Skenario ini mengambil sampel 1 sekolah (SMA) kelas 12 yang mengikuti utbk tahun 2024 Y :Keputusan menolak/menerima mahasiswa baru pada universitas UNNES X1 :Lama pengalaman belajar (bulan) X2 :Status siswa saat ini (0: IPA, 1: IPS) X3 :Pilihan kelompok studi (0: saintek, 1: soshum) X4 :Skor UTBK (skala 1000)
X1 :Lama pengalaman belajar (bulan) Membangkitkan variabel X1 dengan lama belajar 0-12 bulan dengan nilai tengah 6 dan banyak siswa pendaftar UTBK adalah 120
set.seed(11)
n <- 120
u <- runif(n)
X1 <- round(12*(-(log(1-u)/6)))
X1## [1] 1 0 1 0 0 6 0 1 4 0 0 1 5 4 3 2 1 1 0 1 0 2 1 1 0 1 1 0 0 1 1 1 1 0 3 2 1
## [38] 0 1 0 1 0 1 2 1 4 2 0 2 4 1 2 1 1 1 1 1 3 0 2 0 3 0 2 3 0 0 1 0 2 2 1 1 0
## [75] 6 2 1 1 1 0 0 1 1 1 3 1 0 2 3 1 1 3 0 1 3 1 3 0 2 1 3 2 1 1 0 1 2 3 2 0 2
## [112] 0 1 3 2 1 5 1 1 1X2 :Status siswa saat ini Keterangan yang diperlukan (0= IPA) dan (1= IPS)
set.seed(1)
X2 <- round(runif(n))
X2## [1] 0 0 1 1 0 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 1
## [38] 0 1 0 1 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0
## [75] 0 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 1 1
## [112] 1 0 0 0 0 1 0 0 1X3 :Pilihan kelompok studi Keterangan yang digunakan (0: saintek) dan (1: soshum)
set.seed(10)
X3 <- round(runif(n))
X3## [1] 1 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1
## [38] 1 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 1 1
## [75] 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 0 1 0 1
## [112] 1 0 1 1 1 0 1 1 1X4 adalah data skor UTBK (skala 1000)
set.seed(12)
X4 <- sample(1:1000,120,replace = TRUE)
X4## [1] 450 346 336 247 859 174 453 69 476 546 136 91 82 381 978 121 294 620
## [19] 618 218 999 432 269 440 964 152 313 72 127 890 427 174 509 103 542 236
## [37] 421 633 34 679 974 583 666 32 84 404 960 787 596 260 141 973 624 347
## [55] 165 727 764 48 751 839 795 589 852 219 104 133 723 914 85 764 951 570
## [73] 216 13 416 936 88 201 692 184 949 551 796 552 187 253 527 104 903 526
## [91] 464 842 706 140 810 936 933 848 133 75 278 579 628 510 396 901 204 579
## [109] 133 342 42 528 699 678 914 194 104 595 778 306b0 <- -12
b1 <- 3.5
b2 <-5.7
b3 <-2.2
b4 <- 1.4set.seed(11)
datapendukung <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datapendukung## [1] 623.7 472.4 467.6 341.7 1190.6 258.3 627.9 93.8 676.3 752.4
