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 <- 1.1
b2 <- 1.2
b3 <- 0.1
b4 <- 0.1set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 1.1287 1.2404 1.2110 1.2993 0.1699 -0.0342 1.3056 1.2978 0.1480
## [10] 1.2295 0.0956 0.0378 1.2721 0.0216 0.0306 1.3174 -0.0251 0.0358
## [19] 0.0343 1.2688 1.3647 0.1010 1.3369 1.3375 1.2583 1.2839 1.3081
## [28] 0.1553 1.2031 1.2661 -0.0079 1.3187 0.0826 1.2846 0.0108 -0.0281
## [37] 0.0679 1.1558 1.2660 1.2002 1.2913 -0.0180 1.2545 0.0115 0.0260
## [46] 0.0530 1.2924 -0.0374 -0.0206 0.0806 -0.0330 -0.0484 1.2700 1.1452
## [55] 1.3005 1.1810 1.1664 0.0641 0.0988 -0.0782 0.0920 1.1711 1.2322
## [64] 1.2227 1.2612 1.2507 0.0885 1.1558 0.1504 1.1664 1.3229 1.3136
## [73] 1.2526 1.2727 1.2976 0.0581 -0.0240 0.1501 -0.0455 1.2692 0.0575
## [82] 0.0488 -0.0247 1.3181 0.0101 -0.0121 2.3455 1.2059 0.0425 -0.0437p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.7555989 0.7756336 0.7704758 0.7857172 0.5423731 0.4914508 0.7867759
## [8] 0.7854645 0.5369326 0.7737311 0.5238818 0.5094489 0.7811020 0.5053998
## [15] 0.5076494 0.7887488 0.4937253 0.5089490 0.5085742 0.7805373 0.7965225
## [22] 0.5252286 0.7919797 0.7920785 0.7787333 0.7831129 0.7871950 0.5387472
## [29] 0.7690758 0.7800744 0.4980250 0.7889653 0.5206383 0.7832318 0.5027000
## [36] 0.4929755 0.5169685 0.7605687 0.7800572 0.7685604 0.7843671 0.4955001
## [43] 0.7780779 0.5028750 0.5064996 0.5132469 0.7845531 0.4906511 0.4948502
## [50] 0.5201391 0.4917507 0.4879024 0.7807427 0.7586331 0.7859191 0.7651276
## [57] 0.7624937 0.5160195 0.5246799 0.4804600 0.5229838 0.7633438 0.7742034
## [64] 0.7725384 0.7792326 0.7774210 0.5221106 0.7605687 0.5375293 0.7624937
## [71] 0.7896638 0.7881149 0.7777496 0.7812046 0.7854308 0.5145209 0.4940003
## [78] 0.5374547 0.4886270 0.7806058 0.5143710 0.5121976 0.4938253 0.7888654
## [85] 0.5025250 0.4969750 0.9125759 0.7695727 0.5106234 0.4890767set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1
## [39] 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0
## [77] 0 0 0 1 1 0 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 0 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 0 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 1 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 0 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 0 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) -41.5642 40.4948 -1.026 0.30470
## x1 -16.3379 1455.3976 -0.011 0.99104
## x2 1.3358 0.4631 2.885 0.00392 **
## x3 0.0317 0.4691 0.068 0.94612
## x4 0.5156 0.5060 1.019 0.30821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 122.58 on 89 degrees of freedom
## Residual deviance: 110.21 on 85 degrees of freedom
## AIC: 120.21
##
## Number of Fisher Scoring iterations: 14