Z : Keputusan menolak/menerima siswa pada SNBP UNNES prodi SSD X1 : Rekam jejak alumni (orang) X2 : Pengambilan Jurusan (0 : sesuai,1 : linjur) X3 : Penempatan pilihan PTN (0: pertama,1 : kedua) X4 : nilai (skala 10)
X1 : Rekam jejak alumni (orang) membangkitkan variabel X1 dengan rekam jejak alumni 0-25 orang dengan nilai tengah 17 dan banyaknya pendaftar 247
set.seed(247)
n <- 247
u <- runif(n)
X1 <- round(25*(-log(1-u)/17))
X1## [1] 1 4 2 1 0 2 1 0 5 1 1 1 1 1 1 3 1 2 1 1 0 2 2 2 3 0 1 2 2 1 3 3 0 1 1 3 1
## [38] 1 3 1 1 1 1 0 5 1 3 4 1 4 1 3 2 2 0 0 2 1 3 1 6 0 0 0 3 0 1 2 1 3 0 0 2 0
## [75] 2 2 1 1 1 1 2 2 1 1 1 0 0 0 3 2 3 3 2 1 3 1 1 2 2 2 0 1 0 1 1 1 0 5 3 1 1
## [112] 1 1 1 1 4 1 0 1 1 0 2 1 0 1 0 1 2 1 1 1 2 6 0 0 3 0 0 1 1 2 2 2 1 1 1 1 7
## [149] 1 4 1 1 1 2 6 3 0 0 0 0 3 2 1 1 1 6 1 1 1 1 0 2 3 2 4 0 0 1 1 0 4 1 1 0 0
## [186] 0 1 1 0 0 1 0 0 1 1 1 2 0 0 1 1 3 1 0 2 0 0 0 0 1 2 1 1 0 1 0 4 2 2 0 0 3
## [223] 2 2 1 0 0 2 1 2 2 0 3 1 0 2 0 1 1 0 2 3 0 0 0 1 0X2 : Pengambilan jurusan Keterangan yang digunakan (0=sesuai) dan (1=linjur)
set.seed(111)
X2 <- round(runif(n))
X2## [1] 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0
## [38] 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0
## [75] 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 1 0
## [112] 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 0 1 0
## [149] 0 0 1 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 1 0 0 1 0 1 1 0 1 1 1 0 0
## [186] 0 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 0 1 1 1
## [223] 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 0 1X3 : Penempatan pilihan PTN Keterangan yang digunakan (0= pertama),(1= kedua)
set.seed(171)
X3 <- round(runif(n))
X3## [1] 0 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 0 0
## [38] 1 0 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 0 0 1
## [75] 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0
## [112] 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0 0 1 0 1 1 0 0 0 0 1 0 0 1 1 0 1 1 1 0 0 0
## [149] 1 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0
## [186] 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 1 1 0 0 1 1 0
## [223] 0 1 0 0 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 1X4 adalah data nilai pendaftar dengan skala 10
set.seed(107)
X4 <- sample(1:10,247,replace = TRUE)
X4## [1] 2 3 6 2 3 3 3 6 8 6 8 3 3 3 8 4 4 5 1 2 6 1 7 2 1
## [26] 6 2 10 1 1 4 4 1 5 1 7 9 7 4 5 5 6 8 6 9 5 3 1 3 4
## [51] 4 8 6 4 3 4 2 5 7 8 9 9 2 8 5 1 1 10 6 8 4 3 4 10 3
## [76] 2 8 2 4 1 5 1 4 1 3 5 6 1 5 5 4 4 4 4 1 4 10 10 2 7
## [101] 10 6 2 9 6 8 6 6 4 2 6 6 9 7 8 7 3 5 3 8 9 6 4 3 1
## [126] 3 5 2 5 7 6 9 9 10 9 10 10 3 9 4 8 10 10 2 10 2 10 3 7 2
## [151] 3 5 2 5 1 10 9 1 6 5 3 6 5 10 10 4 10 10 2 3 10 2 10 1 4
## [176] 8 9 9 6 2 8 1 6 2 1 5 10 5 5 3 3 10 9 2 1 6 4 5 8 10
## [201] 2 10 3 6 4 2 4 7 4 8 7 6 3 4 10 8 3 3 2 1 5 1 7 3 8
## [226] 3 8 1 9 1 10 7 7 5 1 10 5 9 6 9 1 8 8 3 10 9 10Menentukan koef
b0 <- -12
