Penerimaan calon mahasiswa baru SSD jalur seleksi mandiri tes, dengan
jumlah pendaftar 400 orang.
Y : Keputusan menerima/menolak calon
mahasiswa baru
X1 : Status kelulusan (0: lulusan baru, 1: gap 1
tahun, 2: gap 2 tahun)
X2 : Pendidikan terakhir (0: SMK, 1: SMA, 2:
MA, 3: lainnya/sederajat)
X3 : Rata-rata nilai rapor pendidikan
sebelumnya (0: di atas 70, 1: di bawah 70)
X4 : Nilai seleksi
mandiri (skala 100)
X1 : Status kelulusan (0= lulusan baru, 1= gap 1 tahun, 2= gap 2
tahun)
Membangkitkan peubah X1 yang merupakan bilangan acak uniform
0, 1, atau 2.
Status kelulusan pendaftar merupakan lulusan
baru/lulus di tahun tersebut dan maksimal lulusan 2 tahun terakhir.
set.seed(4)
n <- 400
min_u <- 0
max_u <- 2
u <- runif(n, min = min_u, max = max_u)
x1 <- round(u)
x1## [1] 1 0 1 1 2 1 1 2 2 0 2 1 0 2 1 1 2 1 2 2 1 2 1 1 1 2 1 2 1 1 1 0 2 1 1 2 1
## [38] 1 1 0 2 0 1 0 2 0 2 2 1 1 1 1 2 2 2 1 0 0 2 1 1 0 2 2 0 1 0 1 2 2 1 1 1 1
## [75] 2 1 1 1 1 0 2 1 1 2 2 0 0 2 0 2 0 0 1 1 1 0 2 1 0 1 1 1 1 1 1 1 1 0 2 2 1
## [112] 1 1 1 2 1 1 2 2 0 0 1 2 1 0 1 2 0 1 1 1 0 1 0 2 2 1 1 1 0 1 1 1 2 0 1 1 2
## [149] 2 1 1 0 1 2 1 2 0 1 1 2 2 2 1 1 1 0 0 0 0 2 0 1 0 1 0 0 1 0 1 1 0 1 0 2 1
## [186] 0 1 1 2 2 1 2 2 0 1 1 2 1 1 0 2 2 1 1 1 0 1 1 1 0 0 2 2 1 2 1 0 0 0 1 1 1
## [223] 2 1 2 1 1 1 1 2 1 2 0 0 1 1 2 1 0 1 1 1 0 1 2 0 1 1 2 1 2 0 0 1 1 1 1 2 0
## [260] 2 2 0 1 0 0 0 0 1 1 2 2 2 1 1 1 2 0 2 2 1 2 1 2 0 0 0 1 0 0 1 1 0 0 1 1 2
## [297] 1 1 2 0 2 1 0 0 2 2 2 2 1 2 1 1 0 2 0 1 0 1 2 1 1 0 1 1 0 2 0 0 2 1 0 1 1
## [334] 1 0 0 1 2 1 0 2 0 1 0 1 0 0 1 2 0 1 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 0 2 1
## [371] 0 0 1 1 1 1 1 1 1 0 0 2 1 2 1 1 0 1 1 0 1 0 0 2 2 1 0 1 2 1X2 : Pendidikan terakhir (0= SMK, 1=SMA, 2=MA, 3=
lainnya/sederajat)
Pendidikan terakhir pendaftar adalah riwayat
pendidikan sebelumnya, pendaftar merupakan lulusan dari
SMK/SMA/MA/sederajat.
