Y :Keputusan menolak/menerima pelamar kerja pada PT W posisi A X1:Lama pengalaman kerja sebelumnya (bulan) X2:Status pekerjaan saat ini (0: bekerja, 1: Tidak bekerja) X3:Tingkat pendididkan (0: lulusan Sekolah Menengah, 1:Lulusan Perguruan Tinggi) X4:IPK (Skala 4)
X1 : Lama Pengalaman Kerja Sebelumnya 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=Tidak Bekerja) dam (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 : Data 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(12)
X4 <- round(rnorm(n,3,0.5),2)
X4## [1] 2.26 3.79 2.52 2.54 2.00 2.86 2.84 2.69 2.95 3.21 2.61 2.35 2.61 3.01 2.92
## [16] 2.65 3.59 3.17 3.25 2.85 3.11 4.00 3.51 2.85 2.49 2.87 2.90 3.07 3.07 3.18
## [31] 3.34 4.04 2.73 2.46 2.81 2.76 3.14 2.76 3.40 2.50 3.05 2.42 3.29 2.20 2.85
## [46] 3.22 2.51 3.09 3.37 2.75 2.98 2.94 3.23 4.01 2.47 3.37 3.27 2.34 2.87 3.16
## [61] 3.20 3.50 3.43 3.10 3.42 3.42 3.98 1.93 3.49 3.57 2.74 3.13 2.79 2.91 2.95
## [76] 2.68 2.36 2.81 3.26 2.91 3.00 2.36 2.90 3.58 2.99 3.45 2.91 3.56 2.73 2.52
## [91] 3.19 2.51 3.45 3.06 3.52 2.83 3.23 2.65 2.88 2.50Menentukan koefisien
b0 <- -12
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 3.2set.seed(1)
datapendukung <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datapendukung## [1] 2.732 6.828 10.564 -0.672 7.600 7.652 27.788 6.808 14.640 2.272
## [11] 16.552 34.020 6.552 10.832 21.844 20.180 2.988 5.144 5.400 21.320
## [21] 14.652 24.500 16.432 24.820 9.668 3.384 24.980 39.524 11.824 5.176
## [31] 12.388 49.128 6.436 51.572 17.992 35.332 4.748 14.832 79.880 -0.500
## [41] 5.260 30.744 27.028 27.040 14.620 8.804 24.032 36.388 2.284 7.000
## [51] 5.036 1.408 4.536 7.832 13.104 2.284 2.464 1.688 17.384 1.612
## [61] 11.940 16.700 55.476 19.420 12.644 5.944 14.436 7.376 5.868 20.924
## [71] 10.468 8.216 13.628 56.812 14.940 14.576 31.052 24.692 30.432 -2.688
## [81] 8.600 16.252 42.780 72.156 -2.432 13.040 25.012 6.092 6.936 20.564
## [91] 40.708 2.732 6.040 11.492 41.764 4.556 15.536 0.480 -2.784 23.200p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.93888869 0.99891815 0.99997417 0.33804915 0.99949980 0.99952513
## [7] 1.00000000 0.99889632 0.99999956 0.90653139 0.99999994 1.00000000
## [13] 0.99857478 0.99998024 1.00000000 1.00000000 0.95202905 0.99419954
## [19] 0.99550373 1.00000000 0.99999957 1.00000000 0.99999993 1.00000000
## [25] 0.99993673 0.96720074 1.00000000 1.00000000 0.99999267 0.99438119
## [31] 0.99999583 1.00000000 0.99839976 1.00000000 0.99999998 1.00000000
## [37] 0.99140549 0.99999964 1.00000000 0.37754067 0.99483155 1.00000000
## [43] 1.00000000 1.00000000 0.99999955 0.99984989 1.00000000 1.00000000
## [49] 0.90754323 0.99908895 0.99354228 0.80345030 0.98939743 0.99960333
## [55] 0.99999796 0.90754323 0.92157923 0.84396096 0.99999997 0.83368887
## [61] 0.99999348 0.99999994 1.00000000 1.00000000 0.99999677 0.99738533
## [67] 