b1 <- 2.3
b2 <- 1.8
b3 <- 2.7
b4 <- 3.4set.seed(1)
datapendukung <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datapendukung## [1] -1.1 11.9 15.7 1.6 0.9 2.8 0.5 10.2 29.4 10.7 22.0 5.0 0.5 3.2 20.2
## [16] 8.5 6.6 11.4 -3.6 1.6 11.1 -1.3 16.4 2.1 0.1 8.4 1.6 29.3 0.5 -1.8
## [31] 11.2 10.3 -8.6 7.3 -3.6 20.5 20.9 18.6 10.3 9.1 11.8 10.7 22.0 12.9 31.9
## [46] 10.0 5.1 5.1 2.3 15.3 5.7 26.6 15.7 6.2 2.7 3.4 1.2 10.0 18.7 22.0
## [61] 32.4 18.6 -2.5 15.2 16.4 -8.6 -3.6 29.3 13.4 24.8 3.4 0.0 6.2 24.7 7.3
## [76] -0.6 17.5 1.6 3.9 -4.5 12.3 -2.2 6.6 -6.3 3.2 5.0 12.9 -6.8 16.4 11.4
## [91] 10.3 10.3 8.0 5.7 2.8 6.6 28.8 26.6 1.2 20.9 23.8 10.7 -0.7 23.6 15.2
## [106] 17.5 10.2 19.9 10.3 1.6 10.7 12.5 22.7 18.6 22.0 25.5 2.3 9.5 3.2 19.3
## [121] 18.6 17.5 8.4 2.7 -1.8 0.9 9.1 1.2 11.8 14.1 13.4 27.7 34.2 23.8 18.6
## [136] 28.9 26.5 0.0 22.7 6.6 24.3 28.4 31.1 1.6 27.0 -2.9 26.1 14.3 16.8 4.0
## [151] 5.0 10.0 -2.9 9.6 5.2 33.4 20.4 -8.6 12.9 9.5 5.1 13.0 10.0 24.3 26.1
## [166] 15.4 27.0 28.8 -0.2 5.0 23.8 1.2 33.4 -1.3 13.5 19.7 21.3 25.4 15.2 -2.5
## [181] 26.2 -4.5 15.2 -2.5 -8.6 5.0 28.8 9.1 9.5 0.0 0.5 26.5 20.4 -2.9 -3.6
## [196] 10.7 8.9 6.8 15.2 26.1 1.6 30.7 3.2 10.2 8.0 -5.2 6.1 11.8 1.6 19.3
## [211] 19.1 12.5 3.2 1.6 28.8 19.7 11.9 2.8 -0.6 -4.1 9.5 0.1 18.2 7.3 17.5
## [226] -1.8 17.0 0.5 23.6 0.5 26.6 16.3 18.7 9.1 -5.9 28.4 6.8 25.4 12.5 23.1
## [241] 0.5 22.1 19.7 -1.8 24.7 23.6 26.5p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.2497398944 0.9999932096 0.9999998481 0.8320183851 0.7109495026
## [6] 0.9426758241 0.6224593312 0.9999628311 1.0000000000 0.9999774556
## [11] 0.9999999997 0.9933071491 0.6224593312 0.9608342772 0.9999999983
## [16] 0.9997965730 0.9986414800 0.9999888046 0.0265969936 0.8320183851
## [21] 0.9999848879 0.2141650170 0.9999999246 0.8909031788 0.5249791875
## [26] 0.9997751832 0.8320183851 1.0000000000 0.6224593312 0.1418510649
## [31] 0.9999863260 0.9999663680 0.0001840719 0.9993249173 0.0265969936
## [36] 0.9999999987 0.9999999992 0.9999999916 0.9999663680 0.9998883467
## [41] 0.9999924955 0.9999774556 0.9999999997 0.9999975020 1.0000000000
## [46] 0.9999546021 0.9939401985 0.9939401985 0.9088770390 0.9999997734
## [51] 0.9966651927 1.0000000000 0.9999998481 0.9979746796 0.9370266439
## [56] 0.9677045353 0.7685247835 0.9999546021 0.9999999924 0.9999999997
## [61] 1.0000000000 0.9999999916 0.0758581800 0.9999997495 0.9999999246
## [66] 0.0001840719 0.0265969936 1.0000000000 0.9999984849 1.0000000000
## [71] 0.9677045353 0.5000000000 0.9979746796 1.0000000000 0.9993249173
