Mencoba membangkitkan data

Skenario

Y : Keputusan PPDB SMP A berdasarkan pada Nilai Ujian Nasional (NEM) SD

X1 : Nilai Matematika

X2 : Nilai IPA

X3 : Nilai Bahasa Indonesia

Membangkitkan Variabel

Membangkitkan data X1

X1 : Nilai Matematika

Membangkitkan nilai matematika dengan nilai rata-rata 7, persebarannya 0.7, serta banyak siswa adalah 500 orang

set.seed(1)
n <-  500
u <- runif(n)

x1 <- round(rnorm(n,7,0.7),2)
x1
##   [1] 7.10 7.29 6.95 6.83 7.49 7.80 5.32 7.40 7.26 6.70 7.67 6.73 6.80 7.60 8.20
##  [16] 7.19 6.70 6.17 6.77 6.34 6.82 7.28 6.40 8.85 7.11 7.79 5.40 7.52 6.08 7.64
##  [31] 7.28 6.71 7.93 6.51 6.59 6.30 6.53 7.66 7.30 7.70 6.73 7.26 7.17 6.00 8.24
##  [46] 7.09 7.54 7.67 6.96 6.79 7.63 6.27 8.38 6.73 8.16 8.06 7.06 7.40 6.28 7.23
##  [61] 7.73 7.07 6.68 6.54 6.97 7.75 6.66 6.92 6.09 7.35 7.92 8.05 7.57 5.69 7.34
##  [76] 7.32 6.75 7.12 6.40 7.48 6.77 5.90 6.74 7.96 6.77 7.51 7.66 7.00 6.75 6.63
##  [91] 7.52 6.26 7.17 6.80 5.41 6.01 7.64 6.87 7.56 8.32 8.03 7.47 7.27 6.87 8.10
## [106] 7.42 6.18 6.89 5.66 6.86 5.19 7.92 6.56 6.70 6.88 7.43 7.47 7.40 6.60 6.05
## [121] 6.73 7.19 6.42 6.95 6.18 6.99 7.09 6.90 6.89 8.23 7.53 7.78 6.35 7.12 7.81
## [136] 6.96 5.51 7.24 5.67 6.43 7.93 7.43 7.76 7.21 6.92 6.35 8.12 7.03 6.50 7.61
## [151] 7.75 8.33 6.58 6.73 6.71 6.74 6.74 6.79 8.01 6.51 6.73 7.46 7.79 6.46 6.64
## [166] 7.37 7.71 6.82 6.00 8.20 8.00 6.50 6.95 5.77 7.40 8.13 5.85 6.45 6.55 6.52
## [181] 5.58 7.35 5.93 6.98 7.42 6.86 7.62 6.98 6.55 7.45 6.70 8.24 6.99 7.60 7.14
## [196] 4.89 6.04 6.70 7.17 5.36 7.67 6.58 6.47 5.91 5.98 7.04 7.36 5.53 6.30 7.38
## [211] 