set.seed(44)
min_x<- 0
max_x<- 3
x <- runif(n, min = min_x, max = max_x)
x2 <- round(x)
x2## [1] 2 1 2 2 0 0 1 0 0 1 0 0 2 0 1 1 0 1 1 1 2 1 1 1 1 1 1 2 1 2 2 2 3 0 0 2 3
## [38] 1 3 1 2 1 2 1 1 1 1 1 1 1 0 0 1 2 3 2 3 1 2 0 1 1 3 1 3 3 0 2 2 3 2 0 2 1
## [75] 2 2 1 0 1 3 1 2 1 1 1 1 3 2 3 3 1 1 1 2 1 1 3 2 1 1 0 2 2 3 0 2 0 2 3 0 3
## [112] 1 2 1 0 2 1 1 0 2 3 0 1 1 0 1 2 3 3 1 1 1 1 0 2 1 1 1 3 2 2 3 0 3 0 0 2 2
## [149] 2 3 1 1 1 1 1 2 0 2 3 1 2 1 2 3 0 3 0 2 2 1 2 1 1 1 1 2 2 2 2 1 3 0 3 1 1
## [186] 3 2 1 1 3 1 2 0 2 1 1 2 3 1 3 1 0 3 0 0 0 2 2 1 2 3 1 1 3 1 2 1 3 3 2 1 1
## [223] 1 1 0 2 1 0 2 1 1 2 1 2 3 2 1 1 3 2 1 2 2 2 2 1 2 1 1 1 3 3 1 1 3 2 1 2 3
## [260] 2 2 1 0 1 3 1 0 1 2 1 0 1 1 3 0 2 3 1 1 2 3 1 2 2 1 1 1 1 0 1 2 1 3 0 2 3
## [297] 1 3 3 0 1 2 2 0 2 2 1 0 1 1 1 3 2 2 0 1 3 2 2 2 1 2 1 1 0 0 1 0 2 1 1 2 2
## [334] 1 1 1 1 2 1 3 1 2 2 3 0 2 2 0 2 1 1 1 1 1 1 2 3 1 0 1 0 2 2 2 0 2 1 1 1 1
## [371] 1 0 0 0 3 0 3 0 3 1 2 1 0 0 2 0 1 1 3 3 2 2 2 2 1 1 0 3 1 1X3 : Rata-rata nilai rapor pendidikan sebelumnya (0=di atas 70, 1=di
bawah 70)
Rata-rata nilai rapor semester 1-5 pendaftar pada
pendidikan sebelumnya adalah di atas 70 atau di bawah 70 pada skala
100.
set.seed(444)
x3 <- round(runif(n))
x3## [1] 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 1 0 1 1 1 1 0 1 0 0 0
## [38] 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0
## [75] 0 0 1 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 1 1 0 1 1
## [112] 0 0 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0
## [149] 1 0 0 1 0 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 1 1 1 0 1 1 0
## [186] 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 0 1 1 1 1 0
## [223] 1 1 0 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0
## [260] 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1
## [297] 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 1
## [334] 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 1 0 1 0 0 0 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 0
## [371] 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 1X4 : Nilai seleksi mandiri (skala 100)
Nilai seleksi mandiri
pendaftar merupakan nilai hasil ujian seleksi mandiri dengan skala
100.
set.seed(4444)
x4 <- round(rnorm(n,85,5))
x4## [1] 96 75 77 88 90 88 87 87 85 91 89 79 77 85 91 82 83 93
## [19] 81 81 78 83 100 89 86 89 82 89 85 85 86 88 88 83 77 84
## [37] 94 78 85 84 89 71 85 82 85 85 86 85 80 81 83 91 83 88
## [55] 83 85 90 95 85 82 89 89 85 81 92 83 73 87 78 86 81 88
## [73] 88 91 92 88 94 101 87 83 85 84 80 92 81 87 82 84 86 84
## [91] 90 83 85 95 78 90 80 87 85 86 91 91 86 83 89 90 89 80
## [109] 95 88 82 84 88 82 88 85 88 77 84 90 87 91 92 82 90 91
## [127] 94 87 84 79 78 86 84 87 75 80 97 85 81 93 72 79 86 91
## [145] 86 90 87 84 85 84 83 87 89 80 81 92 80 79 82 90 94 81
## [163] 87 81 87 88 91 85 88 88 87 86 88 82 75 80 83 86 85 91
## [181] 87 85 85 84 82 93 93 78 75 90 90 79 87 82 90 77 81 90
## [199] 86 87 87 87 85 86 88 87 83 89 89 96 