## [76] 0.3543436938 0.9999999749 0.8320183851 0.9801596943 0.0109869426
## [81] 0.9999954483 0.0997504891 0.9986414800 0.0018329389 0.9608342772
## [86] 0.9933071491 0.9999975020 0.0011125360 0.9999999246 0.9999888046
## [91] 0.9999663680 0.9999663680 0.9996646499 0.9966651927 0.9426758241
## [96] 0.9986414800 1.0000000000 1.0000000000 0.7685247835 0.9999999992
## [101] 1.0000000000 0.9999774556 0.3318122278 0.9999999999 0.9999997495
## [106] 0.9999999749 0.9999628311 0.9999999977 0.9999663680 0.8320183851
## [111] 0.9999774556 0.9999962734 0.9999999999 0.9999999916 0.9999999997
## [116] 1.0000000000 0.9088770390 0.9999251538 0.9608342772 0.9999999958
## [121] 0.9999999916 0.9999999749 0.9997751832 0.9370266439 0.1418510649
## [126] 0.7109495026 0.9998883467 0.7685247835 0.9999924955 0.9999992476
## [131] 0.9999984849 1.0000000000 1.0000000000 1.0000000000 0.9999999916
## [136] 1.0000000000 1.0000000000 0.5000000000 0.9999999999 0.9986414800
## [141] 1.0000000000 1.0000000000 1.0000000000 0.8320183851 1.0000000000
## [146] 0.0521535631 1.0000000000 0.9999993840 0.9999999494 0.9820137900
## [151] 0.9933071491 0.9999546021 0.0521535631 0.9999322759 0.9945137011
## [156] 1.0000000000 0.9999999986 0.0001840719 0.9999975020 0.9999251538
## [161] 0.9939401985 0.9999977397 0.9999546021 1.0000000000 1.0000000000
## [166] 0.9999997949 1.0000000000 1.0000000000 0.4501660027 0.9933071491
## [171] 1.0000000000 0.7685247835 1.0000000000 0.2141650170 0.9999986290
## [176] 0.9999999972 0.9999999994 1.0000000000 0.9999997495 0.0758581800
## [181] 1.0000000000 0.0109869426 0.9999997495 0.0758581800 0.0001840719
## [186] 0.9933071491 1.0000000000 0.9998883467 0.9999251538 0.5000000000
## [191] 0.6224593312 1.0000000000 0.9999999986 0.0521535631 0.0265969936
## [196] 0.9999774556 0.9998636297 0.9988874640 0.9999997495 1.0000000000
## [201] 0.8320183851 1.0000000000 0.9608342772 0.9999628311 0.9996646499
## [206] 0.0054862989 0.9977621515 0.9999924955 0.8320183851 0.9999999958
## [211] 0.9999999949 0.9999962734 0.9608342772 0.8320183851 1.0000000000
## [216] 0.9999999972 0.9999932096 0.9426758241 0.3543436938 0.0163024994
## [221] 0.9999251538 0.5249791875 0.9999999875 0.9993249173 0.9999999749
## [226] 0.1418510649 0.9999999586 0.6224593312 0.9999999999 0.6224593312
## [231] 1.0000000000 0.9999999166 0.9999999924 0.9998883467 0.0027319608
## [236] 1.0000000000 0.9988874640 1.0000000000 0.9999962734 0.9999999999
## [241] 0.6224593312 0.9999999997 0.9999999972 0.1418510649 1.0000000000
## [246] 0.9999999999 1.0000000000set.seed(2)
Z <- rbinom(n,1,p)
Z## [1] 0 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 1 1
## [75] 1 1 1 0 1 0 1 0 1 0 1 1 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
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## [149] 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0