6.68 8.52 7.87 7.42 7.00 7.20 6.51 7.44 8.04 7.76 6.43 5.87 6.92 7.31 7.95
## [226] 6.08 7.26 7.16 7.84 6.98 6.75 6.20 6.64 6.75 8.65 8.71 6.88 6.27 5.62 7.36
## [241] 6.24 8.60 6.38 7.08 9.67 6.22 7.22 6.23 7.24 6.39 7.05 6.79 6.17 7.01 7.69
## [256] 8.12 6.04 6.83 7.81 6.22 5.23 6.34 6.32 7.03 6.72 7.16 6.70 7.26 6.74 7.83
## [271] 6.48 7.20 6.38 7.15 6.97 5.82 6.90 7.83 7.48 7.10 6.17 7.82 7.06 6.68 8.15
## [286] 6.46 7.21 7.90 7.42 6.79 6.71 7.25 7.36 7.01 7.92 6.95 6.51 7.38 5.46 7.27
## [301] 7.35 6.84 6.22 6.72 8.08 6.48 5.37 7.57 6.65 6.64 6.15 6.98 7.49 6.59 6.58
## [316] 7.77 6.83 6.89 6.56 7.63 6.30 7.59 7.56 6.67 7.59 7.69 7.40 8.42 5.63 6.18
## [331] 6.04 7.12 8.11 8.17 7.34 7.62 6.90 7.33 7.26 6.47 6.79 6.91 7.98 6.27 5.52
## [346] 7.54 6.43 6.69 7.63 6.47 6.76 8.05 7.37 7.38 6.90 6.20 5.95 6.84 8.40 7.16
## [361] 7.12 7.23 6.73 6.02 8.87 6.70 6.79 5.75 6.83 6.83 6.82 5.75 8.25 8.23 7.48
## [376] 6.23 7.50 6.83 6.78 7.95 6.14 6.64 6.49 7.01 5.90 6.51 7.50 7.33 6.32 7.39
## [391] 5.30 6.76 7.50 6.54 6.97 5.88 7.59 5.70 6.77 6.82 7.04 6.42 8.28 6.00 7.18
## [406] 4.94 7.00 7.36 6.24 7.49 7.23 7.68 6.41 6.32 5.76 6.77 6.06 7.48 7.05 8.53
## [421] 6.19 7.83 6.63 5.98 7.40 6.00 6.26 6.49 7.15 6.30 7.75 6.16 7.15 7.10 6.25
## [436] 6.70 6.54 7.67 8.09 6.27 7.65 6.95 5.62 6.47 7.32 7.10 5.29 7.41 7.46 6.79
## [451] 6.50 8.38 6.94 6.99 6.21 6.06 5.93 6.70 7.95 8.23 8.10 7.91 6.83 6.14 6.77
## [466] 5.31 6.78 8.16 7.09 7.77 7.34 6.45 8.22 6.95 6.32 7.05 5.94 7.60 7.35 6.75
## [481] 6.66 7.66 6.26 6.31 7.30 6.68 7.65 6.86 7.84 7.35 5.43 6.07 7.90 7.48 6.32
## [496] 6.06 7.72 6.43 8.26 8.24