95 87 84 86 82 76
## [217] 81 78 90 86 88 93 79 81 97 83 79 78 80 84 83 86 87 86
## [235] 85 87 94 78 83 86 86 85 74 89 80 85 88 73 77 81 92 86
## [253] 82 86 93 83 80 88 83 88 87 84 81 81 83 78 83 84 103 85
## [271] 97 87 86 80 89 86 85 90 85 85 79 84 81 84 85 85 84 89
## [289] 82 87 89 82 85 79 89 83 89 79 85 86 79 83 91 86 88 85
## [307] 88 95 80 77 90 78 81 86 85 81 79 82 88 81 83 87 88 84
## [325] 82 89 75 85 85 89 84 90 85 81 90 79 89 80 83 84 86 85
## [343] 87 90 85 89 84 86 78 73 76 83 82 78 95 78 95 82 85 78
## [361] 83 90 88 86 83 91 86 88 85 81 86 81 82 94 92 72 81 84
## [379] 88 87 86 95 94 81 83 82 76 89 93 83 80 82 99 84 84 85
## [397] 89 83 86 85b0 <- -10
b1 <- -2
b2 <- 1.5
b3 <- 1
b4 <- 0.1set.seed(44444)
datapenerimaan <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapenerimaan## [1] 0.6 -1.0 -1.3 -0.2 -5.0 -2.2 -0.8 -5.3 -4.5 0.6 -5.1 -4.1 0.7 -5.5 -1.4
## [16] -1.3 -4.7 -0.2 -3.4 -4.4 -1.2 -3.2 -0.5 -1.6 -1.9 -3.6 -1.3 -2.1 -1.0 0.5
## [31] 0.6 2.8 -0.7 -2.7 -4.3 -2.6 1.9 -2.7 2.0 -0.1 -2.1 -1.4 -0.5 -0.3 -3.0
## [46] 1.0 -2.9 -4.0 -2.5 -2.4 -3.7 -1.9 -3.2 -2.2 -0.2 0.5 4.5 1.0 -1.5 -3.8
## [61] -1.6 0.4 -1.0 -3.4 3.7 0.8 -2.7 0.7 -3.2 0.1 0.1 -3.2 -0.2 -1.4 -1.8
## [76] -0.2 -0.1 -0.9 -1.8 3.8 -4.0 -0.6 -2.5 -2.3 -4.4 0.2 2.7 -1.6 4.1 -1.1
## [91] 1.5 -0.2 -2.0 1.5 -2.7 1.5 -0.5 -0.3 1.0 -0.9 -2.9 0.1 -0.4 0.8 -3.1
## [106] 0.0 -2.1 2.0 0.0 -4.2 1.7 -2.1 -0.2 -2.3 -4.2 -0.5 -0.7 -4.8 -4.6 3.0
## [121] 4.2 -1.9 -2.3 -1.3 -1.0 -0.4 -1.6 3.2 0.9 -2.6 -1.7 1.1 -2.1 -0.3 -3.5
## [136] -3.5 -0.8 -1.0 0.6 3.3 -0.8 0.4 -3.4 0.6 -0.4 -3.0 -0.3 -2.6 -1.5 0.9
## [151] -2.2 1.2 -1.6 -3.5 -2.4 -0.8 -1.0 -0.1 1.7 -3.5 -1.6 -3.4 -0.3 1.6 -2.3
## [166] 4.3 0.1 2.5 2.8 -2.7 1.7 -1.9 1.3 -2.3 -1.0 1.0 -0.7 1.6 0.5 -0.4
## [181] 4.2 -3.5 4.0 -3.1 -2.3 3.8 1.3 -2.7 -5.0 0.5 -1.5 -3.1 -5.3 2.2 -1.5
## [196] -2.8 -2.9 2.5 -1.9 3.2 -3.8 -5.3 2.0 -2.4 -3.2 -1.3 -0.7 0.9 -1.6 3.6
## [211] 4.0 -3.8 -4.1 1.1 -3.3 -0.4 -0.4 3.3 4.5 0.6 -0.7 -1.2 -3.6 -1.4 -4.3
## [226] 0.3 -2.6 -3.2 -1.0 -4.1 -2.2 -1.4 0.2 2.6 2.0 0.7 -3.1 -1.7 3.8 -0.4
## [241] -0.9 0.5 0.4 -0.1 -3.0 1.0 0.8 -3.2 -4.8 -2.4 -0.3 4.1 0.7 -1.9 2.8
## [256] -0.7 -2.5 -1.2 2.8 -2.2 -2.3 -0.1 -3.9 0.6 2.8 -0.7 -0.7 -2.1 1.3 -3.0
## [271] -3.3 -2.8 -0.9 0.5 -2.1 -1.4 3.0 -3.5 -4.0 -0.5 -1.6 -1.1 -1.9 1.4 1.0
## [286] 0.0 -1.1 0.4 -0.8 -0.8 0.9 0.7 4.0 -4.1 0.9 -0.2 -1.6 0.4 0.0 -0.4
## [301] -4.6 -0.7 3.1 -1.4 -2.2 -2.5 -2.7 -4.5 -2.5 -4.8 -1.5 1.3 2.1 -1.4 -0.5
## [316] -2.4 2.4 -0.8 -2.2 0.1 -2.2 2.7 -1.7 -1.1 -1.8 -5.1 -1.0 -0.5 -2.5 -1.6
## [331] -0.1 0.0 0.5 -2.4 0.5 0.4 -1.6 -2.0 -2.2 2.9 -2.9 1.5 -0.3 4.5 -3.5
## [346] 2.9 2.4 -2.4 -2.2 -1.2 -1.9 -0.2 -2.3 -2.7 -1.0 -0.2 4.0 -2.3 -0.5 -1.7
## [361] -0.7 1.0 0.8 0.6 -3.7 0.1 1.1 0.3 -4.0 -2.4 0.1 -0.9 -3.8 -2.6 2.7
## [376] -4.8 0.6 -2.6 1.3 1.2 1.6 -3.0 -2.6 -5.9 0.3 -3.8 0.1 -0.6 1.8 3.8
## [391] 0.0 1.2 3.9 -1.6 -3.1 -2.0 -0.1 0.8 -3.9 -1.0p <- exp(datapenerimaan)/(1+exp(datapenerimaan))
p## [1] 0.645656306 0.268941421 0.214165017 0.450166003 0.006692851 0.099750489