## [11] 180.6 121.1 126.0 537.6 1373.4 164.4 408.8 865.2 853.2 304.6
## [21] 1394.5 602.0 376.0 607.5 1337.6 206.5 431.9 88.8 173.7 1237.5
## [31] 591.5 240.8 704.1 134.4 763.0 333.3 588.8 876.4 47.0 940.8
## [41] 1360.8 809.9 929.6 47.7 114.8 573.3 1339.0 1089.8 835.1 373.9
## [51] 188.9 1365.1 865.1 477.3 222.5 1011.5 1061.1 71.4 1045.1 1171.8
## [61] 1106.7 823.1 1180.8 301.6 152.0 176.4 1002.4 1276.8 107.0 1070.3
## [71] 1326.4 797.4 296.1 8.4 591.4 1313.3 120.4 275.1 966.0 251.3
## [81] 1318.8 768.6 1105.9 766.5 266.0 345.7 733.7 140.6 1262.7 727.9
## [91] 641.1 1179.5 984.3 195.4 1138.2 1309.8 1304.7 1177.4 189.1 102.2
## [101] 393.4 805.6 870.7 711.2 550.3 1255.1 282.8 809.1 189.1 472.5
## [111] 61.7 735.1 970.1 949.9 1276.8 265.3 156.8 826.7 1082.9 427.8p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 1.0000000 1.0000000 1.0000000 1.0000000 NaN 1.0000000 1.0000000
## [8] 1.0000000 1.0000000 NaN 1.0000000 1.0000000 1.0000000 1.0000000
## [15] NaN 1.0000000 1.0000000 NaN NaN 1.0000000 NaN
## [22] 1.0000000 1.0000000 1.0000000 NaN 1.0000000 1.0000000 1.0000000
## [29] 1.0000000 NaN 1.0000000 1.0000000 1.0000000 1.0000000 NaN
## [36] 1.0000000 1.0000000 NaN 1.0000000 NaN NaN NaN
## [43] NaN 1.0000000 1.0000000 1.0000000 NaN NaN NaN
## [50] 1.0000000 1.0000000 NaN NaN 1.0000000 1.0000000 NaN
## [57] NaN 1.0000000 NaN NaN NaN NaN NaN
## [64] 1.0000000 1.0000000 1.0000000 NaN NaN 1.0000000 NaN
## [71] NaN NaN 1.0000000 0.9997752 1.0000000 NaN 1.0000000
## [78] 1.0000000 NaN 1.0000000 NaN NaN NaN NaN
## [85] 1.0000000 1.0000000 NaN 1.0000000 NaN NaN 1.0000000
## [92] NaN NaN 1.0000000 NaN NaN NaN NaN
## [99] 1.0000000 1.0000000 1.0000000 NaN NaN NaN 1.0000000
## [106] NaN 1.0000000 NaN 1.0000000 1.0000000 1.0000000 NaN
## [113] NaN NaN NaN 1.0000000 1.0000000 NaN NaN
## [120] 1.0000000set.seed(2)
Y <- rbinom(n,1,p)## Warning in rbinom(n, 1, p): NAs producedY## [1] 1 1 1 1 NA 1 1 1 1 NA 1 1 1 1 NA 1 1 NA NA 1 NA 1 1 1 NA
## [26] 1 1 1 1 NA 1 1 1 1 NA 1 1 NA 1 NA NA NA NA 1 1 1 NA NA NA 1
## [51] 1 NA NA 1 1 NA NA 1 NA NA NA NA NA 1 1 1 NA NA 1 NA NA NA 1 1 1
## [76] NA 1 1 NA 1 NA NA NA NA 1 1 NA 1 NA NA 1 NA NA 1 NA NA NA NA 1 1
## [101] 1 NA NA NA 1 NA 1 NA 1 1 1 NA NA NA NA 1 1 NA NA 1datagab <- data.frame(Y,X1,X2,X3,X4)
datagab## Y X1 X2 X3 X4
## 1 1 1 0 1 450
## 2 1 0 0 0 346
## 3 1 1 1 0 336
## 4 1 0 1 1 247
## 5 NA 0 0 0 859
## 6 1 6 1 0 174
## 7 1 0 1 0 453
## 8 1 1 1 0 69
## 9 1 4 1 1 476
## 10 NA 0 0 0 546
## 11 1 0 0 1 136
## 12 1 1 0 1 91
## 13 1 5 1 0 82
## 14 1 4 0 1 381
## 15 NA 3 1 0 978
## 16 1 2 0 0 121
## 17 1 1 1 0 294
## 18 NA 1 1 0 620
## 19 NA 0 0 0 618
## 20 1 1 1 1 218
## 21 NA 0 1 1 999
## 22 1 2 0 1 432
## 23 1 1 1 1 269
