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: Tidak Kuliah, 1: Lulus Kuliah) X4 : IPK (Skala 4)
#Membangkitkan data X1 X1 : Lama pengalaman kerja sebelumnya (bulan) Membangkitkan variabel X1 dengan lama pekerjaan 0-60 bukan dengan nilai tengah 12 dan 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 7#Membangkitkan data X2 X2 : Status pekerjaan keterangan yang digunakan (0=Tidak Bekerja) dan (1=Tidak 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 0##Membangkitkan data X3 X3 : Tingkat pendidikan Keterangan yang digunakan (0: Tidak Kuliah, 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 1##Membangkitkan data X4 X4 : IPK (Skala 4)
set.seed(222)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.74 3.00 3.69 2.81 3.09 2.88 2.39 3.78 3.21 2.40 3.53 2.35 2.65 3.30 2.90
## [16] 2.41 2.00 3.00 3.26 2.63 3.36 3.36 2.67 3.75 2.28 1.92 3.20 2.80 2.85 3.67
## [31] 2.59 3.34 2.89 2.94 2.90 3.20 3.33 3.05 2.91 3.47 3.10 3.25 2.72 3.56 4.10
## [46] 3.16 2.53 3.41 2.81 3.17 3.30 3.26 2.52 2.39 2.90 3.53 3.19 3.62 3.16 2.48
## [61] 2.43 3.62 3.39 3.37 3.03 3.42 3.10 3.73 2.77 1.61 3.03 2.97 2.41 1.74 3.41
## [76] 3.13 2.97 3.34 3.01 3.27 3.34 2.40 2.38 3.11 2.27 2.94 3.27 3.36 2.21 3.55
## [91] 2.83 3.31 3.25 3.84 3.19 3.12 3.21 2.41 2.68 3.03##Membangkitkan data Y
###Menetukan koef
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] 1.988 0.300 8.928 -3.428 6.908 3.956 23.568 4.736 11.052 -3.120
## [11] 14.436 31.320 3.380 7.160 17.980 16.592 -4.100 0.600 0.912 17.356
## [21] 10.732 17.732 10.404 22.200 6.436 -1.496 21.540 35.060 7.420 1.404
## [31] 6.808 42.208 3.168 49.228 14.480 32.340 0.696 11.660 74.492 -2.336
## [41] 1.220 28.900 21.764 26.272 12.420 4.292 21.036 32.592 -3.128 4.004
## [51] 1.460 -2.088 -0.776 -0.132 10.680 -2.264 -2.172 0.544 13.992 -3.524
## [61] 6.616 11.844 50.568 15.544 7.336 1.104 7.420 7.676 0.024 13.432
## [71] 7.336 3.764 9.592 51.588 11.592 11.756 29.064 21.708 25.612 -6.076
## [81] 5.008 13.580 38.356 66.432 -7.276 7.528 21.624 0.732 2.852 18.760
## [91] 35.896 0.672 0.900 8.308 36.328 1.244 11.052 -3.108 -6.784 20.836p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.8795313865 0.5744425168 0.9998673946 0.0314317628 0.9990012432
## [6] 0.9812199222 0.9999999999 0.9913026370 0.9999841448 0.0422897718
## [11] 0.9999994623 1.0000000000 0.9670736052 0.9992235488 0.9999999845
## [16] 0.9999999377 0.0163024994 0.6456563062 0.7134092502 0.9999999710
## [21] 0.9999781656 0.9999999801 0.9999696899 0.9999999998 0.9983997598
## [26] 0.1830228676 0.9999999996 1.0000000000 0.9994012096 0.8028178610
## [31] 0.9988963190 1.0000000000 0.9596121424 1.0000000000 0.9999994855
## [36] 1.0000000000 0.6673003248 0.9999913678 1.0000000000 0.0881850191
## [41] 0.7720635494 1.0000000000 0.9999999996 1.0000000000 0.9999959630
## [46] 0.9865070081 0.9999999993 1.0000000000 0.0419669449 0.9820843048
## [51] 0.8115326748 0.1102686404 0.3151826173 0.4670478327 0.9999770002
## [56] 0.0941486759 0.1022932287 0.6327424281 0.9999991618 0.0286370181
## [61] 0.9986630146 0.9999928185 1.0000000000 0.9999998224 0.9993487725
## [66] 0.7510088346 0.9994012096 0.9995363885 0.5059997120 0.9999985326
## [71] 0.9993487725 0.9773348318 0.9999317319 1.0000000000 0.9999907604
## [76] 0.9999921579 1.0000000000 0.9999999996 1.0000000000 0.0022920820
## [81] 0.9933601242 0.9999987344 1.0000000000 1.0000000000 0.0006914694
## [86] 0.9994624763 0.9999999996 0.6752440053 0.9454219733 0.9999999929
## [91] 1.0000000000 0.6619508479 0.7109495026 0.9997535241 1.0000000000
## [96] 0.7762595046 0.9999841448 0.0427784663 0.0011304595 0.9999999991set.seed(5)
y <- rbinom(n,1,p)