## [7] 0.310025519 0.004966802 0.010986943 0.645656306 0.006059801 0.016302499
## [13] 0.668187772 0.004070138 0.197816111 0.214165017 0.009013299 0.450166003
## [19] 0.032295465 0.012128435 0.231475217 0.039165723 0.377540669 0.167981615
## [25] 0.130108474 0.026596994 0.214165017 0.109096821 0.268941421 0.622459331
## [31] 0.645656306 0.942675824 0.331812228 0.062973356 0.013386918 0.069138420
## [37] 0.869891526 0.062973356 0.880797078 0.475020813 0.109096821 0.197816111
## [43] 0.377540669 0.425557483 0.047425873 0.731058579 0.052153563 0.017986210
## [49] 0.075858180 0.083172696 0.024127021 0.130108474 0.039165723 0.099750489
## [55] 0.450166003 0.622459331 0.989013057 0.731058579 0.182425524 0.021881271
## [61] 0.167981615 0.598687660 0.268941421 0.032295465 0.975872979 0.689974481
## [67] 0.062973356 0.668187772 0.039165723 0.524979187 0.524979187 0.039165723
## [73] 0.450166003 0.197816111 0.141851065 0.450166003 0.475020813 0.289050497
## [79] 0.141851065 0.978118729 0.017986210 0.354343694 0.075858180 0.091122961
## [85] 0.012128435 0.549833997 0.937026644 0.167981615 0.983697501 0.249739894
## [91] 0.817574476 0.450166003 0.119202922 0.817574476 0.062973356 0.817574476
## [97] 0.377540669 0.425557483 0.731058579 0.289050497 0.052153563 0.524979187
## [103] 0.401312340 0.689974481 0.043107255 0.500000000 0.109096821 0.880797078
## [109] 0.500000000 0.014774032 0.845534735 0.109096821 0.450166003 0.091122961
## [115] 0.014774032 0.377540669 0.331812228 0.008162571 0.009951802 0.952574127
## [121] 0.985225968 0.130108474 0.091122961 0.214165017 0.268941421 0.401312340
## [127] 0.167981615 0.960834277 0.710949503 0.069138420 0.154465265 0.750260106
## [133] 0.109096821 0.425557483 0.029312231 0.029312231 0.310025519 0.268941421
## [139] 0.645656306 0.964428811 0.310025519 0.598687660 0.032295465 0.645656306
## [145] 0.401312340 0.047425873 0.425557483 0.069138420 0.182425524 0.710949503
## [151] 0.099750489 0.768524783 0.167981615 0.029312231 0.083172696 0.310025519
## [157] 0.268941421 0.475020813 0.845534735 0.029312231 0.167981615 0.032295465
## [163] 0.425557483 0.832018385 0.091122961 0.986613082 0.524979187 0.924141820
## [169] 0.942675824 0.062973356 0.845534735 0.130108474 0.785834983 0.091122961
## [175] 0.268941421 0.731058579 0.331812228 0.832018385 0.622459331 0.401312340
## [181] 0.985225968 0.029312231 0.982013790 0.043107255 0.091122961 0.978118729
## [187] 0.785834983 0.062973356 0.006692851 0.622459331 0.182425524 0.043107255
## [193] 0.004966802 0.900249511 0.182425524 0.057324176 0.052153563 0.924141820
## [199] 0.130108474 0.960834277 0.021881271 0.004966802 0.880797078 0.083172696
## [205] 0.039165723 0.214165017 0.331812228 0.710949503 0.167981615 0.973403006
## [211] 0.982013790 0.021881271 0.016302499 0.750260106 0.035571189 0.401312340
## [217] 0.401312340 0.964428811 0.989013057 0.645656306 0.331812228 0.231475217
## [223] 0.026596994 0.197816111 