Membangkitkan data X2

X2 : Nilai IPA

Membangkitkan nilai IPA dengan nilai rata-rata 7, persebarannya 0.6, serta banyak siswa adalah 500 orang

set.seed(2)
x2 <- round(rnorm(n,7,0.6),2)
x2
##   [1] 6.46 7.11 7.95 6.32 6.95 7.08 7.42 6.86 8.19 6.92 7.25 7.59 6.76 6.38 8.07
##  [16] 5.61 7.53 7.02 7.61 7.26 8.25 6.28 7.95 8.17 7.00 5.53 7.29 6.64 7.48 7.17
##  [31] 7.44 7.19 7.65 6.83 6.53 6.64 5.96 6.46 6.66 6.85 6.77 5.82 6.49 8.14 7.37
##  [46] 8.19 6.82 6.95 6.89 6.28 6.50 8.24 6.66 7.77 6.37 5.82 6.81 7.56 7.68 8.00
##  [61] 5.93 8.22 6.58 7.09 7.30 6.51 5.80 6.71 7.05 6.46 6.45 7.20 6.92 7.26 6.97
##  [76] 6.46 7.78 7.46 7.63 6.15 7.60 5.98 6.68 6.18 5.68 8.09 6.61 6.83 6.77 7.23
##  [91] 7.96 8.01 6.29 6.18 6.09 6.25 8.18 7.00 6.49 6.64 7.64 7.16 6.81 6.55 6.48
## [106] 8.23 7.56 8.21 6.75 6.79 6.38 6.85 7.28 7.82 7.34 7.27 7.74 7.69 7.06 6.53
## [121] 7.74 7.08 8.03 6.74 6.37 7.32 6.60 7.38 5.97 5.95 7.41 7.20 7.52 5.79 7.73
## [136] 7.72 7.62 7.47 8.27 6.13 6.65 7.25 6.52 7.05 7.45 6.61 7.39 7.33 6.52 6.40
## [151] 7.59 6.90 7.43 6.49 7.77 6.19 7.46 7.28 7.16 7.40 7.24 6.62 6.84 7.22 6.21
## [166] 6.47 8.25 5.74 6.26 7.59 7.65 7.50 7.03 7.19 6.46 6.61 6.84 6.44 7.49 6.03
## [181] 6.38 6.24 7.24 6.32 7.33 7.71 7.02 7.31 6.61 7.30 6.24 6.95 6.19 6.84 7.65
## [196] 7.42 6.73 6.53 6.49 6.55 7.18 6.39 8.72 7.13 6.42 7.23 6.93 6.79 7.36 7.14
## [211] 7.62 6.69 8.08 6.14 7.08 7.27 7.73 6.21 6.32 8.01 7.24 7.43 6.52 8.15 7.04
## [226] 7.28 6.95 7.54 6.78 8.35 7.94 5.83 6.63 6.98 5.97 6.37 6.66 7.29 7.06 6.65
## [241] 6.95 8.73 7.70 7.33 7.47 6.66 7.06 7.17 6.72 6.73 7.30 6.76 6.89 6.81 7.51
## [256] 7.29 6.82 6.83 7.59 6.90 7.81 6.94 6.81 5.85 6.61 6.16 8.16 7.28 7.32 6.98
## [271] 5.37 6.97 7.00 6.26 7.15 7.96 6.98 7.62 6.76 7.04 8.22 7.39 7.77 6.62 7.81
## [286] 8.00 7.70 7.00 7.78 6.94 6.32 7.35 7.05 6.86 7.89 6.79 7.25 5.74 6.18 6.59
## [301] 6.81 6.81 7.53 5.87 7.44 7.47 7.12 7.55 7.74 6.59 7.31 6.72 6.70 5.82 6.66
## [316] 7.29 7.73 7.07 7.05 6.88 6.70 7.65 7.55 7.22 7.18 7.32 7.58 8.15 7.90 6.81
## [331] 7.57 6.95 6.88 7.44 7.62 6.82 7.46 6.99 7.90 7.27 7.10 7.23 6.23 7.14 7.43
## [346] 6.85 6.81 7.59 7.50 7.23 7.87 7.40 6.78 7.93 8.40 7.84 6.44 7.50 7.44 7.07
## [361] 7.93 7.85 6.78 6.70 5.87 6.32 6.73 7.70 6.81 7.03 6.89 8.37 6.52 7.74 6.98
## [376] 6.60 6.85 8.52 7.09 7.45 6.74 6.56 7.09 6.57 7.79 6.06 7.03 6.98 7.85 7.50
## [391] 7.11 7.80 7.72 7.52 6.93 7.21 6.41 7.68 7.14 7.55 6.87 5.37 6.39 6.50 7.51
## [406] 6.86 6.58 5.53 7.69 7.00 7.04 6.34 7.12 6.26 6.92 6.21 7.66 6.40 7.95 7.17
## [421] 7.44 6.25 7.85 7.44 7.77 7.54 7.32 7.47 6.29 7.21 6.30 6.80 6.39 7.79 7.02
## [436] 6.95 6.74 7.13 6.99 6.73 5.74 6.83 6.06 7.18 7.15 6.92 6.57 7.64 5.79 7.30
## [451] 6.63 7.02 6.85 6.41 7.95 6.84 6.70 8.25 7.40 7.12 6.98 7.07 6.85 6.60 7.57
## [466] 7.30 7.27 6.96 7.78 6.18 7.93 7.87 7.56 6.22 7.17 6.52 6.77 6.64 5.54 6.69
## [481] 6.36 7.08 6.46 6.95 7.96 6.28 6.00 7.04 6.43 6.43 7.52 7.10 7.78 6.35 7.24
## [496] 6.98 6.25 6.44 6.88 7.09

Membangkitkan data X3

X3 : Nilai Bahasa Indonesia

Membangkitkan nilai Bahasa Indonesia dengan nilai rata-rata 7, persebarannya 0.5, serta banyak siswa adalah 500 orang

