Y : keputusan menolak/menerika 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 SMA, 1: Lulusan PT ) X4 : IPK (skala 4)
XI : Lama pengalaman kerja sebelumnya (bulan) Membangkitkan Variabel X1 dengan lama pengerjaan 0-60 bulan dengan nilai tengah 12 dan banyak 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 7X2 : Status pekerjaan keterangan yang digunakan (0 = Tidak bekerja) dan (1 = Beerja)
set.seed(100)
X2 <- round(runif(n))
X2 ## [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 1X3 : Tingkat Pendidikan keterangan yang digunakan (0 = lulus SMA/Tidak kuliah ) dan (1 = lulus kuliah)
set.seed(100)
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 adalah data IPK pelamar dengan skala 4
set.seed(100)
X4 <- round(rnorm(n, 3, 0.5),2)
X4## [1] 2.75 3.07 2.96 3.44 3.06 3.16 2.71 3.36 2.59 2.82 3.04 3.05 2.90 3.37 3.06
## [16] 2.99 2.81 3.26 2.54 4.16 2.78 3.38 3.13 3.39 2.59 2.78 2.64 3.12 2.42 3.12
## [31] 2.95 3.88 2.93 2.94 2.65 2.89 3.09 3.21 3.53 3.49 2.95 3.70 2.11 3.31 2.74
## [46] 3.66 2.82 3.66 3.02 2.06 2.78 2.13 3.09 3.95 1.86 3.49 2.30 3.91 3.69 2.58
## [61] 2.87 2.97 2.81 4.29 3.06 2.64 3.32 3.10 2.97 2.95 3.22 2.47 2.42 3.82 1.97
## [76] 3.01 2.46 3.14 3.50 1.96 3.45 2.98 2.33 2.03 3.35 2.92 3.11 3.41 3.86 2.95
## [91] 2.72 3.71 2.55 2.42 2.73 4.22 2.58 3.21 2.41 2.41###Menentukan Koef
b0 <- -10
b1 <- 3.5
b2 <- 0.5
b3 <- 1.7
b4 <- 1.2set.seed(100)
datapendukung <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datapendukung## [1] 0.300 -2.816 9.752 -5.872 4.172 4.292 23.452 1.032 9.308 -3.116
## [11] 13.348 34.360 0.480 4.544 20.372 16.788 -3.128 0.912 0.048 18.192
## [21] 9.536 17.256 9.956 20.768 3.608 -3.164 19.868 34.444 9.104 0.744
## [31] 4.040 42.356 0.516 48.228 16.380 34.168 -2.792 13.552 76.936 -2.312
## [41] 0.540 31.640 22.732 27.672 12.988 4.892 23.584 35.092 -2.876 -0.528
## [51] 0.336 -3.944 -2.792 1.740 8.432 -2.312 -3.740 -1.808 14.128 -3.404
## [61] 3.944 13.264 51.572 18.348 4.172 0.168 4.484 4.220 -2.936 16.740
## [71] 4.364 -0.036 9.104 56.284 12.064 13.312 30.152 20.468 27.900 -7.648
## [81] 4.640 13.276 40.496 64.636 -5.980 9.704 20.432 -2.408 1.632 20.240
## [91] 37.464 -2.048 0.060 3.404 37.476 2.064 9.296 -2.648 -7.108 19.592p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.5744425168 0.0564656650 0.9999418252 0.0028093187 0.9848128209
## [6] 0.9865070081 0.9999999999 0.7373034538 0.9999093125 0.0424520742
## [11] 0.9999984040 1.0000000000 0.6177478748 0.9894810269 0.9999999986
## [16] 0.9999999488 0.0419669449 0.7134092502 0.5119976965 0.9999999874
## [21] 0.9999278001 0.9999999680 0.9999525601 0.9999999990 0.9736093406
## [26] 0.0405431696 0.9999999976 1.0000000000 0.9998887923 0.6778699261
## [31] 0.9827068434 1.0000000000 0.6262119578 1.0000000000 0.9999999230
## [36] 1.0000000000 0.0577580149 0.9999986985 1.0000000000 0.0901339905
## [41] 0.6318124177 1.0000000000 0.9999999999 1.0000000000 0.9999977124
## [46] 0.9925495315 0.9999999999 1.0000000000 0.0533528010 0.3709834770
## [51] 0.5832185491 0.0190024881 0.0577580149 0.8506870655 0.9997822619
## [56] 0.0901339905 0.0232029381 0.1408800176 0.9999992684 0.0321706884
## [61] 0.9809975119 