0.013386918 0.574442517 0.069138420 0.039165723
## [229] 0.268941421 0.016302499 0.099750489 0.197816111 0.549833997 0.930861580
## [235] 0.880797078 0.668187772 0.043107255 0.154465265 0.978118729 0.401312340
## [241] 0.289050497 0.622459331 0.598687660 0.475020813 0.047425873 0.731058579
## [247] 0.689974481 0.039165723 0.008162571 0.083172696 0.425557483 0.983697501
## [253] 0.668187772 0.130108474 0.942675824 0.331812228 0.075858180 0.231475217
## [259] 0.942675824 0.099750489 0.091122961 0.475020813 0.019840306 0.645656306
## [265] 0.942675824 0.331812228 0.331812228 0.109096821 0.785834983 0.047425873
## [271] 0.035571189 0.057324176 0.289050497 0.622459331 0.109096821 0.197816111
## [277] 0.952574127 0.029312231 0.017986210 0.377540669 0.167981615 0.249739894
## [283] 0.130108474 0.802183889 0.731058579 0.500000000 0.249739894 0.598687660
## [289] 0.310025519 0.310025519 0.710949503 0.668187772 0.982013790 0.016302499
## [295] 0.710949503 0.450166003 0.167981615 0.598687660 0.500000000 0.401312340
## [301] 0.009951802 0.331812228 0.956892745 0.197816111 0.099750489 0.075858180
## [307] 0.062973356 0.010986943 0.075858180 0.008162571 0.182425524 0.785834983
## [313] 0.890903179 0.197816111 0.377540669 0.083172696 0.916827304 0.310025519
## [319] 0.099750489 0.524979187 0.099750489 0.937026644 0.154465265 0.249739894
## [325] 0.141851065 0.006059801 0.268941421 0.377540669 0.075858180 0.167981615
## [331] 0.475020813 0.500000000 0.622459331 0.083172696 0.622459331 0.598687660
## [337] 0.167981615 0.119202922 0.099750489 0.947846437 0.052153563 0.817574476
## [343] 0.425557483 0.989013057 0.029312231 0.947846437 0.916827304 0.083172696
## [349] 0.099750489 0.231475217 0.130108474 0.450166003 0.091122961 0.062973356
## [355] 0.268941421 0.450166003 0.982013790 0.091122961 0.377540669 0.154465265
## [361] 0.331812228 0.731058579 0.689974481 0.645656306 0.024127021 0.524979187
## [367] 0.750260106 0.574442517 0.017986210 0.083172696 0.524979187 0.289050497
## [373] 0.021881271 0.069138420 0.937026644 0.008162571 0.645656306 0.069138420
## [379] 0.785834983 0.768524783 0.832018385 0.047425873 0.069138420 0.002731961
## [385] 0.574442517 0.021881271 0.524979187 0.354343694 0.858148935 0.978118729
## [391] 0.500000000 0.768524783 0.980159694 0.167981615 0.043107255 0.119202922
## [397] 0.475020813 0.689974481 0.019840306 0.268941421set.seed(123456)
y <- rbinom(n,1,p)
y## [1] 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0
## [38] 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 1 0 1 0 1 1 0 1 0
## [75] 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1
## [112] 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0
## [149] 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 0 1 0 0
## [186] 1 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 1
## [223] 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1
## [260] 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1
## [297] 