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 membangkitkan variabel X1 dengan lama pekerjaan 0-60 bulan dengan nilai tengah 12 dan banyak pelamar adalah 100
set.seed(1)
n <- 100
u <- 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=Tidak bekerja) dan (1=Bekerja)
set.seed(2)
x2 <- round(runif(n))
x2## [1] 0 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1
## [38] 0 1 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0
## [75] 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0X3 : Tingkat pendidikan Keterangan yang digunakan (0=Lulusan SMA) dan (1=Lulusan PTN)
set.seed(3)
x3 <- round(runif(n))
x3## [1] 0 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1
## [38] 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 1 0 0 1 1 1 1
## [75] 1 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1X4 : IPK Keterangan yang digunakan (data pelamar dengan skala 4)
set.seed(4)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.11 2.73 3.45 3.30 3.82 3.34 2.36 2.89 3.95 3.89 3.28 3.01 3.19 2.98 3.02
## [16] 3.08 3.58 2.98 2.95 2.86 3.77 3.08 3.65 3.64 3.30 2.86 3.63 3.45 2.54 3.62
## [31] 3.08 3.53 2.62 2.26 3.43 2.80 2.89 3.47 2.77 2.68 3.67 3.09 3.65 2.16 2.59
## [46] 2.57 3.05 2.81 3.36 2.10 2.67 2.69 2.96 3.22 3.99 2.70 2.72 3.35 2.92 3.67
## [61] 2.47 3.53 2.34 4.03 3.07 2.88 2.80 3.44 3.26 2.91 3.08 2.76 2.52 3.09 3.36
## [76] 2.82 3.12 2.67 2.60 2.97 3.64 2.89 2.71 2.26 2.48 2.35 2.58 2.43 3.18 2.90
## [91] 2.36 2.60 3.08 3.31 3.34 2.98 4.17 2.71 3.48 2.86b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 2.2
set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 2.842 5.206 11.090 38.260 4.104 38.048 43.192 13.358 17.890 0.758
## [11] 2.916 1.822 20.218 5.256 22.844 9.476 18.376 82.256 5.190 23.292
## [21] 46.794 -0.724 15.030 0.508 3.260 4.992 -0.314 6.290 32.788 6.664
## [31] 6.276 14.266 5.764 -2.028 28.546 16.660 26.558 0.134 19.294 5.396
## [41] 29.074 15.998 25.030 10.452 9.198 23.154 -3.790 5.682 21.392 17.820
## [51] 5.374 29.918 9.212 2.784 0.478 1.640 2.484 10.870 13.424 8.074
## [61] 39.634 4.266 7.848 4.866 16.454 -1.164 8.360 23.768 -3.828 30.402
## [71] 5.476 29.272 4.244 5.498 9.592 36.904 30.864 2.374 25.420 54.734
## [81] 10.208 19.558 5.462 0.972 21.656 0.870 16.176 -2.154 2.696 1.580
## [91] -1.808 -5.280 13.276 34.482 27.048 23.556 8.674 5.462 24.656 15.492p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.944903676 0.994546341 0.999984736 1.000000000 0.983761524 1.000000000
## [7] 1.000000000 0.999998420 0.999999983 0.680919355 0.948631730 0.860805938
## [13] 0.999999998 0.994810939 1.000000000 0.999923336 0.999999990 1.000000000
## [19] 0.994458868 1.000000000 1.000000000 0.326512765 0.999999703 0.624337511
## [25] 0.963030791 0.993253754 0.422138686 0.998148674 1.000000000 0.998725594
## [31] 0.998122622 0.999999363 0.996871287 0.116294304 1.000000000 0.999999942
## [37] 1.000000000 0.533449963 0.999999996 0.995485787 1.000000000 0.999999887
## [43] 1.000000000 0.999971110 0.999898769 1.000000000 0.022096322 0.996604828
## [49] 0.999999999 0.999999982 0.995385837 1.000000000 0.999900176 0.941805065
## [55] 0.617275493 0.837534937 0.923012521 0.999980980 0.999998521 0.999688562
## [61] 1.000000000 0.986156510 