## [186] 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0
## [223] 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1datagab <- data.frame(Z,X1,X2,X3,X4)
datagab## Z X1 X2 X3 X4
## 1 0 1 1 0 2
## 2 1 4 1 1 3
## 3 1 2 0 1 6
## 4 1 1 1 1 2
## 5 0 0 0 1 3
## 6 0 2 0 0 3
## 7 1 1 0 0 3
## 8 1 0 1 0 6
## 9 1 5 0 1 8
## 10 1 1 0 0 6
## 11 1 1 1 1 8
## 12 1 1 1 1 3
## 13 0 1 0 0 3
## 14 1 1 0 1 3
## 15 1 1 0 1 8
## 16 1 3 0 0 4
## 17 1 1 0 1 4
## 18 1 2 1 0 5
## 19 0 1 0 1 1
## 20 1 1 1 1 2
## 21 1 0 0 1 6
## 22 0 2 0 1 1
## 23 1 2 0 0 7
## 24 1 2 0 1 2
## 25 1 3 1 0 1
## 26 1 0 0 0 6
## 27 1 1 1 1 2
## 28 1 2 0 1 10
## 29 0 2 1 1 1
## 30 0 1 1 1 1
## 31 1 3 0 1 4
## 32 1 3 1 0 4
## 33 0 0 0 0 1
## 34 1 1 0 0 5
## 35 0 1 0 1 1
## 36 1 3 1 0 7
## 37 1 1 0 0 9
## 38 1 1 1 1 7
## 39 1 3 1 0 4
## 40 1 1 1 0 5
## 41 1 1 1 1 5
## 42 1 1 0 0 6
## 43 1 1 1 1 8
## 44 1 0 1 1 6
## 45 1 5 1 0 9
## 46 1 1 0 1 5
## 47 1 3 0 0 3
## 48 1 4 1 1 1
## 49 1 1 1 0 3
## 50 1 4 1 1 4
## 51 1 1 1 0 4
## 52 1 3 1 1 8
## 53 1 2 0 1 6
## 54 1 2 0 0 4
## 55 1 0 1 1 3
## 56 1 0 1 0 4
## 57 0 2 1 0 2
## 58 1 1 0 1 5
## 59 1 3 0 0 7
## 60 1 1 1 1 8
## 61 1 6 0 0 9
## 62 1 0 0 0 9
## 63 0 0 0 1 2
## 64 1 0 0 0 8
## 65 1 3 1 1 5
## 66 0 0 0 0 1
## 67 0 1 0 1 1
## 68 1 2 0 1 10
## 69 1 1 0 1 6
## 70 1 3 0 1 8
## 71 1 0 1 0 4
## 72 0 0 1 0 3
## 73 1 2 0 0 4
## 74 1 0 0 1 10
## 75 1 2 1 1 3
## 76 1 2 0 0 2
## 77 1 1 0 0 8
## 78 0 1 1 1 2
## 79 1 1 0 0 4
## 80 0 1 1 0 1
## 81 1 2 0 1 5
## 82 0 2 1 0 1
## 83 1 1 0 1 4
## 84 0 1 0 0 1
## 85 1 1 0 1 3
## 86 1 0 0 0 5
## 87 1 0 1 1 6
## 88 0 0 1 0 1
## 89 1 3 1 1 5
## 90 1 2 1 0 5
## 91 1 3 1 0 4
## 92 1 3 1 0 4
## 93 1 2 1 0 4
## 94 1 1 1 0 4
## 95 1 3 1 1 1
## 96 1 1 0 1 4
## 97 1 1 1 1 10
## 98 1 2 0 0 10
## 99 1 2 1 0 2
## 100 1 2 1 1 7
## 101 1 0 1 0 10
## 102 1 1 0 0 6
## 103 1 0 1 1 2
## 104 1 1 0 1 9
## 105 1 1 1 1 6
## 106 1 1 0 0 8
## 107 1 0 1 0 6
## 108 1 5 0 0 6
## 109 1 3 1 0 4
## 110 1 1 1 1 2
## 111 1 1 0 0 6
## 112 1 1 1 0 6
## 113 1 1 1 0 9
## 114 1 1 1 1 7
## 115 1 1 1 1 8
## 116 1 4 1 1 7
## 117 1 1 1 0 3
## 118 1 0 1 1 5
## 119 1 1 0 1 3
## 120 1 1 1 0 8
## 121 1 0 0 0 9
## 122 1 2 1 1 6
## 123 1 1 1 1 4
## 124 1 0 1 1 3
## 125 0 1 1 1 1
## 126 0 0 0 1 3
## 127 1 1 1 0 5
## 128 1 2 1 0 2
## 129 1 1 1 1 5
## 130 1 1 0 0 7
## 131 1 1 0 1 6
## 132 1 2 1 1 9
## 133 1 6 1 0 9
## 134 1 0 1 0 10
## 135 1 0 0 0 9
## 136 1 3 0 0 10
## 137 1 0 1 1 10
## 138 1 0 1 0 3
## 139 1 1 1 0 9
## 140 1 1 0 1 4
## 141 1 2 1 1 8
## 142 1 2 1 0 10
## 143 1 2 1 1 10
## 144 1 1 1 1 2
## 145 1 1 0 1 10
## 146 0 1 0 0 2
## 147 1 1 1 0 10
## 148 1 7 0 0 3
## 149 1 1 0 1 7
## 150 1 4 0 0 2
## 151 1 1 1 1 3
## 152 1 1 0 1 5
## 153 0 