set.seed(3)
x3 <- round(rnorm(n,7,0.5),2)
x3
##   [1] 6.52 6.85 7.13 6.42 7.10 7.02 7.04 7.56 6.39 7.63 6.63 6.43 6.64 7.13 7.08
##  [16] 6.85 6.52 6.68 7.61 7.10 6.71 6.53 6.90 6.17 6.76 6.63 7.58 7.51 6.96 6.43
##  [31] 7.45 7.43 7.36 7.37 6.82 7.35 7.65 7.02 6.51 7.40 7.39 6.84 7.85 6.60 7.17
##  [46] 5.87 6.92 7.57 6.77 6.55 7.36 6.60 7.13 6.13 6.29 6.77 6.48 7.68 7.46 6.61
##  [61] 7.29 7.46 7.13 7.18 7.59 6.76 6.79 7.48 6.36 7.09 6.98 7.23 7.51 7.13 7.12
##  [76] 7.37 7.61 7.19 6.51 6.92 7.87 6.82 7.34 7.61 7.40 7.00 7.11 6.56 7.22 6.56
##  [91] 6.57 6.51 6.67 7.53 6.80 6.96 6.77 7.27 7.47 6.90 7.31 6.80 7.53 7.30 7.51
## [106] 7.30 7.10 6.05 6.66 7.24 6.77 6.86 6.79 7.81 6.64 6.77 7.01 7.11 7.09 6.97
## [121] 6.25 7.18 7.26 6.76 7.34 6.62 7.19 6.67 6.14 7.58 7.35 7.07 7.75 6.18 7.06
## [136] 5.80 7.72 6.56 6.35 6.56 6.42 6.01 6.51 6.92 7.46 7.20 6.38 6.68 7.97 7.21
## [151] 6.35 8.32 7.24 7.43 7.54 7.11 7.03 6.51 6.34 8.23 6.67 7.46 7.48 7.80 7.92
## [166] 7.35 7.61 6.44 7.33 7.61 7.12 6.79 7.12 6.72 6.75 7.58 8.08 6.15 6.20 6.48
## [181] 7.16 6.56 7.20 7.12 6.78 6.73 6.34 7.34 8.08 6.79 6.32 6.66 7.32 7.39 8.34
## [196] 6.31 7.03 6.90 6.37 6.67 6.33 7.14 7.54 6.18 6.77 7.73 6.16 7.78 6.25 6.41
## [211] 6.82 6.54 7.42 6.76 7.36 6.69 7.17 7.91 7.73 7.21 7.10 7.84 6.52 7.33 6.72
## [226] 8.22 6.85 6.94 7.48 7.30 6.40 6.85 7.20 6.16 6.64 6.87 6.50 6.54 7.22 6.84
## [241] 6.86 7.64 7.15 7.64 7.52 7.33 6.84 7.23 7.24 8.27 6.73 7.68 7.07 6.61 7.94
## [256] 7.28 6.08 6.72 6.99 5.92 7.89 7.38 6.76 6.76 7.17 6.51 6.73 7.03 7.05 7.58
## [271] 7.20 6.93 6.81 7.64 6.65 7.28 7.15 7.11 7.46 6.56 7.00 6.90 5.88 7.24 7.81
## [286] 6.40 7.83 7.10 7.69 6.57 7.34 6.71 6.66 7.19 7.66 7.16 6.75 7.63 6.85 7.41
## [301] 6.37 7.38 6.60 6.81 6.32 8.76 6.39 7.10 7.05 6.56 7.13 7.33 7.38 5.88 6.55
## [316] 6.92 6.97 6.75 7.26 7.02 6.54 7.46 6.83 7.05 8.06 7.10 6.52 6.86 7.08 6.44
## [331] 6.04 6.76 7.27 6.54 7.34 7.76 7.10 7.85 7.14 6.71 6.92 8.24 7.54 7.17 7.11
## [346] 7.03 7.15 7.75 7.31 7.04 5.80 7.02 7.17 7.22 7.11 7.06 6.00 7.75 7.18 7.22
## [361] 7.38 7.20 6.01 6.46 7.53 8.06 6.75 7.20 8.02 6.90 7.69 6.74 6.64 6.91 6.80
## [376] 7.41 6.61 6.53 7.21 8.05 6.78 6.45 6.74 6.76 7.06 6.96 6.82 5.98 6.62 6.21
## [391] 6.86 6.65 6.91 6.89 7.46 6.44 6.67 6.56 6.93 7.15 8.08 7.44 7.25 7.06 6.85
## [406] 7.39 8.06 7.27 7.77 7.83 6.76 6.82 6.74 7.20 6.84 6.66 7.82 6.66 6.84 6.68
## [421] 7.32 7.01 7.11 6.88 7.01 6.38 6.40 7.14 7.86 6.47 6.94 6.68 7.47 7.96 6.50
## [436] 7.27 7.02 6.84 7.14 6.83 7.06 7.27 7.57 6.36 7.46 6.58 6.29 5.67 7.42 8.19
## [451] 7.01 6.18 7.24 6.49 7.55 6.43 6.99 7.15 8.60 7.04 7.29 7.26 6.78 6.66 8.08
## [466] 7.30 6.81 7.36 7.85 7.54 7.39 7.03 6.46 7.00 8.02 6.44 7.57 6.69 5.90 6.39
## [481] 6.98 7.80 7.46 7.41 7.51 6.76 7.82 6.68 6.31 6.87 7.84 5.73 7.01 7.98 6.81
## [496] 7.46 6.28 6.44 6.51 6.97

