Y : Keputusan menolak/ menerima pelamar kerja pada PTA 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)
##Membangkitkan data X1 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(18000)
n<-(100)
u<- runif(n)
x1 <- round (60*(-(log(1-u)/12)))
x1## [1] 7 12 7 2 8 4 1 3 1 2 3 6 11 1 2 1 2 0 2 1 6 4 10 24 3
## [26] 27 15 6 2 1 3 2 0 3 6 6 12 1 4 5 9 1 24 2 3 4 2 1 3 0
## [51] 0 0 8 2 20 3 24 5 3 6 0 2 4 9 2 1 2 2 2 3 3 1 1 0 1
## [76] 8 11 2 0 1 5 4 2 4 0 4 17 1 5 2 1 1 1 5 6 6 15 5 5 2##Membangkitkan Data X2 X2 : status pekerjaan keterangan yang digunakan (0=Tidak Bekerja) dan (1=Bekerja)
set.seed(47)
X2 <- round(runif(n))
X2## [1] 1 0 1 1 1 1 0 0 1 1 0 1 0 1 1 1 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0
## [38] 1 0 1 0 0 1 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1
## [75] 1 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 0 1 0 0 0 1 0 1 1X3 : Tingkat Pendidikan Keterangan yang digunakan (0: Lulus Sekolah Menengah, 1: Lulusan Perguruan Tinggi)
set.seed(45)
X3 <- round(runif(n))
X3## [1] 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 1 0 1 1 1 0 0 0 0 1 0 1 1
## [38] 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 1 0 1 1 1 1 1
## [75] 0 1 1 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 1 1X4 Adalah data IPK pelamar dengan skala 4
set.seed(88)
X4<- round(rnorm(n,3,0.5),2)
X4## [1] 2.89 3.32 4.17 2.08 3.23 3.06 3.07 1.98 2.37 3.33 2.60 3.50 3.70 2.89 3.09
## [16] 2.57 3.17 3.40 2.93 3.46 3.13 2.61 2.63 2.42 3.45 2.57 2.68 2.29 2.83 2.58
## [31] 2.52 3.26 2.72 3.25 3.23 2.64 2.26 3.33 2.72 2.72 3.97 3.63 2.47 3.68 2.46
## [46] 3.12 3.70 3.46 3.28 2.59 2.89 3.14 2.00 3.16 2.47 2.78 2.30 3.19 3.75 3.24
## [61] 3.25 2.73 3.04 2.86 2.81 3.25 2.80 3.50 3.34 3.21 3.53 3.04 3.88 4.26 3.43
## [76] 3.28 3.24 2.96 2.60 2.47 2.65 3.17 3.09 3.36 3.03 3.04 2.70 2.71 2.21 2.60
## [91] 2.63 2.77 2.25 2.08 4.28 2.92 3.93 3.21 3.51 3.01b0<- -11
b1<- 3.5
b2<- 0.5
b3<- 2.7
b4<- 3.2set.seed(5)
datapendukung <- b0+(b1*x1)+(b2*X2)+(b3*+X3)+(b4*+X4)
datapendukung## [1] 25.948 41.624 27.344 3.156 27.836 13.292 2.324 8.536 0.584 7.156
## [11] 7.820 24.400 39.340 2.248 6.388 1.224 8.844 -0.120 5.376 3.572
## [21] 20.016 11.852 35.616 83.444 13.740 91.724 52.776 20.028 8.256 1.256
## [31] 8.064 6.432 -2.296 12.600 20.836 21.148 40.932 3.656 11.704 15.704
## [41] 33.204 6.816 84.104 10.976 7.372 15.684 8.340 6.772 9.996 -2.212
## [51] -1.752 2.248 23.400 8.812 69.604 8.896 83.060 16.708 14.700 20.368
## [61] 2.600 7.436 13.228 29.652 4.992 3.400 8.160 9.900 7.188 12.472
## [71] 13.996 4.928 7.616 5.832 3.976 30.196 41.068 8.172 0.520 0.404
## [81] 15.480 13.644 5.888 16.952 1.396 13.228 60.340 1.672 16.272 4.820
## [91] 3.616 4.064 2.900 15.856 23.696 19.344 57.276 19.472 20.932 8.832p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 1.00000000 1.00000000 1.00000000 0.95914449 1.00000000 0.99999831
## [7] 0.91084530 0.99980376 0.64198729 0.99922044 0.99959854 1.00000000
## [13] 1.00000000 0.90447788 0.99832121 0.77276671 0.99985578 0.47003595
## [19] 0.99539501 0.97266841 1.00000000 0.99999288 1.00000000 1.00000000
## [25] 0.99999892 1.00000000 1.00000000 1.00000000 0.99974037 0.77833676
## [31] 0.99968543 0.99839336 0.09145478 0.99999663 1.00000000 1.00000000
## [37] 1.00000000 0.97481502 0.99999174 0.99999985 