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(100) ## mengunci data set seed
n <- 100
u <- runif(n)
## 60 = lama pekerja
## 12 = nilai tengah
## 100 = banyak pelamar
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 saat ini (0: Bekerja, 1: tidak bekerja)
set.seed(322) ## mengunci data set seed
x2 <- round(runif(n))
x2## [1] 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 1
## [38] 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 0 1
## [75] 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1x3 : Tingkat Pendidikan X3 : Tingkat pendidikan (0: Lulusan Sekolah Menengah, 1: Lulusan Perguruan Tinggi)
set.seed(100) ## mengunci data set seed
x3 <- round(runif(n))
x3## [1] 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 0
## [38] 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 0 1 1
## [75] 1 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 1X4 : IPK (skala 4)
set.seed(11)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 2.70 3.01 2.24 2.32 3.59 2.53 3.66 3.31 2.98 2.50 2.59 2.83 2.23 2.87 2.43
## [16] 3.01 2.89 3.44 2.70 2.67 2.66 2.99 2.78 3.18 3.04 3.00 2.91 2.62 2.89 2.51
## [31] 2.45 2.53 3.34 2.21 2.57 3.24 2.91 3.77 2.69 2.83 2.18 3.01 3.45 2.56 3.45
## [46] 2.83 1.91 3.44 3.36 3.11 3.39 2.89 2.59 3.25 3.08 3.27 2.92 3.22 3.74 3.03
## [61] 2.58 4.17 2.94 2.02 3.27 3.85 2.60 2.46 2.70 3.38 3.23 2.94 2.62 3.11 3.56
## [76] 3.08 2.66 3.23 2.47 3.20 2.97 3.16 2.70 2.55 4.13 2.70 2.35 3.25 2.57 2.25
## [91] 3.60 2.49 3.47 2.73 3.26 2.82 3.66 2.43 3.71 2.70mennetukan Koefesien
b0 <- -10
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 1.2set.seed(2)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 0.240 -2.888 9.388 -6.716 4.808 4.036 25.592 0.972 10.276 -3.000
## [11] 13.308 35.096 -0.324 4.444 20.616 17.812 -3.032 1.128 0.240 17.404
## [21] 10.392 17.788 10.536 21.516 4.148 -2.900 21.192 34.844 10.668 0.012
## [31] 3.440 41.236 1.508 48.352 16.784 35.088 -2.508 14.724 76.928 -3.104
## [41] -0.384 31.312 24.840 27.272 14.840 3.896 23.492 35.828 -1.968 0.732
## [51] 1.568 -3.032 -2.892 1.400 10.396 -2.576 -2.496 -2.636 15.188 -2.864
## [61] 3.596 15.204 52.728 16.624 4.424 2.120 4.120 3.452 -2.760 17.756
## [71] 4.876 0.528 9.844 56.432 14.972 14.396 30.892 21.576 27.664 -5.660
## [81] 4.564 13.992 41.440 66.260 -4.544 10.440 20.020 -2.100 0.584 19.900
## [91] 39.020 -3.012 1.664 3.776 38.612 0.384 11.592 -3.084 -5.048 20.940p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.559713649 0.052749964 0.999916284 0.001209908 0.991901942 0.982638736
## [7] 1.000000000 0.725517961 0.999965551 0.047425873 0.999998339 1.000000000
## [13] 0.419701228 0.988387584 0.999999999 0.999999982 0.046000978 0.755469617
## [19] 0.559713649 0.999999972 0.999969324 0.999999981 0.999973438 1.000000000
## [25] 0.984449656 0.052153563 0.999999999 1.000000000 0.999976722 0.502999964
## [31] 0.968931516 1.000000000 0.818764618 1.000000000 0.999999949 1.000000000
## [37] 0.075299250 0.999999597 1.000000000 0.042942560 0.405162509 1.000000000
## [43] 1.000000000 1.000000000 0.999999641 0.980081758 1.000000000 1.000000000
## [49] 0.122603869 0.675244005 0.827498306 0.046000978 