0.999609620 0.992354779 0.999999929 0.237941224
## [67] 0.999766010 1.000000000 0.021289956 1.000000000 0.995831409 1.000000000
## [73] 0.985852935 0.995921747 0.999931732 1.000000000 1.000000000 0.914823065
## [79] 1.000000000 1.000000000 0.999963127 0.999999997 0.995772887 0.725517961
## [85] 1.000000000 0.704745698 0.999999906 0.103958029 0.936790200 0.829204518
## [91] 0.140880018 0.005066629 0.999998285 1.000000000 1.000000000 1.000000000
## [97] 0.999829055 0.995772887 1.000000000 0.999999813set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 1 1 1 1 1 1 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 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 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 1 2 0 0 3.11
## 2 1 2 1 1 2.73
## 3 1 4 1 0 3.45
## 4 1 12 0 0 3.30
## 5 1 1 1 1 3.82
## 6 1 11 1 1 3.34
## 7 1 14 0 0 2.36
## 8 1 5 1 0 2.89
## 9 1 5 0 1 3.95
## 10 1 0 1 1 3.89
## 11 1 1 1 1 3.28
## 12 1 1 0 1 3.01
## 13 1 6 1 1 3.19
## 14 1 2 0 1 2.98
## 15 1 7 0 1 3.02
## 16 1 3 1 1 3.08
## 17 1 6 1 0 3.58
## 18 1 24 0 1 2.98
## 19 1 2 0 1 2.95
## 20 1 8 0 0 2.86
## 21 1 14 1 0 3.77
## 22 1 1 0 0 3.08
## 23 1 5 1 0 3.65
## 24 1 1 0 0 3.64
## 25 1 2 0 0 3.30
## 26 1 2 0 1 2.86
## 27 0 0 0 1 3.63
## 28 1 2 0 1 3.45
## 29 1 10 1 1 2.54
## 30 1 2 0 1 3.62
## 31 1 3 0 0 3.08
## 32 1 5 0 0 3.53
## 33 1 3 1 0 2.62
## 34 1 1 1 0 2.26
## 35 1 9 1 0 3.43
## 36 1 6 1 0 2.80
## 37 1 8 1 1 2.89
## 38 0 1 0 0 3.47
## 39 1 6 1 1 2.77
## 40 1 3 0 0 2.68
## 41 1 9 1 0 3.67
## 42 1 5 0 1 3.09
## 43 1 8 0 0 3.65
## 44 1 4 0 1 2.16
## 45 1 4 1 0 2.59
## 46 1 8 1 0 2.57
## 47 0 0 1 0 3.05
## 48 1 3 0 0 2.81
## 49 1 7 1 0 3.36
## 50 1 6 1 1 2.10
## 51 1 3 0 0 2.67
## 52 1 10 0 0 2.69
## 53 1 3 1 1 2.96
## 54 1 1 1 1 3.22
## 55 0 0 0 1 3.99
## 56 1 1 1 1 2.70
## 57 1 2 1 0 2.72
## 58 1 4 1 0 3.35
## 59 1 5 1 0 2.92
## 60 1 3 1 0 3.67
## 61 1 12 1 1 2.47
## 62 1 2 1 0 3.53
## 63 1 3 1 1 2.34
## 64 1 2 0 0 4.03
## 65 1 5 1 1 3.07
## 66 0 1 0 0 2.88
## 67 1 3 0 1 2.80
## 68 1 7 0 1 3.44
## 69 0 0 0 0 3.26
## 70 1 10 0 0 2.91
## 71 1 2 0 1 3.08
## 72 1 9 0 1 2.76
## 73 1 2 0 1 2.52
## 74 1 2 0 1 3.09
## 75 1 3 0 1 3.36
## 76 1 11 1 1 2.82
## 77 1 10 0 0 3.12
## 78 1 2 1 0 2.67
## 79 1 8 0 1 2.60
## 80 1 16 1 1 2.97
## 81 1 3 0 1 3.64
## 82 1 6 1 1 2.89
## 83 1 3 0 0 2.71
## 84 0 2 0 0 2.26
## 85 1 7 0 1 2.48
## 86 0 1 1 1 2.35
## 87 1 6 1 0 2.58
## 88 0 1 0 0 2.43
## 89 1 1 1 1 3.18
## 90 1 1 0 1 2.90
## 91 0 1 1 0 2.36
## 92 0 0 0 0 2.60
## 93 1 5 0 0 3.08
## 94 1 10 1 1 3.31
## 95 1 8 0 1 3.34
## 96 1 8 0 0 2.98
## 97 1 3 0 0 4.17
## 98 1 3 0 0 2.71
## 99 1 8 0 0 3.48
## 100 1 5 0 1 2.86kesimpulan <- glm(y~x1+x2+x3+x4, family = binomial(link = "logit"),data = datagab)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(kesimpulan)##
## Call:
## glm(formula = y ~ x1 + x2 + x3 + x4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.639 7.647 -2.307 0.0211 *
## x1 4.672 1.836 2.544 0.0110 *
## x2 2.936 1.773 1.656 0.0977 .
## x3 1.693 1.285 1.317 0.1878
## x4 3.890 1.915 2.032 0.0422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 69.303 on 99 degrees of freedom
## Residual deviance: 18.090 on 95 degrees of freedom
## AIC: 28.09
##
## Number of Fisher Scoring iterations: 11`