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:lulusam perguruan tinggi) X4 : IPK (skala 4)
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(100)
n <- 100
u <- runif(n)
x1 <- round(60*(-(log(1-u)/10)))
x1 ## [1] 2 2 5 0 4 4 10 3 5 1 6 13 2 3 9 7 1 3 3 7 5 7 5 8 3
## [26] 1 9 13 5 2 4 16 3 18 7 13 1 6 27 1 2 12 9 11 6 4 9 13 1 2
## [51] 2 1 2 2 5 2 1 2 5 1 4 6 19 7 4 3 4 4 2 7 3 2 5 20 7
## [76] 6 12 9 11 1 4 5 15 24 0 5 8 2 2 8 14 1 3 4 14 3 4 1 0 9X2 : status pekerjaan keterangan yang digunakan (0:bekerja, 1:tidak bekerja)
set.seed(1234)
x2 <- round(runif(n))
x2 ## [1] 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0
## [38] 0 1 1 1 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1
## [75] 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1X3 : tingkat pendidikan keterangan yang digunakan (0:lulus SMA/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 1X4 : IPK pelamar dengan skala 4
x4 <- round(rnorm(n, 3, 0.5),3)
x4 ## [1] 3.127 2.986 2.979 3.684 2.887 3.758 2.226 3.292 3.062 3.108 3.190 2.749
## [13] 2.833 2.491 2.464 3.152 3.224 3.027 3.461 4.025 2.754 1.845 3.503 2.645
## [25] 2.656 3.513 2.858 2.390 3.091 2.931 3.003 3.193 2.815 3.322 2.890 3.166
## [37] 3.548 3.218 2.837 3.574 3.497 3.274 3.119 2.686 3.680 2.700 4.094 3.766
## [49] 2.882 2.487 2.645 3.128 2.877 2.826 2.524 2.977 2.608 2.166 2.810 3.459
## [61] 2.712 3.304 2.191 2.972 3.260 3.151 3.053 2.680 2.575 2.488 3.059 2.526
## [73] 2.755 2.872 3.922 2.674 3.118 3.039 2.519 2.964 3.722 3.226 3.021 2.789
## [85] 1.973 3.566 2.270 3.370 3.955 2.278 3.351 2.869 2.214 2.243 2.199 2.735
## [97] 2.269 3.344 4.050 2.356b0 <- -11
b1 <- 1.5
b2 <- 2.5
b3 <- 1.7
b4 <- 2.2 set.seed(1)
datapendukung <- b0 +(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] -1.1206 2.7692 5.5538 1.3048 5.5514 5.7676 10.5972 2.4424 7.4364
## [10] -0.1624 9.2180 17.0478 -0.0674 3.1802 7.9208 10.6344 -2.4072 0.1594
## [19] 1.1142 10.0550 4.2588 5.2590 5.9066 8.5190 1.0432 2.4286 12.9876
## [28] 17.9580 5.8002 -1.5518 3.3066 21.7246 1.3930 27.5084 5.8580 17.9652
## [37] 0.0056 5.0796 38.2414 0.8628 2.1934 16.7028 9.3618 13.9092 6.0960
## [46] 3.4400 14.0068 16.7852 -3.1596 1.6714 -2.1810 -2.6184 2.5294 0.7172
## [55] 3.7528 1.0494 -3.7624 0.9652 4.3820 0.6098 5.1664 5.2688 22.3202
## [64] 6.0384 3.8720 2.9322 3.4166 5.0960 -0.6350 7.4736 1.9298 1.7572
## [73] 4.2610 27.8184 8.1284 6.3828 13.8596 10.8858 11.0418 -0.4792 5.6884
## [82] 5.2972 18.1462 35.3358 -6.6594 6.8452 7.6940 1.1140 2.4010 8.5116
## [91] 17.3722 1.0118 -1.6292 1.6346 14.8378 2.0170 1.6918 -2.1432 -2.0900
## [100] 11.8832p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.245900007 0.940988579 0.996142227 0.786641707 0.996132993 0.996882495
## [7] 0.999975015 0.920003898 0.999410944 0.459488997 0.999900773 0.999999961
## [13] 0.483156376 0.960082332 0.999637020 0.999975927 0.082625306 0.539765837
## [19] 0.752911292 0.999957031 0.986057872 0.994826403 0.997285962 0.999800401
## [25] 0.739466976 0.918982359 0.999997711 0.999999984 0.996982185 0.174826444
## [31] 0.964654537 1.000000000 0.801070744 1.000000000 0.997151187 0.999999984
## [37] 0.501399996 0.993816081 1.000000000 0.703245322 0.899655261 0.999999944
## [43] 0.999914062 0.999999089 0.997753202 0.968931516 0.999999174 0.999999949
## [49] 0.040714673 0.841762388 0.101469718 0.067963575 0.926177340 0.671990140
## [55] 0.977085405 0.740659666 0.022700638 0.724161723 0.987653996 0.647895178
## [61] 0.994327293 0.994876598 