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1
## [334] 0 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 0 1 1 0 0 0
## [371] 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 0datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 0 1 2 0 96
## 2 1 0 1 0 75
## 3 0 1 2 0 77
## 4 0 1 2 0 88
## 5 0 2 0 0 90
## 6 0 1 0 1 88
## 7 0 1 1 1 87
## 8 0 2 0 0 87
## 9 0 2 0 1 85
## 10 1 0 1 0 91
## 11 0 2 0 0 89
## 12 0 1 0 0 79
## 13 0 0 2 0 77
## 14 0 2 0 0 85
## 15 1 1 1 0 91
## 16 1 1 1 1 82
## 17 0 2 0 1 83
## 18 0 1 1 1 93
## 19 0 2 1 1 81
## 20 0 2 1 0 81
## 21 0 1 2 0 78
## 22 0 2 1 1 83
## 23 0 1 1 0 100
## 24 0 1 1 0 89
## 25 0 1 1 0 86
## 26 0 2 1 0 89
## 27 1 1 1 1 82
## 28 0 2 2 0 89
## 29 1 1 1 1 85
## 30 1 1 2 1 85
## 31 1 1 2 1 86
## 32 1 0 2 1 88
## 33 0 2 3 0 88
## 34 0 1 0 1 83
## 35 0 1 0 0 77
## 36 0 2 2 0 84
## 37 0 1 3 0 94
## 38 0 1 1 0 78
## 39 1 1 3 1 85
## 40 1 0 1 0 84
## 41 0 2 2 0 89
## 42 0 0 1 0 71
## 43 1 1 2 0 85
## 44 0 0 1 0 82
## 45 0 2 1 1 85
## 46 1 0 1 1 85
## 47 0 2 1 1 86
## 48 0 2 1 0 85
## 49 0 1 1 0 80
## 50 0 1 1 0 81
## 51 0 1 0 0 83
## 52 0 1 0 1 91
## 53 0 2 1 1 83
## 54 0 2 2 0 88
## 55 1 2 3 1 83
## 56 0 1 2 1 85
## 57 1 0 3 1 90
## 58 0 0 1 0 95
## 59 0 2 2 1 85
## 60 0 1 0 0 82
## 61 0 1 1 0 89
## 62 1 0 1 0 89
## 63 0 2 3 0 85
## 64 0 2 1 1 81
## 65 1 0 3 0 92
## 66 1 1 3 0 83
## 67 0 0 0 0 73
## 68 1 1 2 1 87
## 69 0 2 2 0 78
## 70 1 2 3 1 86
## 71 1 1 2 1 81
## 72 0 1 0 0 88
## 73 1 1 2 0 88
## 74 0 1 1 0 91
## 75 0 2 2 0 92
## 76 0 1 2 0 88
## 77 1 1 1 1 94
## 78 0 1 0 1 101
## 79 0 1 1 0 87
## 80 1 0 3 1 83
## 81 0 2 1 0 85
## 82 0 1 2 0 84
## 83 0 1 1 0 80
## 84 1 2 1 1 92
## 85 0 2 1 0 81
## 86 1 0 1 0 87
## 87 1 0 3 0 82
## 88 0 2 2 1 84
## 89 1 0 3 1 86
## 90 0 2 3 0 84
## 91 1 0 1 1 90
## 92 0 0 1 0 83
## 93 0 1 1 0 85
## 94 1 1 2 1 95
## 95 0 1 1 0 78
## 96 1 0 1 1 90
## 97 1 2 3 1 80
## 98 1 1 2 0 87
## 99 1 0 1 1 85
## 100 0 1 1 1 86
## 101 0 1 0 0 91
## 102 0 1 2 0 91
## 103 0 1 2 0 86
## 104 1 1 3 0 83
## 105 0 1 0 0 89
## 106 1 1 2 0 90
## 107 1 1 0 1 89
## 108 1 0 2 1 80
## 109 1 2 3 0 95
## 110 0 2 0 1 88
## 111 1 1 3 1 82
## 112 0 1 1 0 84
## 113 0 1 2 0 88
## 114 0 1 1 0 82
## 115 0 2 0 1 88
## 116 0 1 2 0 85
## 117 0 1 1 1 88
## 118 0 2 1 0 77
## 119 0 2 0 1 84
## 120 0 0 2 1 90
## 121 1 0 3 1 87
## 122 0 1 0 1 91
## 123 0 2 1 1 92
## 124 1 1 1 1 82
## 125 0 0 0 0 90
## 126 0 1 1 1 91
## 127 0 2 2 0 94
## 128 1 0 3 0 87
## 129 0 1 3 0 84
## 130 0 1 1 0 79
## 131 0 1 1 1 78
## 132 1 0 1 1 86
## 133 0 1 1 0 84
## 134 1 0 0 1 87
## 135 0 2 2 0 75
## 136 0 2 1 1 80
## 137 0 1 1 0 97
## 138 1 1 1 1 85
## 139 1 1 3 0 81
## 140 1 0 2 1 93
## 141 1 1 2 1 72
## 142 0 1 3 0 79
## 143 0 1 0 0 86
## 144 0 2 3 1 91
## 145 0 0 0 1 86
## 146 0 1 0 0 90
## 147 1 1 2 0 87
## 148 0 2 2 0 84
## 149 0 2 2 1 85
## 150 1 1 3 0 84
## 151 0 1 1 0 83
## 152 1 0 1 1 87
## 153 0 1 1 0 89
## 154 0 2 1 1 80
## 155 0 1 1 0 81
## 156 0 2 2 1 92
## 157 0 0 0 1 80
## 158 1 1 2 1 79
## 159 1 1 3 1 82
## 