Membangkitkan data Y

Menentukan Koef

PPDB SMP A Memiliki Standard Minimum NEM sebesar 20.00

b0 <- -20.00
b1 <- 1.00
b2 <- 1.00
b3 <- 1.00
set.seed(3)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)
datapendukung
##   [1]  8.000000e-02  1.250000e+00  2.030000e+00 -4.300000e-01  1.540000e+00
##   [6]  1.900000e+00 -2.200000e-01  1.820000e+00  1.840000e+00  1.250000e+00
##  [11]  1.550000e+00  7.500000e-01  2.000000e-01  1.110000e+00  3.350000e+00
##  [16] -3.500000e-01  7.500000e-01 -1.300000e-01  1.990000e+00  7.000000e-01
##  [21]  1.780000e+00  9.000000e-02  1.250000e+00  3.190000e+00  8.700000e-01
##  [26] -5.000000e-02  2.700000e-01  1.670000e+00  5.200000e-01  1.240000e+00
##  [31]  2.170000e+00  1.330000e+00  2.940000e+00  7.100000e-01 -6.000000e-02
##  [36]  2.900000e-01  1.400000e-01  1.140000e+00  4.700000e-01  1.950000e+00
##  [41]  8.900000e-01 -8.000000e-02  1.510000e+00  7.400000e-01  2.780000e+00
##  [46]  1.150000e+00  1.280000e+00  2.190000e+00  6.200000e-01 -3.800000e-01
##  [51]  1.490000e+00  1.110000e+00  2.170000e+00  6.300000e-01  8.200000e-01
##  [56]  6.500000e-01  3.500000e-01  2.640000e+00  1.420000e+00  1.840000e+00
##  [61]  9.500000e-01  2.750000e+00  3.900000e-01  8.100000e-01  1.860000e+00
##  [66]  1.020000e+00 -7.500000e-01  1.110000e+00 -5.000000e-01  9.000000e-01
##  [71]  1.350000e+00  2.480000e+00  2.000000e+00  8.000000e-02  1.430000e+00
##  [76]  1.150000e+00  2.140000e+00  1.770000e+00  5.400000e-01  5.500000e-01
##  [81]  2.240000e+00 -1.300000e+00  7.600000e-01  1.750000e+00 -1.500000e-01
##  [86]  2.600000e+00  1.380000e+00  3.900000e-01  7.400000e-01  4.200000e-01
##  [91]  2.050000e+00  7.800000e-01  1.300000e-01  5.100000e-01 -1.700000e+00
##  [96] -7.800000e-01  2.590000e+00  1.140000e+00  1.520000e+00  1.860000e+00
## [101]  2.980000e+00  1.430000e+00  1.610000e+00  7.200000e-01  2.090000e+00
## [106]  2.950000e+00  8.400000e-01  1.150000e+00 -9.300000e-01  8.900000e-01
## [111] -1.660000e+00  1.630000e+00  6.300000e-01  2.330000e+00  8.600000e-01
## [116]  1.470000e+00  2.220000e+00  2.200000e+00  7.500000e-01 -4.500000e-01
## [121]  7.200000e-01  1.450000e+00  1.710000e+00  4.500000e-01 -1.100000e-01
## [126]  9.300000e-01  8.800000e-01  9.500000e-01 -1.000000e+00  1.760000e+00
## [131]  2.290000e+00  2.050000e+00  1.620000e+00 -9.100000e-01  2.600000e+00
## [136]  4.800000e-01  8.500000e-01  1.270000e+00  2.900000e-01 -8.800000e-01
## [141]  1.000000e+00  6.900000e-01  7.900000e-01  1.180000e+00  1.830000e+00
## [146]  1.600000e-01  1.890000e+00  1.040000e+00  9.900000e-01  1.220000e+00
## [151]  1.690000e+00  3.550000e+00  1.250000e+00  6.500000e-01  2.020000e+00
## [156]  4.000000e-02  1.230000e+00  5.800000e-01  1.510000e+00  2.140000e+00
## [161]  6.400000e-01  1.540000e+00  2.110000e+00  1.480000e+00  7.700000e-01
## [166]  1.190000e+00  3.570000e+00 -1.000000e+00 -4.100000e-01  3.400000e+00
