Y : Kapasitas menolak/menerima pelamar kerja pada PT A posisi B x1 : Lama pengalaman kerja sebelumnya (sebulan) 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 0-40 bulan dengan nilai tengah 12 dan banyak pelamar adalah 100
set.seed(123)
n <- 100
u <- runif(n)
x1 <- round(60*(-log(1-u)/12))
x1## [1] 2 8 3 11 14 0 4 11 4 3 16 3 6 4 1 12 1 0 2 15 11 6 5 26 5
## [26] 6 4 5 2 1 16 12 6 8 0 3 7 1 2 1 1 3 3 2 1 1 1 3 2 10
## [51] 0 3 8 1 4 1 1 7 11 2 5 0 2 2 8 3 8 8 8 3 7 5 6 0 3
## [76] 1 2 5 2 1 1 6 3 8 1 3 21 11 11 1 1 5 2 5 2 1 8 0 3 4x2: Status pekerjaan Keterangan yang digantikan (0=Tidak bekerja) dan (1=Bekerja)
set.seed(157)
x2 <- round(runif(n))
x2## [1] 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 0 1
## [38] 0 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 0
## [75] 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0x3: Tingkat pendidikan Keterangan yang digunakan (0=Lulus SMA/Tidak kuliah) dan (1=Lulus kuliah)
set.seed(111)
x3 <- round(runif(n))
x3## [1] 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0
## [38] 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0
## [75] 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1x4 adalah data IPK pelamar dengan skala 4
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.10 3.78 3.46 3.18 3.09 2.58 3.49 3.90 3.06 2.94 2.89 3.72 3.20 3.46 3.72
## [16] 2.81 3.10 2.60 3.15 3.70 3.51 3.24 2.66 3.08 2.81 3.47 2.68 2.95 3.52 3.19
## [31] 2.37 2.61 3.21 2.81 2.39 3.51 3.22 2.38 2.70 3.33 4.03 3.25 2.13 3.36 3.01
## [46] 2.30 3.63 2.94 2.64 2.39 3.30 2.42 3.22 3.10 2.65 2.54 2.49 3.30 3.87 2.83
## [61] 3.60 3.51 2.98 3.69 1.90 2.94 2.34 2.29 3.44 3.20 3.34 4.18 2.49 3.15 3.78
## [76] 2.58 3.66 3.24 3.09 3.13 4.30 3.54 2.20 2.96 3.18 2.56 1.34 2.77 3.22 2.70
## [91] 3.34 3.32 2.69 3.21 3.44 3.01 3.91 2.77 2.94 3.38metentukan koef
b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 1.2set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 2.920 24.236 4.152 34.516 42.208 -7.404 7.688 34.880 7.172 3.528
## [11] 51.168 6.664 13.840 7.652 -3.036 34.372 -3.280 -5.180 0.280 49.140
## [21] 31.712 14.388 10.192 83.696 13.072 14.664 9.416 10.040 2.924 -0.472
## [31] 48.344 37.332 13.852 20.872 -8.132 6.412 17.864 -1.944 2.440 -0.804
## [41] 0.536 3.400 5.256 3.232 -0.688 -4.740 -2.644 5.728 2.368 30.068
## [51] -4.340 5.104 20.864 -3.780 9.380 -1.752 -1.812 17.960 32.144 2.596
## [61] 11.320 -6.288 -0.424 0.928 21.980 3.028 20.308 19.748 21.128 3.840
## [71] 20.208 14.216 12.988 -7.220 7.236 -3.904 0.892 13.588 -0.292 -1.044
## [81] -1.840 17.448 2.140 21.052 -3.184 3.072 67.308 34.024 34.564 -1.560
## [91] -0.792 13.184 1.928 13.052 2.828 -3.888 24.892 -7.176 5.728 9.756p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.9488262991 1.0000000000 0.9845107716 1.0000000000 1.0000000000
## [6] 0.0006084422 0.9995419161 1.0000000000 0.9992328034 0.9714740401
## [11] 1.0000000000 0.9987255942 0.9999990242 0.9995251326 0.0458257564
## [16] 1.0000000000 0.0362637164 0.0055965092 0.5695462239 1.0000000000
## [21] 1.0000000000 0.9999994359 0.9999625325 1.0000000000 0.9999978967
## [26] 0.9999995719 0.9999185956 0.9999563821 0.9490201706 0.3841429798
## [31] 1.0000000000 1.0000000000 0.9999990358 0.9999999991 0.0002938933
## [36] 0.9983609531 0.9999999826 0.1252090722 0.9198270878 0.3091705309
## [41] 0.6308814262 0.9677045353 