160 0 2 1 0 90
## 161 1 2 2 0 94
## 162 0 2 1 1 81
## 163 0 1 2 0 87
## 164 1 1 3 1 81
## 165 0 1 0 1 87
## 166 1 0 3 1 88
## 167 0 0 0 1 91
## 168 1 0 2 1 85
## 169 1 0 2 1 88
## 170 0 2 1 1 88
## 171 1 0 2 0 87
## 172 0 1 1 0 86
## 173 0 0 1 1 88
## 174 0 1 1 0 82
## 175 0 0 1 0 75
## 176 0 0 2 0 80
## 177 1 1 2 0 83
## 178 1 0 2 0 86
## 179 1 1 2 1 85
## 180 0 1 1 1 91
## 181 1 0 3 1 87
## 182 0 1 0 0 85
## 183 1 0 3 1 85
## 184 0 2 1 1 84
## 185 0 1 1 0 82
## 186 1 0 3 0 93
## 187 1 1 2 1 93
## 188 0 1 1 0 78
## 189 0 2 1 0 75
## 190 1 2 3 1 90
## 191 0 1 1 0 90
## 192 0 2 2 0 79
## 193 0 2 0 0 87
## 194 1 0 2 1 82
## 195 1 1 1 0 90
## 196 0 1 1 0 77
## 197 0 2 2 0 81
## 198 0 1 3 1 90
## 199 0 1 1 0 86
## 200 1 0 3 0 87
## 201 0 2 1 0 87
## 202 0 2 0 0 87
## 203 1 1 3 1 85
## 204 0 1 0 1 86
## 205 0 1 0 0 88
## 206 0 0 0 0 87
## 207 1 1 2 0 83
## 208 1 1 2 1 89
## 209 0 1 1 0 89
## 210 1 0 2 1 96
## 211 1 0 3 0 95
## 212 0 2 1 0 87
## 213 0 2 1 0 84
## 214 0 1 3 0 86
## 215 0 2 1 1 82
## 216 1 1 2 1 76
## 217 0 0 1 0 81
## 218 1 0 3 1 78
## 219 1 0 3 1 90
## 220 1 1 2 1 86
## 221 0 1 1 1 88
## 222 1 1 1 0 93
## 223 0 2 1 1 79
## 224 0 1 1 1 81
## 225 0 2 0 0 97
## 226 0 1 2 1 83
## 227 0 1 1 0 79
## 228 0 1 0 1 78
## 229 0 1 2 0 80
## 230 0 2 1 0 84
## 231 0 1 1 0 83
## 232 0 2 2 1 86
## 233 1 0 1 0 87
## 234 1 0 2 1 86
## 235 1 1 3 1 85
## 236 1 1 2 1 87
## 237 0 2 1 0 94
## 238 0 1 1 1 78
## 239 1 0 3 1 83
## 240 0 1 2 0 86
## 241 1 1 1 1 86
## 242 1 1 2 1 85
## 243 1 0 2 0 74
## 244 0 1 2 0 89
## 245 0 2 2 0 80
## 246 1 0 1 1 85
## 247 1 1 2 1 88
## 248 0 1 1 0 73
## 249 0 2 1 0 77
## 250 1 1 1 0 81
## 251 1 2 3 0 92
## 252 1 0 3 1 86
## 253 1 0 1 1 82
## 254 0 1 1 0 86
## 255 1 1 3 1 93
## 256 0 1 2 0 83
## 257 0 1 1 0 80
## 258 1 2 2 1 88
## 259 1 0 3 0 83
## 260 0 2 2 0 88
## 261 0 2 2 0 87
## 262 1 0 1 0 84
## 263 0 1 0 0 81
## 264 0 0 1 1 81
## 265 1 0 3 0 83
## 266 1 0 1 0 78
## 267 1 0 0 1 83
## 268 0 1 1 0 84
## 269 1 1 2 0 103
## 270 0 2 1 1 85
## 271 0 2 0 1 97
## 272 0 2 1 1 87
## 273 0 1 1 1 86
## 274 1 1 3 0 80
## 275 1 1 0 1 89
## 276 1 2 2 1 86
## 277 1 0 3 0 85
## 278 0 2 1 0 90
## 279 0 2 1 0 85
## 280 1 1 2 0 85
## 281 1 2 3 0 79
## 282 1 1 1 1 84
## 283 0 2 2 1 81
## 284 1 0 2 0 84
## 285 1 0 1 1 85
## 286 0 0 1 0 85
## 287 1 1 1 1 84
## 288 1 0 1 0 89
## 289 1 0 0 1 82
## 290 1 1 1 1 87
## 291 1 1 2 1 89
## 292 1 0 1 1 82
## 293 1 0 3 1 85
## 294 0 1 0 0 79
## 295 1 1 2 1 89
## 296 1 2 3 1 83
## 297 0 1 1 0 89
## 298 1 1 3 0 79
## 299 1 2 3 1 85
## 300 0 0 0 1 86
## 301 0 2 1 0 79
## 302 0 1 2 0 83
## 303 1 0 2 1 91
## 304 0 0 0 0 86
## 305 0 2 2 0 88
## 306 0 2 2 0 85
## 307 0 2 1 1 88
## 308 0 2 0 0 95
## 309 0 1 1 0 80
## 310 0 2 1 0 77
## 311 1 1 1 0 90
## 312 1 1 3 1 78
## 313 1 0 2 1 81
## 314 1 2 2 1 86
## 315 1 0 0 1 85
## 316 0 1 1 0 81
## 317 1 0 3 0 79
## 318 0 1 2 0 82
## 319 0 2 2 0 88
## 320 0 1 2 1 81
## 321 0 1 1 0 83
## 322 1 0 2 1 87
## 323 0 1 1 0 88
## 324 1 1 1 1 84
## 325 0 0 0 0 