## [171]  2.770000e+00  7.900000e-01  1.100000e+00 -3.200000e-01  6.100000e-01
## [176]  2.320000e+00  7.700000e-01 -9.600000e-01  2.400000e-01 -9.700000e-01
## [181] -8.800000e-01  1.500000e-01  3.700000e-01  4.200000e-01  1.530000e+00
## [186]  1.300000e+00  9.800000e-01  1.630000e+00  1.240000e+00  1.540000e+00
## [191] -7.400000e-01  1.850000e+00  5.000000e-01  1.830000e+00  3.130000e+00
## [196] -1.380000e+00 -2.000000e-01  1.300000e-01  3.000000e-02 -1.420000e+00
## [201]  1.180000e+00  1.100000e-01  2.730000e+00 -7.800000e-01 -8.300000e-01
## [206]  2.000000e+00  4.500000e-01  1.000000e-01 -9.000000e-02  9.300000e-01
## [211]  1.120000e+00  1.750000e+00  3.370000e+00  3.200000e-01  1.440000e+00
## [216]  1.160000e+00  1.410000e+00  1.560000e+00  2.090000e+00  2.980000e+00
## [221]  7.700000e-01  1.140000e+00 -4.000000e-02  2.790000e+00  1.710000e+00
## [226]  1.580000e+00  1.060000e+00  1.640000e+00  2.100000e+00  2.630000e+00
## [231]  1.090000e+00 -1.120000e+00  4.700000e-01 -1.100000e-01  1.260000e+00
## [236]  1.950000e+00  4.000000e-02  1.000000e-01 -1.000000e-01  8.500000e-01
## [241]  5.000000e-02  4.970000e+00  1.230000e+00  2.050000e+00  4.660000e+00
## [246]  2.100000e-01  1.120000e+00  6.300000e-01  1.200000e+00  1.390000e+00
## [251]  1.080000e+00  1.230000e+00  1.300000e-01  4.300000e-01  3.140000e+00
## [256]  2.690000e+00 -1.060000e+00  3.800000e-01  2.390000e+00 -9.600000e-01
## [261]  9.300000e-01  6.600000e-01 -1.100000e-01 -3.600000e-01  5.000000e-01
## [266] -1.700000e-01  1.590000e+00  1.570000e+00  1.110000e+00  2.390000e+00
## [271] -9.500000e-01  1.100000e+00  1.900000e-01  1.050000e+00  7.700000e-01
## [276]  1.060000e+00  1.030000e+00  2.560000e+00  1.700000e+00  7.000000e-01
## [281]  1.390000e+00  2.110000e+00  7.100000e-01  5.400000e-01  3.770000e+00
## [286]  8.600000e-01  2.740000e+00  2.000000e+00  2.890000e+00  3.000000e-01
## [291]  3.700000e-01  1.310000e+00  1.070000e+00  1.060000e+00  3.470000e+00
## [296]  9.000000e-01  5.100000e-01  7.500000e-01 -1.510000e+00  1.270000e+00
## [301]  5.300000e-01  1.030000e+00  3.500000e-01 -6.000000e-01  1.840000e+00
## [306]  2.710000e+00 -1.120000e+00  2.220000e+00  1.440000e+00 -2.100000e-01
## [311]  5.900000e-01  1.030000e+00  1.570000e+00 -1.710000e+00 -2.100000e-01
## [316]  1.980000e+00  1.530000e+00  7.100000e-01  8.700000e-01  1.530000e+00
## [321] -4.600000e-01  2.700000e+00  1.940000e+00  9.400000e-01  2.830000e+00
## [326]  2.110000e+00  1.500000e+00  3.430000e+00  6.100000e-01 -5.700000e-01
## [331] -3.500000e-01  8.300000e-01  2.260000e+00  2.150000e+00  2.300000e+00
## [336]  2.200000e+00  1.460000e+00  2.170000e+00  2.300000e+00  4.500000e-01
## [341]  8.100000e-01  2.380000e+00  1.750000e+00  5.800000e-01  6.000000e-02
## [346]  1.420000e+00  3.900000e-01  2.030000e+00  2.440000e+00  7.400000e-01
## [351]  4.300000e-01  2.470000e+00  1.320000e+00  2.530000e+00  