570 0.0154892284 0.9982941765
## [6] 0.9842028114 1.0000000000 0.8853399468 0.9999166184 0.0153072966
## [11] 0.9999991947 1.0000000000 0.9543488292 0.9982941765 0.9999999476
## [16] 0.9999999826 0.0265969936 0.1713627258 0.5049998333 0.9999999326
## [21] 0.9998952679 0.9999999423 0.9998977513 0.9999999976 0.9973432727
## [26] 0.1534233049 0.9999999991 1.0000000000 0.9973112862 0.5880718032
## [31] 0.9985747759 1.0000000000 0.9657758423 1.0000000000 0.9999992416
## [36] 1.0000000000 0.4099916248 0.9999574165 1.0000000000 0.0158594997
## [41] 0.3057636599 1.0000000000 0.9999999976 1.0000000000 0.9999800048
## [46] 0.9189376751 0.9999999986 1.0000000000 0.0313102156 0.9400262030
## [51] 0.4393008503 0.0196082854 0.3265127646 0.6025256899 0.9999367277
## [56] 0.0479709468 0.0079705444 0.4128975105 0.9999984418 0.0225767314
## [61] 0.9985402073 0.9999445515 1.0000000000 0.9999977124 0.9965017916
## [66] 0.4452207649 0.9985917525 0.9982317586 0.4630673897 0.9999996396
## [71] 0.9975273768 0.8053384164 0.9999695684 1.0000000000 0.9999382281
## [76] 0.9999777245 1.0000000000 0.9999999968 1.0000000000 0.0005616892
## [81] 0.9568927451 0.9999986669 1.0000000000 1.0000000000 0.0005353791
## [86] 0.9988919003 0.9999999992 0.3926945008 0.9313746747 0.9999999811
## [91] 1.0000000000 0.4070920095 0.4363472498 0.9973432727 1.0000000000
## [96] 0.3186462662 0.9999850083 0.0485219659 0.0005289963 0.9999999972set.seed(123)
y <- rbinom(n,1,p)
y## [1] 0 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
## [38] 1 1 0 0 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1datagab <- data.frame(y,X1,X2,X3,X4)
datagab## y X1 X2 X3 X4
## 1 0 2 0 0 2.72
## 2 1 1 1 1 2.88
## 3 1 4 0 0 3.78
## 4 0 0 1 1 3.04
## 5 1 3 1 1 3.06
## 6 1 3 0 0 3.86
## 7 1 8 1 1 3.23
## 8 0 2 1 1 2.37
## 9 1 4 1 1 2.66
## 10 0 1 0 0 2.78
## 11 1 5 1 1 3.61
## 12 1 11 0 0 3.18
## 13 1 2 1 1 3.20
## 14 1 3 1 1 3.06
## 15 1 7 0 0 2.72
## 16 1 6 1 1 3.89
## 17 0 1 0 0 3.25
## 18 0 2 0 0 2.02
## 19 1 2 0 0 3.35
## 20 1 6 1 1 2.76
## 21 1 4 1 1 2.47
## 22 1 6 1 1 2.89
## 23 1 4 1 1 2.49
## 24 1 7 1 1 2.64
## 25 1 3 1 1 2.69
## 26 0 1 1 1 2.16
## 27 1 7 1 1 3.42
## 28 1 11 1 1 3.08
## 29 1 4 0 0 2.43
## 30 1 2 0 0 3.63
## 31 1 3 1 1 3.21
## 32 1 13 1 1 2.85
## 33 1 2 1 1 3.45
## 34 1 15 1 1 3.44
## 35 1 6 0 0 3.41
## 36 1 11 0 0 3.34
## 37 0 1 1 1 3.28
## 38 1 5 0 0 2.97
## 39 1 23 0 0 2.85
## 40 0 1 0 0 2.81
## 41 0 2 0 0 2.65
## 42 1 10 0 0 2.90
## 43 1 8 0 0 2.37
## 44 1 9 0 0 4.08
## 45 1 5 0 0 3.60
## 46 1 3 0 0 2.44
## 47 1 8 0 0 2.80
## 48 1 11 0 0 2.77
## 49 0 1 0 0 3.39
## 50 1 2 1 1 2.96
## 51 0 2 0 0 3.13
## 52 0 1 0 0 2.99
## 53 1 1 1 1 2.98
## 54 1 2 0 0 3.68
## 55 1 4 1 1 2.89
## 56 0 1 0 0 3.76
## 57 0 1 0 0 2.23
## 58 0 1 1 1 3.29
## 59 1 5 1 1 3.06
## 60 0 1 0 0 3.11
## 61 1 3 1 1 3.19
## 62 1 5 0 0 2.75
## 63 1 16 0 0 2.83
## 64 1 6 0 0 2.49
## 65 1 3 1 1 2.46
## 66 0 2 0 0 3.15
## 67 1 3 1 1 3.22
## 68 1 3 1 1 3.03
## 69 1 1 1 1 3.46
## 70 1 6 0 0 4.03
## 71 1 3 1 1 2.75
## 72 0 2 1 1 1.85
## 73 1 4 1 1 3.50
## 74 1 17 0 0 2.65
## 75 1 5 0 0 2.66
## 76 1 5 0 0 3.51
## 77 1 10 0 0 2.86
## 78 1 7 1 1 2.39
## 79 1 9 0 0 3.09
## 80 0 0 0 0 2.93
## 81 1 3 0 0 3.00
## 82 1 5 1 1 3.19
## 83 1 13 0 0 2.81
## 84 1 20 1 1 3.32
## 85 0 0 0 0 2.89
## 86 1 4 0 0 3.17
## 87 1 7 1 1 3.55
## 88 0 1 1 1 3.22
## 89 1 2 1 1 2.84
## 90 1 7 0 0 3.57
## 91 1 12 0 0 3.50
## 92 0 1 1 1 3.27
## 93 0 2 0 0 3.12
## 94 1 3 1 1 2.69
## 95 1 12 0 0 3.68
## 96 1 2 0 0 2.70
## 97 1 4 1 1 4.09
## 98 0 1 0 0 3.77
## 99 0 0 0 0 2.88
## 100 1 7 1 1 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: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -19.836 7.139 -2.779 0.00546 **
## X1 4.130 1.394 2.963 0.00305 **
## X2 3.487 1.484 2.350 0.01876 *
## X3 NA NA NA NA
## X4 3.600 1.564 2.302 0.02136 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 114.611 on 99 degrees of freedom
## Residual deviance: 26.065 on 96 degrees of freedom
## AIC: 34.065
##
## Number of Fisher Scoring iterations: 10