## 24 1 1 0 0 440
## 25 NA 0 0 0 964
## 26 1 1 0 1 152
## 27 1 1 0 1 313
## 28 1 0 0 0 72
## 29 1 0 1 1 127
## 30 NA 1 0 0 890
## 31 1 1 0 1 427
## 32 1 1 1 0 174
## 33 1 1 0 0 509
## 34 1 0 0 1 103
## 35 NA 3 1 0 542
## 36 1 2 1 1 236
## 37 1 1 1 1 421
## 38 NA 0 0 1 633
## 39 1 1 1 1 34
## 40 NA 0 0 1 679
## 41 NA 1 1 0 974
## 42 NA 0 1 0 583
## 43 NA 1 1 0 666
## 44 1 2 1 1 32
## 45 1 1 1 0 84
## 46 1 4 1 0 404
## 47 NA 2 0 0 960
## 48 NA 0 0 0 787
## 49 NA 2 1 0 596
## 50 1 4 1 1 260
## 51 1 1 0 0 141
## 52 NA 2 1 1 973
## 53 NA 1 0 0 624
## 54 1 1 0 0 347
## 55 1 1 0 0 165
## 56 NA 1 0 1 727
## 57 NA 1 0 0 764
## 58 1 3 1 0 48
## 59 NA 0 1 0 751
## 60 NA 2 0 1 839
## 61 NA 0 1 0 795
## 62 NA 3 0 0 589
## 63 NA 0 0 0 852
## 64 1 2 0 0 219
## 65 1 3 1 1 104
## 66 1 0 0 1 133
## 67 NA 0 0 1 723
## 68 NA 1 1 0 914
## 69 1 0 0 0 85
## 70 NA 2 1 0 764
## 71 NA 2 0 0 951
## 72 NA 1 1 1 570
## 73 1 1 0 1 216
## 74 1 0 0 1 13
## 75 1 6 0 0 416
## 76 NA 2 1 1 936
## 77 1 1 1 0 88
## 78 1 1 0 1 201
## 79 NA 1 1 0 692
## 80 1 0 1 0 184
## 81 NA 0 0 1 949
## 82 NA 1 1 0 551
## 83 NA 1 0 0 796
## 84 NA 1 0 1 552
## 85 1 3 1 0 187
## 86 1 1 0 0 253
## 87 NA 0 1 1 527
## 88 1 2 0 0 104
## 89 NA 3 0 0 903
## 90 NA 1 0 0 526
## 91 1 1 0 0 464
## 92 NA 3 0 1 842
## 93 NA 0 1 1 706
## 94 1 1 1 1 140
## 95 NA 3 1 0 810
## 96 NA 1 1 1 936
## 97 NA 3 0 0 933
## 98 NA 0 0 1 848
## 99 1 2 1 1 133
## 100 1 1 1 0 75
## 101 1 3 1 0 278
## 102 NA 2 0 0 579
## 103 NA 1 0 0 628
## 104 NA 1 1 0 510
## 105 1 0 1 1 396
## 106 NA 1 0 1 901
## 107 1 2 0 1 204
## 108 NA 3 0 0 579
## 109 1 2 1 1 133
## 110 1 0 1 0 342
## 111 1 2 1 1 42
## 112 NA 0 1 1 528
## 113 NA 1 0 0 699
## 114 NA 3 0 1 678
## 115 NA 2 0 1 914
## 116 1 1 0 1 194
## 117 1 5 1 0 104
## 118 NA 1 0 1 595
## 119 NA 1 0 1 778
## 120 1 1 1 1 306modelreglog <- 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) 2.657e+01 1.221e+05 0 1
## X1 -1.063e-06 3.156e+04 0 1
## X2 -1.513e-07 9.356e+04 0 1
## X3 3.268e-07 9.074e+04 0 1
## X4 -8.843e-10 3.342e+02 0 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 0.000e+00 on 62 degrees of freedom
## Residual deviance: 3.655e-10 on 58 degrees of freedom
## (57 observations deleted due to missingness)
## AIC: 10
##
## Number of Fisher Scoring iterations: 25KESIMPULAN Simulasi ini menunjukkan bagaimana data UTBK 2024 dapat dibangkitkan dan dianalisis menggunakan regresi logistik. Hasil analisis regresi logistik menunjukkan pengaruh variabel X1, X2, X3, dan X4 terhadap peluang diterima di UNNES.