0.9999982641 1.0000000000 0.9999999892 0.9848128209
## [66] 0.5419014940 0.9888378304 0.9855142760 0.0504023772 0.9999999463
## [71] 0.9874325742 0.4910009719 0.9998887923 1.0000000000 0.9999942367
## [76] 0.9999983455 1.0000000000 0.9999999987 1.0000000000 0.0004767698
## [81] 0.9904346813 0.9999982848 1.0000000000 1.0000000000 0.0025224475
## [86] 0.9999389649 0.9999999987 0.0825646872 0.8364434345 0.9999999984
## [91] 1.0000000000 0.1142546262 0.5149955016 0.9678293116 1.0000000000
## [96] 0.8873546155 0.9999082178 0.0661123855 0.0008178610 0.9999999969set.seed(5)
y <- rbinom(n, 1, p)
y## [1] 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0
## [38] 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1datagab <- data.frame(y, X1, X2, X3, X4)
datagab## y X1 X2 X3 X4
## 1 1 2 0 0 2.75
## 2 0 1 0 0 3.07
## 3 1 4 1 1 2.96
## 4 0 0 0 0 3.44
## 5 1 3 0 0 3.06
## 6 1 3 0 0 3.16
## 7 1 8 1 1 2.71
## 8 0 2 0 0 3.36
## 9 1 4 1 1 2.59
## 10 0 1 0 0 2.82
## 11 1 5 1 1 3.04
## 12 1 11 1 1 3.05
## 13 1 2 0 0 2.90
## 14 1 3 0 0 3.37
## 15 1 7 1 1 3.06
## 16 1 6 1 1 2.99
## 17 0 1 0 0 2.81
## 18 0 2 0 0 3.26
## 19 0 2 0 0 2.54
## 20 1 6 1 1 4.16
## 21 1 4 1 1 2.78
## 22 1 6 1 1 3.38
## 23 1 4 1 1 3.13
## 24 1 7 1 1 3.39
## 25 1 3 0 0 2.59
## 26 0 1 0 0 2.78
## 27 1 7 1 1 2.64
## 28 1 11 1 1 3.12
## 29 1 4 1 1 2.42
## 30 0 2 0 0 3.12
## 31 1 3 0 0 2.95
## 32 1 13 1 1 3.88
## 33 1 2 0 0 2.93
## 34 1 15 1 1 2.94
## 35 1 6 1 1 2.65
## 36 1 11 1 1 2.89
## 37 0 1 0 0 3.09
## 38 1 5 1 1 3.21
## 39 1 23 1 1 3.53
## 40 0 1 0 0 3.49
## 41 1 2 0 0 2.95
## 42 1 10 1 1 3.70
## 43 1 8 1 1 2.11
## 44 1 9 1 1 3.31
## 45 1 5 1 1 2.74
## 46 1 3 0 0 3.66
## 47 1 8 1 1 2.82
## 48 1 11 1 1 3.66
## 49 0 1 0 0 3.02
## 50 1 2 0 0 2.06
## 51 1 2 0 0 2.78
## 52 0 1 0 0 2.13
## 53 0 1 0 0 3.09
## 54 1 2 0 0 3.95
## 55 1 4 1 1 1.86
## 56 1 1 0 0 3.49
## 57 0 1 0 0 2.30
## 58 1 1 0 0 3.91
## 59 1 5 1 1 3.69
## 60 0 1 0 0 2.58
## 61 1 3 0 0 2.87
## 62 1 5 1 1 2.97
## 63 1 16 1 1 2.81
## 64 1 6 1 1 4.29
## 65 1 3 0 0 3.06
## 66 1 2 0 0 2.64
## 67 1 3 0 0 3.32
## 68 0 3 0 0 3.10
## 69 1 1 0 0 2.97
## 70 1 6 1 1 2.95
## 71 1 3 0 0 3.22
## 72 0 2 0 0 2.47
## 73 1 4 1 1 2.42
## 74 1 17 1 1 3.82
## 75 1 5 1 1 1.97
## 76 1 5 1 1 3.01
## 77 1 10 1 1 2.46
## 78 1 7 1 1 3.14
## 79 1 9 1 1 3.50
## 80 0 0 0 0 1.96
## 81 1 3 0 0 3.45
## 82 1 5 1 1 2.98
## 83 1 13 1 1 2.33
## 84 1 20 1 1 2.03
## 85 0 0 0 0 3.35
## 86 1 4 1 1 2.92
## 87 1 7 1 1 3.11
## 88 1 1 0 0 3.41
## 89 1 2 0 0 3.86
## 90 1 7 1 1 2.95
## 91 1 12 1 1 2.72
## 92 0 1 0 0 3.71
## 93 1 2 0 0 2.55
## 94 1 3 0 0 2.42
## 95 1 12 1 1 2.73
## 96 0 2 0 0 4.22
## 97 1 4 1 1 2.58
## 98 0 1 0 0 3.21
## 99 0 0 0 0 2.41
## 100 1 7 1 1 2.41##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)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.4016 2.4569 -1.792 0.073204 .
## X1 1.8754 0.5182 3.619 0.000296 ***
## X2 14.0704 1717.0298 0.008 0.993462
## X3 NA NA NA NA
## X4 0.4034 0.7312 0.552 0.581112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 110.216 on 99 degrees of freedom
## Residual deviance: 46.849 on 96 degrees of freedom
## AIC: 54.849
##
## Number of Fisher Scoring iterations: 19