y## [1] 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1
## [38] 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 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 1 1 1 1 1 1 1 1 1 0 0 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 1 0 3.74
## 2 0 1 1 1 3.00
## 3 1 4 1 0 3.69
## 4 0 0 1 1 2.81
## 5 1 3 0 1 3.09
## 6 1 3 0 0 2.88
## 7 1 8 0 1 2.39
## 8 1 2 1 1 3.78
## 9 1 4 1 1 3.21
## 10 0 1 1 0 2.40
## 11 1 5 0 1 3.53
## 12 1 11 0 0 2.35
## 13 1 2 1 1 2.65
## 14 1 3 0 1 3.30
## 15 1 7 0 0 2.90
## 16 1 6 0 1 2.41
## 17 0 1 0 0 2.00
## 18 0 2 0 0 3.00
## 19 1 2 0 0 3.26
## 20 1 6 1 1 2.63
## 21 1 4 0 1 3.36
## 22 1 6 0 1 3.36
## 23 1 4 1 1 2.67
## 24 1 7 1 1 3.75
## 25 1 3 1 1 2.28
## 26 0 1 0 1 1.92
## 27 1 7 1 1 3.20
## 28 1 11 1 1 2.80
## 29 1 4 0 0 2.85
## 30 0 2 0 0 3.67
## 31 1 3 1 1 2.59
## 32 1 13 0 1 3.34
## 33 1 2 0 1 2.89
## 34 1 15 1 1 2.94
## 35 1 6 0 0 2.90
## 36 1 11 0 0 3.20
## 37 1 1 1 1 3.33
## 38 1 5 1 0 3.05
## 39 1 23 1 0 2.91
## 40 0 1 0 0 3.47
## 41 1 2 1 0 3.10
## 42 1 10 0 0 3.25
## 43 1 8 1 0 2.72
## 44 1 9 1 0 3.56
## 45 1 5 0 0 4.10
## 46 1 3 0 0 3.16
## 47 1 8 0 0 2.53
## 48 1 11 0 0 3.41
## 49 0 1 0 0 2.81
## 50 1 2 1 1 3.17
## 51 1 2 1 0 3.30
## 52 0 1 1 0 3.26
## 53 0 1 0 1 2.52
## 54 0 2 0 0 2.39
## 55 1 4 1 1 2.90
## 56 1 1 0 0 3.53
## 57 1 1 1 0 3.19
## 58 0 1 0 1 3.62
## 59 1 5 0 1 3.16
## 60 0 1 0 0 2.48
## 61 1 3 1 1 2.43
## 62 1 5 0 0 3.62
## 63 1 16 1 0 3.39
## 64 1 6 1 0 3.37
## 65 1 3 1 1 3.03
## 66 1 2 0 0 3.42
## 67 1 3 1 1 3.10
## 68 1 3 0 1 3.73
## 69 0 1 1 1 2.77
## 70 1 6 1 0 1.61
## 71 1 3 1 1 3.03
## 72 1 2 1 1 2.97
## 73 1 4 0 1 2.41
## 74 1 17 0 0 1.74
## 75 1 5 0 0 3.41
## 76 1 5 1 0 3.13
## 77 1 10 1 0 2.97
## 78 1 7 1 1 3.34
## 79 1 9 1 0 3.01
## 80 0 0 0 0 3.27
## 81 1 3 1 0 3.34
## 82 1 5 1 1 2.40
## 83 1 13 0 0 2.38
## 84 1 20 0 1 3.11
## 85 0 0 0 0 2.27
## 86 1 4 0 0 2.94
## 87 1 7 1 1 3.27
## 88 0 1 1 1 3.36
## 89 1 2 1 1 2.21
## 90 1 7 0 0 3.55
## 91 1 12 1 0 2.83
## 92 1 1 1 1 3.31
## 93 1 2 0 0 3.25
## 94 1 3 1 1 3.84
## 95 1 12 1 0 3.19
## 96 1 2 1 0 3.12
## 97 1 4 1 1 3.21
## 98 0 1 1 0 2.41
## 99 0 0 0 0 2.68
## 100 1 7 0 1 3.03##Analisis Regresi Logistik
modelreglog <- 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)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.12587 0.00000 0.00042 0.04459 2.10764
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -15.6743 6.4121 -2.444 0.01451 *
## x1 3.8711 1.2942 2.991 0.00278 **
## x2 2.5851 1.4053 1.840 0.06583 .
## x3 0.5468 1.1232 0.487 0.62636
## x4 2.7470 1.5582 1.763 0.07792 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 100.080 on 99 degrees of freedom
## Residual deviance: 24.495 on 95 degrees of freedom
## AIC: 34.495
##
## Number of Fisher Scoring iterations: 10