0.052550451 0.802183889
## [55] 0.999969446 0.070699083 0.076139071 0.066857153 0.999999747 0.053962135
## [61] 0.973299252 0.999999751 1.000000000 0.999999940 0.988155776 0.892831930
## [67] 0.984015152 0.969290729 0.059524366 0.999999981 0.992430275 0.629016523
## [73] 0.999946938 1.000000000 0.999999685 0.999999440 1.000000000 1.000000000
## [79] 1.000000000 0.003470431 0.989687168 0.999999162 1.000000000 1.000000000
## [85] 0.010518973 0.999970762 0.999999998 0.109096821 0.641987286 0.999999998
## [91] 1.000000000 0.046886688 0.840774225 0.977599132 1.000000000 0.594837491
## [97] 0.999990760 0.043772085 0.006381184 0.999999999set.seed(3)
y <- rbinom(n,1,p)
y## [1] 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0
## [38] 1 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 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 1 2 0 0 2.70
## 2 0 1 0 0 3.01
## 3 1 4 0 1 2.24
## 4 0 0 1 0 2.32
## 5 1 3 0 0 3.59
## 6 1 3 1 0 2.53
## 7 1 8 1 1 3.66
## 8 1 2 0 0 3.31
## 9 1 4 0 1 2.98
## 10 0 1 1 0 2.50
## 11 1 5 0 1 2.59
## 12 1 11 1 1 2.83
## 13 0 2 0 0 2.23
## 14 1 3 1 0 2.87
## 15 1 7 1 1 2.43
## 16 1 6 1 1 3.01
## 17 0 1 0 0 2.89
## 18 1 2 0 0 3.44
## 19 0 2 0 0 2.70
## 20 1 6 1 1 2.67
## 21 1 4 1 1 2.66
## 22 1 6 1 1 2.99
## 23 1 4 1 1 2.78
## 24 1 7 1 1 3.18
## 25 1 3 0 0 3.04
## 26 0 1 0 0 3.00
## 27 1 7 1 1 2.91
## 28 1 11 1 1 2.62
## 29 1 4 1 1 2.89
## 30 0 2 0 0 2.51
## 31 1 3 0 0 2.45
## 32 1 13 0 1 2.53
## 33 1 2 1 0 3.34
## 34 1 15 1 1 2.21
## 35 1 6 0 1 2.57
## 36 1 11 0 1 3.24
## 37 0 1 1 0 2.91
## 38 1 5 0 1 3.77
## 39 1 23 1 1 2.69
## 40 0 1 0 0 2.83
## 41 0 2 0 0 2.18
## 42 1 10 0 1 3.01
## 43 1 8 0 1 3.45
## 44 1 9 0 1 2.56
## 45 1 5 1 1 3.45
## 46 1 3 0 0 2.83
## 47 1 8 1 1 1.91
## 48 1 11 1 1 3.44
## 49 0 1 1 0 3.36
## 50 1 2 0 0 3.11
## 51 1 2 1 0 3.39
## 52 0 1 0 0 2.89
## 53 0 1 1 0 2.59
## 54 1 2 1 0 3.25
## 55 1 4 0 1 3.08
## 56 0 1 0 0 3.27
## 57 1 1 1 0 2.92
## 58 0 1 0 0 3.22
## 59 1 5 1 1 3.74
## 60 0 1 0 0 3.03
## 61 1 3 0 0 2.58
## 62 1 5 0 1 4.17
## 63 1 16 1 1 2.94
## 64 1 6 1 1 2.02
## 65 1 3 0 0 3.27
## 66 1 2 1 0 3.85
## 67 1 3 1 0 2.60
## 68 1 3 0 0 2.46
## 69 0 1 1 0 2.70
## 70 1 6 0 1 3.38
## 71 1 3 1 0 3.23
## 72 1 2 0 0 2.94
## 73 1 4 0 1 2.62
## 74 1 17 1 1 3.11
## 75 1 5 1 1 3.56
## 76 1 5 1 1 3.08
## 77 1 10 0 1 2.66
## 78 1 7 1 1 3.23
## 79 1 9 1 1 2.47
## 80 0 0 1 0 3.20
## 81 1 3 1 0 2.97
## 82 1 5 0 1 3.16
## 83 1 13 0 1 2.70
## 84 1 20 1 1 2.55
## 85 0 0 1 0 4.13
## 86 1 4 1 1 2.70
## 87 1 7 0 1 2.35
## 88 0 1 1 0 3.25
## 89 0 2 1 0 2.57
## 90 1 7 0 1 2.25
## 91 1 12 0 1 3.60
## 92 0 1 1 0 2.49
## 93 0 2 1 0 3.47
## 94 1 3 0 0 2.73
## 95 1 12 0 1 3.26
## 96 1 2 0 0 2.82
## 97 1 4 1 1 3.66
## 98 0 1 1 0 2.43
## 99 0 0 1 0 3.71
## 100 1 7 1 1 2.70modelreglog <- 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) -17.6497 6.0340 -2.925 0.003444 **
## x1 4.1203 1.1436 3.603 0.000315 ***
## x2 -0.3883 1.2880 -0.302 0.763030
## x3 11.6475 3735.6328 0.003 0.997512
## x4 3.5062 1.7119 2.048 0.040553 *
## ---
## 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: 22.672 on 95 degrees of freedom
## AIC: 32.672
##
## Number of Fisher Scoring iterations: 21