1.00000000 0.99890510
## [43] 1.00000000 0.99998289 0.99937179 0.99999985 0.99976128 0.99885591
## [49] 0.99995442 0.09867805 0.14779512 0.90447788 1.00000000 0.99985109
## [55] 1.00000000 0.99986308 1.00000000 0.99999994 0.99999959 1.00000000
## [61] 0.93086158 0.99941071 0.99999820 1.00000000 0.99325375 0.96770454
## [67] 0.99971422 0.99994983 0.99924497 0.99999617 0.99999917 0.99281108
## [73] 0.99950773 0.99707637 0.98158494 1.00000000 1.00000000 0.99971763
## [79] 0.62714777 0.59964832 0.99999981 0.99999881 0.99723515 0.99999996
## [85] 0.80154838 0.99999820 1.00000000 0.84184229 0.99999991 0.99199777
## [91] 0.97381412 0.98311001 0.94784644 0.99999987 1.00000000 1.00000000
## [97] 1.00000000 1.00000000 1.00000000 0.99985404set.seed(80)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 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 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1datagab <- data.frame(y, x1,X2,X3,X4)
datagab## y x1 X2 X3 X4
## 1 1 7 1 1 2.89
## 2 1 12 0 0 3.32
## 3 1 7 1 0 4.17
## 4 1 2 1 0 2.08
## 5 1 8 1 0 3.23
## 6 1 4 1 0 3.06
## 7 1 1 0 0 3.07
## 8 1 3 0 1 1.98
## 9 1 1 1 0 2.37
## 10 1 2 1 0 3.33
## 11 1 3 0 0 2.60
## 12 1 6 1 1 3.50
## 13 1 11 0 0 3.70
## 14 1 1 1 0 2.89
## 15 1 2 1 0 3.09
## 16 0 1 1 0 2.57
## 17 1 2 0 1 3.17
## 18 0 0 0 0 3.40
## 19 1 2 0 0 2.93
## 20 1 1 0 0 3.46
## 21 1 6 0 0 3.13
## 22 1 4 1 0 2.61
## 23 1 10 1 1 2.63
## 24 1 24 0 1 2.42
## 25 1 3 1 1 3.45
## 26 1 27 0 0 2.57
## 27 1 15 0 1 2.68
## 28 1 6 0 1 2.29
## 29 1 2 1 1 2.83
## 30 1 1 1 0 2.58
## 31 1 3 1 0 2.52
## 32 1 2 0 0 3.26
## 33 0 0 0 0 2.72
## 34 1 3 0 1 3.25
## 35 1 6 1 0 3.23
## 36 1 6 0 1 2.64
## 37 1 12 0 1 2.26
## 38 1 1 1 0 3.33
## 39 1 4 0 0 2.72
## 40 1 5 1 0 2.72
## 41 1 9 0 0 3.97
## 42 1 1 0 1 3.63
## 43 1 24 1 1 2.47
## 44 1 2 1 1 3.68
## 45 1 3 0 0 2.46
## 46 1 4 0 1 3.12
## 47 1 2 1 0 3.70
## 48 1 1 1 1 3.46
## 49 1 3 0 0 3.28
## 50 0 0 1 0 2.59
## 51 0 0 0 0 2.89
## 52 1 0 1 1 3.14
## 53 1 8 0 0 2.00
## 54 1 2 0 1 3.16
## 55 1 20 0 1 2.47
## 56 1 3 1 0 2.78
## 57 1 24 0 1 2.30
## 58 1 5 0 0 3.19
## 59 1 3 1 1 3.75
## 60 1 6 0 0 3.24
## 61 1 0 1 1 3.25
## 62 1 2 0 1 2.73
## 63 1 4 1 0 3.04
## 64 1 9 0 0 2.86
## 65 1 2 0 0 2.81
## 66 1 1 1 0 3.25
## 67 1 2 1 1 2.80
## 68 1 2 0 1 3.50
## 69 1 2 1 0 3.34
## 70 1 3 0 1 3.21
## 71 1 3 1 1 3.53
## 72 1 1 0 1 3.04
## 73 1 1 0 1 3.88
## 74 1 0 1 1 4.26
## 75 1 1 1 0 3.43
## 76 1 8 0 1 3.28
## 77 1 11 1 1 3.24
## 78 1 2 0 1 2.96
## 79 1 0 1 1 2.60
## 80 0 1 0 0 2.47
## 81 1 5 1 0 2.65
## 82 1 4 1 0 3.17
## 83 1 2 0 0 3.09
## 84 1 4 1 1 3.36
## 85 1 0 0 1 3.03
## 86 1 4 1 0 3.04
## 87 1 17 1 1 2.70
## 88 0 1 1 0 2.71
## 89 1 5 0 1 2.21
## 90 1 2 1 0 2.60
## 91 1 1 0 1 2.63
## 92 1 1 0 1 2.77
## 93 1 1 1 1 2.25
## 94 1 5 0 1 2.08
## 95 1 6 0 0 4.28
## 96 1 6 0 0 2.92
## 97 1 15 1 1 3.93
## 98 1 5 0 1 3.21
## 99 1 5 1 1 3.51
## 100 1 2 1 1 3.01modelreglog <- 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) -34.700 4882.919 -0.007 0.994
## x1 21.592 4882.908 0.004 0.996
## X2 1.123 2.043 0.550 0.582
## X3 42.107 17959.153 0.002 0.998
## X4 4.770 3.219 1.482 0.138
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 50.7278 on 99 degrees of freedom
## Residual deviance: 8.8474 on 95 degrees of freedom
## AIC: 18.847
##
## Number of Fisher Scoring iterations: 24