0.9948109392 0.9620208939 0.3344781308
## [46] 0.0086629438 0.0663597807 0.9967569736 0.9143543694 1.0000000000
## [51] 0.0128687642 0.9939642433 0.9999999991 0.0223134387 0.9999156119
## [56] 0.1477951171 0.1403965813 0.9999999841 1.0000000000 0.9306037024
## [61] 0.9999878722 0.0018550257 0.3955599826 0.7166693528 0.9999999997
## [66] 0.9538231638 0.9999999985 0.9999999973 0.9999999993 0.9789586530
## [71] 0.9999999983 0.9999993300 0.9999977124 0.0007312673 0.9992803315
## [76] 0.0197626683 0.7093027299 0.9999987445 0.4275143046 0.2603789289
## [81] 0.1370512926 0.9999999735 0.8947306105 0.9999999993 0.0397722906
## [86] 0.9557228829 1.0000000000 1.0000000000 1.0000000000 0.1736466470
## [91] 0.3117393920 0.9999981196 0.8730278848 0.9999978542 0.9441702701
## [96] 0.0200750153 1.0000000000 0.0007641363 0.9967569736 0.9999420574set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1
## [38] 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0
## [75] 1 0 1 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 1 1 3.10
## 2 1 8 0 1 3.78
## 3 1 3 1 0 3.46
## 4 1 11 1 1 3.18
## 5 1 14 1 0 3.09
## 6 0 0 1 0 2.58
## 7 1 4 1 0 3.49
## 8 1 11 0 1 3.90
## 9 1 4 1 0 3.06
## 10 1 3 1 0 2.94
## 11 1 16 0 1 2.89
## 12 1 3 0 1 3.72
## 13 1 6 0 0 3.20
## 14 1 4 1 0 3.46
## 15 0 1 0 0 3.72
## 16 1 12 0 0 2.81
## 17 0 1 1 0 3.10
## 18 0 0 0 1 2.60
## 19 0 2 1 0 3.15
## 20 1 15 1 1 3.70
## 21 1 11 0 0 3.51
## 22 1 6 1 0 3.24
## 23 1 5 1 0 2.66
## 24 1 26 0 0 3.08
## 25 1 5 1 1 2.81
## 26 1 6 1 0 3.47
## 27 1 4 1 1 2.68
## 28 1 5 0 0 2.95
## 29 1 2 0 1 3.52
## 30 0 1 1 1 3.19
## 31 1 16 1 0 2.37
## 32 1 12 1 1 2.61
## 33 1 6 0 0 3.21
## 34 1 8 1 0 2.81
## 35 0 0 0 0 2.39
## 36 1 3 0 1 3.51
## 37 1 7 1 0 3.22
## 38 0 1 0 1 2.38
## 39 1 2 1 1 2.70
## 40 1 1 0 1 3.33
## 41 1 1 1 1 4.03
## 42 1 3 0 0 3.25
## 43 1 3 1 1 2.13
## 44 1 2 1 1 3.36
## 45 1 1 1 1 3.01
## 46 0 1 0 0 2.30
## 47 1 1 1 0 3.63
## 48 1 3 0 1 2.94
## 49 1 2 1 1 2.64
## 50 1 10 1 1 2.39
## 51 0 0 0 1 3.30
## 52 1 3 0 1 2.42
## 53 1 8 0 0 3.22
## 54 0 1 0 0 3.10
## 55 1 4 1 1 2.65
## 56 0 1 0 1 2.54
## 57 0 1 0 1 2.49
## 58 1 7 1 0 3.30
## 59 1 11 0 0 3.87
## 60 1 2 1 1 2.83
## 61 1 5 1 0 3.60
## 62 0 0 1 0 3.51
## 63 1 2 0 0 2.98
## 64 0 2 1 0 3.69
## 65 1 8 0 1 1.90
## 66 1 3 0 0 2.94
## 67 1 8 1 0 2.34
## 68 1 8 0 0 2.29
## 69 1 8 0 0 3.44
## 70 1 3 1 0 3.20
## 71 1 7 0 1 3.34
## 72 1 5 0 1 4.18
## 73 1 6 0 0 2.49
## 74 0 0 0 0 3.15
## 75 1 3 1 1 3.78
## 76 0 1 1 0 2.58
## 77 1 2 1 0 3.66
## 78 1 5 1 1 3.24
## 79 0 2 0 0 3.09
## 80 0 1 0 1 3.13
## 81 0 1 1 0 4.30
## 82 1 6 1 1 3.54
## 83 1 3 0 0 2.20
## 84 1 8 1 0 2.96
## 85 0 1 1 0 3.18
## 86 1 3 1 0 2.56
## 87 1 21 1 1 1.34
## 88 1 11 1 1 2.77
## 89 1 11 1 1 3.22
## 90 0 1 0 1 2.70
## 91 0 1 0 1 3.34
## 92 1 5 0 1 3.32
## 93 1 2 0 1 2.69
## 94 1 5 0 1 3.21
## 95 1 2 0 1 3.44
## 96 0 1 0 0 3.01
## 97 1 8 1 1 3.91
## 98 0 0 1 0 2.77
## 99 1 3 0 1 2.94
## 100 1 4 0 1 3.38modelreglog <- 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) -13.7005 5.9770 -2.292 0.0219 *
## x1 3.9099 1.2074 3.238 0.0012 **
## x2 0.7892 1.1754 0.671 0.5019
## x3 3.4553 1.5241 2.267 0.0234 *
## x4 1.7448 1.4251 1.224 0.2208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 112.467 on 99 degrees of freedom
## Residual deviance: 24.251 on 95 degrees of freedom
## AIC: 34.251
##
## Number of Fisher Scoring iterations: 10