1 0 0 2
## 154 1 2 0 0 5
## 155 1 6 0 0 1
## 156 1 3 1 1 10
## 157 1 0 1 0 9
## 158 0 0 0 0 1
## 159 1 0 1 1 6
## 160 1 0 1 1 5
## 161 1 3 0 0 3
## 162 1 2 0 0 6
## 163 1 1 0 1 5
## 164 1 1 0 0 10
## 165 1 1 1 0 10
## 166 1 6 0 0 4
## 167 1 1 0 1 10
## 168 1 1 1 1 10
## 169 0 1 0 1 2
## 170 1 1 1 1 3
## 171 1 0 1 0 10
## 172 1 2 1 0 2
## 173 1 3 1 1 10
## 174 1 2 0 1 1
## 175 1 4 0 1 4
## 176 1 0 1 1 8
## 177 1 0 0 1 9
## 178 1 1 1 1 9
## 179 1 1 1 1 6
## 180 0 0 0 1 2
## 181 1 4 1 0 8
## 182 0 1 1 0 1
## 183 1 1 1 1 6
## 184 0 0 0 1 2
## 185 0 0 0 0 1
## 186 1 0 0 0 5
## 187 1 1 1 1 10
## 188 1 1 1 0 5
## 189 1 0 1 1 5
## 190 0 0 1 0 3
## 191 1 1 0 0 3
## 192 1 0 1 1 10
## 193 1 0 1 0 9
## 194 0 1 0 0 2
## 195 0 1 0 1 1
## 196 1 1 0 0 6
## 197 1 2 0 1 4
## 198 1 0 1 0 5
## 199 1 0 0 0 8
## 200 1 1 1 0 10
## 201 0 1 1 1 2
## 202 1 3 1 0 10
## 203 1 1 0 1 3
## 204 1 0 1 0 6
## 205 1 2 1 0 4
## 206 0 0 0 0 2
## 207 1 0 1 1 4
## 208 1 0 0 0 7
## 209 1 0 0 0 4
## 210 1 1 1 0 8
## 211 1 2 0 1 7
## 212 1 1 1 0 6
## 213 1 1 0 1 3
## 214 1 0 0 0 4
## 215 1 1 1 1 10
## 216 1 0 1 1 8
## 217 1 4 1 1 3
## 218 1 2 0 0 3
## 219 0 2 0 0 2
## 220 0 0 1 1 1
## 221 1 0 1 1 5
## 222 0 3 1 0 1
## 223 1 2 1 0 7
## 224 1 2 1 1 3
## 225 1 1 0 0 8
## 226 0 0 0 0 3
## 227 1 0 1 0 8
## 228 1 2 1 1 1
## 229 1 1 0 1 9
## 230 0 2 1 1 1
## 231 1 2 0 0 10
## 232 1 0 1 1 7
## 233 1 3 0 0 7
## 234 1 1 1 0 5
## 235 0 0 0 1 1
## 236 1 2 1 0 10
## 237 1 0 1 0 5
## 238 1 1 1 1 9
## 239 1 1 1 0 6
## 240 1 0 1 1 9
## 241 0 2 1 1 1
## 242 1 3 0 0 8
## 243 1 0 1 1 8
## 244 0 0 0 0 3
## 245 1 0 0 1 10
## 246 1 1 0 1 9
## 247 1 0 1 1 10modelreglog <- glm(Z ~ X1 + X2 + X3 + X4, family = binomial(link="logit"),data=datagab)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(modelreglog)##
## Call:
## glm(formula = Z ~ X1 + X2 + X3 + X4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -13.6141 3.1998 -4.255 2.09e-05 ***
## X1 2.5912 0.6990 3.707 0.00021 ***
## X2 2.0157 0.8207 2.456 0.01405 *
## X3 2.5033 0.8762 2.857 0.00428 **
## X4 3.7399 0.8574 4.362 1.29e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 228.382 on 246 degrees of freedom
## Residual deviance: 54.703 on 242 degrees of freedom
## AIC: 64.703
##
## Number of Fisher Scoring iterations: 10kesimpulan Dengan signifikansi statistik dan koefisien diatas, kita dapat menyimpulkan bahwa rekam jejak alumni (X1) dan nilai siswa (X4) adalah faktor yang penting dalam memprediksi keputusan menolak/menerima siswa pada SNBP UNNES prodi SSD. Ini menunjukkan bahwa prestasi akademik siswa (nilai) dan rekam jejak alumni memainkan peran penting dalam proses seleksi masuk prodi SSD, dengan nilai lebih tinggi dan rekam jejak alumni yang lebih baik cenderung meningkatkan kemungkinan diterimanya seorang siswa.