Y : Keputusan menerima/menolak tugas mahasiswa X1 : Lama pengerjaan tugas sebelumnya X2 : Status nilai saat ini (100: Mengerjakan, 1: Tidak mengerjakan) X3 : Tingkat nilai (0: Lulus rata-rata, 1: Lulusan diatas rata-rata) X4 : Nilai (skala 100)
X1 : Lama pengerjaan tugas sebelumnya (hari)
set.seed(123)
xcontoh <- rnorm(100,80,60)
xcontoh## [1] 46.371461 66.189351 173.522499 84.230503 87.757264 182.903899
## [7] 107.654972 4.096326 38.788829 53.260282 153.444908 101.588830
## [13] 104.046287 86.640963 46.649532 187.214788 109.871029 -37.997029
## [19] 122.081354 51.632516 15.930578 66.921505 18.439733 36.266526
## [25] 42.497644 -21.201599 130.267223 89.202387 11.711784 155.228895
## [31] 105.587853 62.295711 133.707540 132.688009 129.294865 121.318415
## [37] 113.235059 76.285297 61.642240 57.171740 38.317581 67.524963
## [43] 4.076219 210.137358 152.477720 12.613485 55.826910 52.000679
## [49] 126.797907 74.997856 95.199111 78.287195 77.427773 162.116137
## [55] 66.453741 170.988236 -12.925168 115.076825 87.431255 92.956494
## [61] 102.778369 49.860593 60.007557 18.885477 15.692526 98.211718
## [67] 106.892587 83.180254 135.336048 203.005081 50.538130 -58.550133
## [73] 140.344311 37.447954 38.719483 141.534282 62.913620 6.756937
## [79] 90.878209 71.666518 80.345851 103.116824 57.760398 118.662593
## [85] 66.770806 99.906918 145.810341 106.110889 60.444105 148.928457
## [91] 139.610231 112.903818 94.323904 42.325635 161.639147 43.984425
## [97] 211.239980 171.956638 65.857978 18.414746X1 : Lama pengalaman kerja sebelumnya (hari) Membangkitkan variabel X1 dengan lama pengerjaan tugas 0-7 hari dengan nilai tengah 70 dan banyak nilai mahasiswa adalah 90
set.seed(90)
n <- 90
u <- runif(n)
x1 <- round(7*(-(log(1-u)/70)))
x1## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [77] 0 0 0 0 0 0 0 0 0 0 1 0 0 0X2 : Status nilai Keterengan yang digunakan(100: Mengerjakan, 1: Tidak mengerjakan)
set.seed(56789)
x2 <- round(runif(n))
x2## [1] 1 1 1 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 1
## [39] 1 1 1 0 1 0 0 0 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0
## [77] 0 0 0 1 0 0 0 1 0 0 1 1 0 0X3 : Tingkat Nilai Keterengan yang digunakan (0: Lulus rata-rata, 1: Lulusan diatas rata-rata)
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 0
## [39] 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 0 0
## [77] 0 1 0 0 0 1 0 1 0 0 1 1 1 0X4 adalah data nilai dengan skala 100
set.seed(156)
x4 <- round(rnorm(n,80,0.5),3)
x4## [1] 79.287 79.404 80.110 79.993 80.699 79.658 80.056 79.978 80.480 80.295
## [11] 79.956 80.378 79.721 79.216 80.306 80.174 79.749 80.358 80.343 79.688
## [21] 80.647 80.010 80.369 80.375 79.583 79.839 80.081 80.553 80.031 80.661
## [31] 78.921 80.187 79.826 79.846 80.108 79.719 79.679 79.558 80.660 80.002
## [41] 80.913 79.820 80.545 80.115 80.260 80.530 80.924 79.626 79.794 79.806
## [51] 79.670 79.516 79.700 79.452 80.005 79.810 79.664 79.641 79.988 79.218
## [61] 79.920 79.711 80.322 80.227 79.612 80.507 79.885 78.558 80.504 79.664
## [71] 80.229 80.136 79.526 80.727 80.976 80.581 79.760 80.501 79.545 80.692
## [81] 80.575 79.488 79.753 80.181 80.101 79.879 79.455 79.059 79.425 79.563b0 <- -8
b1 <- 1.5
b2 <- 0.5
b3 <- 1.0
b4 <- 0.1set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 0.4287 1.4404 0.5110 1.4993 1.0699 -0.0342 1.5056 1.4978 1.0480