1.000000000 0.997620304 0.979607804 0.949415436
## [67] 0.968219318 0.993916059 0.346377669 0.999432442 0.873227281 0.852858633
## [73] 0.986088085 1.000000000 0.999705047 0.998312469 0.999999043 0.999981278
## [79] 0.999983982 0.382441051 0.996626415 0.995019341 0.999999987 1.000000000
## [85] 0.001280274 0.998936579 0.999544655 0.752874083 0.916903527 0.999798919
## [91] 0.999999971 0.733372264 0.163939983 0.836798819 0.999999640 0.882570446
## [97] 0.844460731 0.104968369 0.110072574 0.999993095set.seed(2)
Y <- rbinom(n,1, p)
Y## [1] 0 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 1 0 1 1 0 1 1 1 0
## [38] 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 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 0 0 1 1 1 1 0 0 1datagab <- data.frame(n, x1, x2, x3, x4)
datagab## n x1 x2 x3 x4
## 1 100 2 0 0 3.127
## 2 100 2 1 1 2.986
## 3 100 5 1 0 2.979
## 4 100 0 1 1 3.684
## 5 100 4 1 1 2.887
## 6 100 4 1 0 3.758
## 7 100 10 0 1 2.226
## 8 100 3 0 1 3.292
## 9 100 5 1 1 3.062
## 10 100 1 1 0 3.108
## 11 100 6 1 1 3.190
## 12 100 13 1 0 2.749
## 13 100 2 0 1 2.833
## 14 100 3 1 1 2.491
## 15 100 9 0 0 2.464
## 16 100 7 1 1 3.152
## 17 100 1 0 0 3.224
## 18 100 3 0 0 3.027
## 19 100 3 0 0 3.461
## 20 100 7 0 1 4.025
## 21 100 5 0 1 2.754
## 22 100 7 0 1 1.845
## 23 100 5 0 1 3.503
## 24 100 8 0 1 2.645
## 25 100 3 0 1 2.656
## 26 100 1 1 1 3.513
## 27 100 9 1 1 2.858
## 28 100 13 1 1 2.390
## 29 100 5 1 0 3.091
## 30 100 2 0 0 2.931
## 31 100 4 0 1 3.003
## 32 100 16 0 1 3.193
## 33 100 3 0 1 2.815
## 34 100 18 1 1 3.322
## 35 100 7 0 0 2.890
## 36 100 13 1 0 3.166
## 37 100 1 0 1 3.548
## 38 100 6 0 0 3.218
## 39 100 27 1 0 2.837
## 40 100 1 1 0 3.574
## 41 100 2 1 0 3.497
## 42 100 12 1 0 3.274
## 43 100 9 0 0 3.119
## 44 100 11 1 0 2.686
## 45 100 6 0 0 3.680
## 46 100 4 1 0 2.700
## 47 100 9 1 0 4.094
## 48 100 13 0 0 3.766
## 49 100 1 0 0 2.882
## 50 100 2 1 1 2.487
## 51 100 2 0 0 2.645
## 52 100 1 0 0 3.128
## 53 100 2 1 1 2.877
## 54 100 2 1 0 2.826
## 55 100 5 0 1 2.524
## 56 100 2 1 0 2.977
## 57 100 1 0 0 2.608
## 58 100 2 1 1 2.166
## 59 100 5 0 1 2.810
## 60 100 1 1 0 3.459
## 61 100 4 1 1 2.712
## 62 100 6 0 0 3.304
## 63 100 19 0 0 2.191
## 64 100 7 0 0 2.972
## 65 100 4 0 1 3.260
## 66 100 3 1 0 3.151
## 67 100 4 0 1 3.053
## 68 100 4 1 1 2.680
## 69 100 2 0 1 2.575
## 70 100 7 1 0 2.488
## 71 100 3 0 1 3.059
## 72 100 2 1 1 2.526
## 73 100 5 0 1 2.755
## 74 100 20 1 0 2.872
## 75 100 7 0 0 3.922
## 76 100 6 1 0 2.674
## 77 100 12 0 0 3.118
## 78 100 9 0 1 3.039
## 79 100 11 0 0 2.519
## 80 100 1 1 0 2.964
## 81 100 4 1 0 3.722
## 82 100 5 0 1 3.226
## 83 100 15 0 0 3.021
## 84 100 24 1 1 2.789
## 85 100 0 0 0 1.973
## 86 100 5 1 0 3.566
## 87 100 8 0 1 2.270
## 88 100 2 0 1 3.370
## 89 100 2 0 1 3.955
## 90 100 8 1 0 2.278
## 91 100 14 0 0 3.351
## 92 100 1 1 1 2.869
## 93 100 3 0 0 2.214
## 94 100 4 0 1 2.243
## 95 100 14 0 0 2.199
## 96 100 3 1 0 2.735
## 97 100 4 0 1 2.269
## 98 100 1 0 0 3.344
## 99 100 0 0 0 4.050
## 100 100 9 1 1 2.356modelreglog <- 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) -13.3382 4.4432 -3.002 0.00268 **
## x1 1.6269 0.5012 3.246 0.00117 **
## x2 2.8565 0.9520 3.000 0.00270 **
## x3 0.6788 0.8460 0.802 0.42230
## x4 2.9697 1.1546 2.572 0.01011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 97.245 on 99 degrees of freedom
## Residual deviance: 41.267 on 95 degrees of freedom
## AIC: 51.267
##
## Number of Fisher Scoring iterations: 9