82
## 326 0 2 0 0 89
## 327 0 0 1 0 75
## 328 0 0 0 1 85
## 329 0 2 2 0 85
## 330 0 1 1 0 89
## 331 0 0 1 0 84
## 332 1 1 2 0 90
## 333 1 1 2 1 85
## 334 0 1 1 0 81
## 335 0 0 1 0 90
## 336 1 0 1 1 79
## 337 0 1 1 0 89
## 338 0 2 2 1 80
## 339 0 1 1 0 83
## 340 1 0 3 0 84
## 341 0 2 1 1 86
## 342 0 0 2 0 85
## 343 1 1 2 0 87
## 344 1 0 3 1 90
## 345 0 1 0 0 85
## 346 1 0 2 1 89
## 347 1 0 2 1 84
## 348 0 1 0 1 86
## 349 0 2 2 1 78
## 350 0 0 1 0 73
## 351 0 1 1 1 76
## 352 0 0 1 0 83
## 353 0 1 1 0 82
## 354 0 1 1 0 78
## 355 0 1 1 0 95
## 356 0 1 2 1 78
## 357 1 0 3 0 95
## 358 0 1 1 0 82
## 359 1 0 0 1 85
## 360 0 1 1 1 78
## 361 0 0 0 1 83
## 362 1 1 2 1 90
## 363 1 1 2 1 88
## 364 1 1 2 1 86
## 365 0 1 0 0 83
## 366 1 1 2 0 91
## 367 1 0 1 1 86
## 368 0 0 1 0 88
## 369 0 2 1 0 85
## 370 0 1 1 0 81
## 371 0 0 1 0 86
## 372 1 0 0 1 81
## 373 0 1 0 0 82
## 374 0 1 0 0 94
## 375 1 1 3 1 92
## 376 0 1 0 0 72
## 377 1 1 3 0 81
## 378 0 1 0 1 84
## 379 1 1 3 0 88
## 380 1 0 1 1 87
## 381 1 0 2 0 86
## 382 0 2 1 0 95
## 383 0 1 0 0 94
## 384 0 2 0 0 81
## 385 0 1 2 1 83
## 386 0 1 0 0 82
## 387 0 0 1 1 76
## 388 0 1 1 1 89
## 389 1 1 3 0 93
## 390 1 0 3 1 83
## 391 1 1 2 1 80
## 392 1 0 2 0 82
## 393 1 0 2 1 99
## 394 0 2 2 1 84
## 395 0 2 1 1 84
## 396 0 1 1 0 85
## 397 0 0 0 1 89
## 398 1 1 3 0 83
## 399 0 2 1 0 86
## 400 0 1 1 1 85modelreglog <- 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) -9.70569 2.63647 -3.681 0.000232 ***
## x1 -2.04777 0.26219 -7.810 5.70e-15 ***
## x2 1.84579 0.21069 8.761 < 2e-16 ***
## x3 1.99962 0.31949 6.259 3.88e-10 ***
## x4 0.08771 0.03028 2.897 0.003767 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 537.59 on 399 degrees of freedom
## Residual deviance: 294.65 on 395 degrees of freedom
## AIC: 304.65
##
## Number of Fisher Scoring iterations: 6Pada skenario di atas, status kelulusan pendaftar terbanyak yaitu pendaftar dengan lulusan satu tahun lalu(gap 1 tahun); pendidikan terakhir terbanyak adalah lulusan SMA; rata-rata nilai rapor pendaftar lebih banyak yang di atas 70; dan nilai seleksi mandiri terbanyak yaitu pada rentang 80-90.
Data X1, X2, X3, dan X4, merupakan variabel yang mewakili status
kelulusan, pendidikan terakhir, rata-rata nilai rapor, dan nilai seleksi
mandiri dari calon mahasiswa baru telah berhasil dibangkitkan sesuai
skenario.
Probabilitas telah dihitung menggunakan model regresi logistik. Data variabel Y (keputusan menerima/menolak) berhasil di bangkitkan berdasarkan probabilitas yang dihasilkan.
Model regresi logistik telah diestimasi menggunakan data yang telah dibangkitkan. Summary dari model regresi logistik telah memberikan informasi mengenai koefisien-koefisien yang diestimasi, nilai deviance, serta uji signifikansi masing-masing koefisien.
Dengan demikian, dokumen ini memberikan gambaran tentang bagaimana proses pembangkitan data dan analisis regresi logistik dapat dilakukan dalam konteks penerimaan calon mahasiswa baru menggunakan jalus seleksi mandiri tes.