## [10] 0.5295 0.9956 0.0378 1.4721 0.9216 0.0306 1.5174 -0.0251 0.0358
## [19] 0.0343 1.4688 1.5647 1.0010 1.5369 1.5375 1.4583 1.4839 1.5081
## [28] 1.0553 0.5031 0.5661 0.8921 1.5187 0.9826 1.4846 0.0108 -0.0281
## [37] 0.9679 0.4558 0.5660 0.5002 0.5913 -0.0180 0.5545 0.0115 0.0260
## [46] 0.0530 0.5924 -0.0374 -0.0206 0.9806 -0.0330 -0.0484 1.4700 0.4452
## [55] 1.5005 0.4810 0.4664 0.9641 0.9988 -0.0782 0.9920 0.4711 0.5322
## [64] 0.5227 1.4612 0.5507 0.9885 1.3558 1.0504 0.4664 1.5229 1.5136
## [73] 1.4526 0.5727 0.5976 0.0581 -0.0240 1.0501 -0.0455 0.5692 0.0575
## [82] 0.9488 -0.0247 1.5181 0.0101 -0.0121 2.9455 1.4059 0.9425 -0.0437p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.6055632 0.8085166 0.6250409 0.8174701 0.7445779 0.4914508 0.8184082
## [8] 0.8172461 0.7403907 0.6293665 0.7301926 0.5094489 0.8133764 0.7153680
## [15] 0.5076494 0.8201553 0.4937253 0.5089490 0.5085742 0.8128749 0.8270267
## [22] 0.7312551 0.8230136 0.8231010 0.8112725 0.8151609 0.8187795 0.7417913
## [29] 0.6231876 0.6378628 0.7093233 0.8203470 0.7276238 0.8152664 0.5027000
## [36] 0.4929755 0.7247007 0.6120173 0.6378397 0.6225063 0.6436634 0.4955001
## [43] 0.6351790 0.5028750 0.5064996 0.5132469 0.6439156 0.4906511 0.4948502
## [50] 0.7272273 0.4917507 0.4879024 0.8130574 0.6094974 0.8176490 0.6179840
## [57] 0.6145313 0.7239419 0.7308226 0.4804600 0.7294828 0.6156441 0.6299961
## [64] 0.6277789 0.8117161 0.6342980 0.7287915 0.7950762 0.7408517 0.6145313
## [71] 0.8209651 0.8195941 0.8103983 0.6393860 0.6451070 0.5145209 0.4940003
## [78] 0.7407941 0.4886270 0.6385786 0.5143710 0.7208738 0.4938253 0.8202585
## [85] 0.5025250 0.4969750 0.9500504 0.8031185 0.7196044 0.4890767set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 0 1
## [39] 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0
## [77] 0 0 0 1 1 1 0 1 1 1 0 1 0 0datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 0 1 0 79.287
## 2 1 0 1 1 79.404
## 3 1 0 1 0 80.110
## 4 1 0 1 1 79.993
## 5 0 0 0 1 80.699
## 6 1 0 0 0 79.658
## 7 1 0 1 1 80.056
## 8 0 0 1 1 79.978
## 9 1 0 0 1 80.480
## 10 1 0 1 0 80.295
## 11 1 0 0 1 79.956
## 12 1 0 0 0 80.378
## 13 1 0 1 1 79.721
## 14 1 0 0 1 79.216
## 15 1 0 0 0 80.306
## 16 0 0 1 1 80.174
## 17 1 0 0 0 79.749
## 18 1 0 0 0 80.358
## 19 1 0 0 0 80.343
## 20 1 0 1 1 79.688
## 21 1 0 1 1 80.647
## 22 1 0 0 1 80.010
## 23 0 0 1 1 80.369
## 24 1 0 1 1 80.375
## 25 1 0 1 1 79.583
## 26 1 0 1 1 79.839
## 27 1 0 1 1 80.081
## 28 1 0 0 1 80.553
## 29 0 0 1 0 80.031
## 30 1 0 1 0 80.661
## 31 1 0 0 1 78.921
## 32 1 0 1 1 80.187
## 33 0 0 0 1 79.826
## 34 0 0 1 1 79.846
## 35 0 0 0 0 80.108
## 36 1 0 0 0 79.719
## 37 0 0 0 1 79.679
## 38 1 0 1 0 79.558
## 39 0 0 1 0 80.660
## 40 1 0 1 0 80.002
## 41 0 0 1 0 80.913
## 42 0 0 0 0 79.820
## 43 1 0 1 0 80.545
## 44 1 0 0 0 80.115
## 45 0 0 0 0 80.260
## 46 0 0 0 0 80.530
## 47 0 0 1 0 80.924
## 48 0 0 0 0 79.626
## 49 0 0 0 0 79.794
## 50 0 0 0 1 79.806
## 51 0 0 0 0 79.670
## 52 0 0 0 0 79.516
## 53 1 0 1 1 79.700
## 54 0 0 1 0 79.452
## 55 1 0 1 1 80.005
## 56 0 0 1 0 79.810
## 57 0 0 1 0 79.664
## 58 0 0 0 1 79.641
## 59 1 0 0 1 79.988
## 60 1 0 0 0 79.218
## 61 0 0 0 1 79.920
## 62 0 0 1 0 79.711
## 63 1 0 1 0 80.322
## 64 1 0 1 0 80.227
## 65 0 0 1 1 79.612
## 66 1 0 1 0 80.507
## 67 1 0 0 1 79.885
## 68 1 0 1 1 78.558
## 69 1 0 0 1 80.504
## 70 1 0 1 0 79.664
## 71 1 0 1 1 80.229
## 72 1 0 1 1 80.136
## 73 1 0 1 1 79.526
## 74 1 0 1 0 80.727
## 75 1 0 1 0 80.976
## 76 0 0 0 0 80.581
## 77 0 0 0 0 79.760
## 78 0 0 0 1 80.501
## 79 0 0 0 0 79.545
## 80 1 0 1 0 80.692
## 81 1 0 0 0 80.575
## 82 1 0 0 1 79.488
## 83 0 0 0 0 79.753
## 84 1 0 1 1 80.181
## 85 1 0 0 0 80.101
## 86 1 0 0 0 79.879
## 87 0 1 1 1 79.455
## 88 1 0 1 1 79.059
## 89 0 0 0 1 79.425
## 90 0 0 0 0 79.563modelreglog <- glm(y ~ x1 + x2 + x3 + x4, family = binomial(link="logit"),data = datagab)
summary(modelreglog)##
## Call:
## glm(formula = y ~ x1 + x2 + x3 + x4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -10.4270 39.6287 -0.263 0.7925
## x1 -16.6994 1455.3976 -0.011 0.9908
## x2 0.8293 0.4553 1.821 0.0686 .
## x3 0.5474 0.4637 1.181 0.2378
## x4 0.1282 0.4952 0.259 0.7958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 120.28 on 89 degrees of freedom
## Residual deviance: 112.95 on 85 degrees of freedom
## AIC: 122.95
##
## Number of Fisher Scoring iterations: 14