0.99999946 0.99937429 0.99717945 1.00000000 0.99997157 0.99972978
## [73] 0.99999879 1.00000000 0.99999968 0.99999953 1.00000000 1.00000000
## [79] 1.00000000 0.06368517 0.99981593 0.99999991 1.00000000 1.00000000
## [85] 0.08076486 0.99999783 1.00000000 0.99774422 0.99902879 1.00000000
## [91] 1.00000000 0.93888869 0.99762410 0.99998979 1.00000000 0.98960520
## [97] 0.99999982 0.61774787 0.05819493 1.00000000set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1datagab <- data.frame(y,X1,X2,X3,X4)
datagab## y X1 X2 X3 X4
## 1 1 2 1 0 2.26
## 2 1 1 1 1 3.79
## 3 1 4 1 0 2.52
## 4 0 0 1 1 2.54
## 5 1 3 0 1 2.00
## 6 1 3 0 0 2.86
## 7 1 8 0 1 2.84
## 8 1 2 1 1 2.69
## 9 1 4 1 1 2.95
## 10 1 1 1 0 3.21
## 11 1 5 0 1 2.61
## 12 1 11 0 0 2.35
## 13 1 2 1 1 2.61
## 14 1 3 0 1 3.01
## 15 1 7 0 0 2.92
## 16 1 6 0 1 2.65
## 17 0 1 0 0 3.59
## 18 1 2 0 0 3.17
## 19 1 2 0 0 3.25
## 20 1 6 1 1 2.85
## 21 1 4 0 1 3.11
## 22 1 6 0 1 4.00
## 23 1 4 1 1 3.51
## 24 1 7 1 1 2.85
## 25 1 3 1 1 2.49
## 26 1 1 0 1 2.87
## 27 1 7 1 1 2.90
## 28 1 11 1 1 3.07
## 29 1 4 0 0 3.07
## 30 1 2 0 0 3.18
## 31 1 3 1 1 3.34
## 32 1 13 0 1 4.04
## 33 1 2 0 1 2.73
## 34 1 15 1 1 2.46
## 35 1 6 0 0 2.81
## 36 1 11 0 0 2.76
## 37 1 1 1 1 3.14
## 38 1 5 1 0 2.76
## 39 1 23 1 0 3.40
## 40 1 1 0 0 2.50
## 41 1 2 1 0 3.05
## 42 1 10 0 0 2.42
## 43 1 8 1 0 3.29
## 44 1 9 1 0 2.20
## 45 1 5 0 0 2.85
## 46 1 3 0 0 3.22
## 47 1 8 0 0 2.51
## 48 1 11 0 0 3.09
## 49 0 1 0 0 3.37
## 50 1 2 1 1 2.75
## 51 1 2 1 0 2.98
## 52 1 1 1 0 2.94
## 53 1 1 0 1 3.23
## 54 1 2 0 0 4.01
## 55 1 4 1 1 2.47
## 56 1 1 0 0 3.37
## 57 1 1 1 0 3.27
## 58 0 1 0 1 2.34
## 59 1 5 0 1 2.87
## 60 1 1 0 0 3.16
## 61 1 3 1 1 3.20
## 62 1 5 0 0 3.50
## 63 1 16 1 0 3.43
## 64 1 6 1 0 3.10
## 65 1 3 1 1 3.42
## 66 1 2 0 0 3.42
## 67 1 3 1 1 3.98
## 68 1 3 0 1 1.93
## 69 1 1 1 1 3.49
## 70 1 6 1 0 3.57
## 71 1 3 1 1 2.74
## 72 1 2 1 1 3.13
## 73 1 4 0 1 2.79
## 74 1 17 0 0 2.91
## 75 1 5 0 0 2.95
## 76 1 5 1 0 2.68
## 77 1 10 1 0 2.36
## 78 1 7 1 1 2.81
## 79 1 9 1 0 3.26
## 80 0 0 0 0 2.91
## 81 1 3 1 0 3.00
## 82 1 5 1 1 2.36
## 83 1 13 0 0 2.90
## 84 1 20 0 1 3.58
## 85 0 0 0 0 2.99
## 86 1 4 0 0 3.45
## 87 1 7 1 1 2.91
## 88 1 1 1 1 3.56
## 89 1 2 1 1 2.73
## 90 1 7 0 0 2.52
## 91 1 12 1 0 3.19
## 92 1 1 1 1 2.51
## 93 1 2 0 0 3.45
## 94 1 3 1 1 3.06
## 95 1 12 1 0 3.52
## 96 1 2 1 0 2.83
## 97 1 4 1 1 3.23
## 98 1 1 1 0 2.65
## 99 0 0 0 0 2.88
## 100 1 7 0 1 2.50modelreglog <- 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) -38.7377 10914.2809 -0.004 0.997
## X1 40.2874 10914.2796 0.004 0.997
## X2 20.4693 7200.1210 0.003 0.998
## X3 0.1512 1.7092 0.088 0.930
## X4 -0.3572 2.0024 -0.178 0.858
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 50.728 on 99 degrees of freedom
## Residual deviance: 10.517 on 95 degrees of freedom
## AIC: 20.517
##
## Number of Fisher Scoring iterations: 25