2.410000e+00
## [356]  1.100000e+00 -1.610000e+00  2.090000e+00  3.020000e+00  1.450000e+00
## [361]  2.430000e+00  2.280000e+00 -4.800000e-01 -8.200000e-01  2.270000e+00
## [366]  1.080000e+00  2.700000e-01  6.500000e-01  1.660000e+00  7.600000e-01
## [371]  1.400000e+00  8.600000e-01  1.410000e+00  2.880000e+00  1.260000e+00
## [376]  2.400000e-01  9.600000e-01  1.880000e+00  1.080000e+00  3.450000e+00
## [381] -3.400000e-01 -3.500000e-01  3.200000e-01  3.400000e-01  7.500000e-01
## [386] -4.700000e-01  1.350000e+00  2.900000e-01  7.900000e-01  1.100000e+00
## [391] -7.300000e-01  1.210000e+00  2.130000e+00  9.500000e-01  1.360000e+00
## [396] -4.700000e-01  6.700000e-01 -6.000000e-02  8.400000e-01  1.520000e+00
## [401]  1.990000e+00 -7.700000e-01  1.920000e+00 -4.400000e-01  1.540000e+00
## [406] -8.100000e-01  1.640000e+00  1.600000e-01  1.700000e+00  2.320000e+00
## [411]  1.030000e+00  8.400000e-01  2.700000e-01 -2.200000e-01 -4.800000e-01
## [416] -3.600000e-01  1.540000e+00  5.400000e-01  1.840000e+00  2.380000e+00
## [421]  9.500000e-01  1.090000e+00  1.590000e+00  3.000000e-01  2.180000e+00
## [426] -8.000000e-02 -2.000000e-02  1.100000e+00  1.300000e+00 -2.000000e-02
## [431]  9.900000e-01 -3.600000e-01  1.010000e+00  2.850000e+00 -2.300000e-01
## [436]  9.200000e-01  3.000000e-01  1.640000e+00  2.220000e+00 -1.700000e-01
## [441]  4.500000e-01  1.050000e+00 -7.500000e-01  1.000000e-02  1.930000e+00
## [446]  6.000000e-01 -1.850000e+00  7.200000e-01  6.700000e-01  2.280000e+00
## [451]  1.400000e-01  1.580000e+00  1.030000e+00 -1.100000e-01  1.710000e+00
## [456] -6.700000e-01 -3.800000e-01  2.100000e+00  3.950000e+00  2.390000e+00
## [461]  2.370000e+00  2.240000e+00  4.600000e-01 -6.000000e-01  2.420000e+00
## [466] -9.000000e-02  8.600000e-01  2.480000e+00  2.720000e+00  1.490000e+00
## [471]  2.660000e+00  1.350000e+00  2.240000e+00  1.700000e-01  1.510000e+00
## [476]  1.000000e-02  2.800000e-01  9.300000e-01 -1.210000e+00 -1.700000e-01
## [481]  8.881784e-16  2.540000e+00  1.800000e-01  6.700000e-01  2.770000e+00
## [486] -2.800000e-01  1.470000e+00  5.800000e-01  5.800000e-01  6.500000e-01
## [491]  7.900000e-01 -1.100000e+00  2.690000e+00  1.810000e+00  3.700000e-01
## [496]  5.000000e-01  2.500000e-01 -6.900000e-01  1.650000e+00  2.300000e+00
p <- exp(datapendukung)/(1+exp(datapendukung))
p
##   [1] 0.5199893 0.7772999 0.8839111 0.3941263 0.8234647 0.8698915 0.4452208
##   [8] 0.8605661 0.8629487 0.7772999 0.8249137 0.6791787 0.5498340 0.7521291
##  [15] 0.9661048 0.4133824 0.6791787 0.4675457 0.8797431 0.6681878 0.8556969
##  [22] 0.5224848 0.7772999 0.9604562 0.7047457 0.4875026 0.5670929 0.8415758
##  [29] 0.6271478 0.7755640 0.8975230 0.7908406 0.9497887 0.6704012 0.4850045
##  [36] 0.5719961 0.5349429 0.7576796 0.6153838 0.8754466 0.7088902 0.4800107
##  [43] 0.8190612 0.6769959 0.9415854 0.7595109 0.7824498 0.8993479 0.6502185
##  [50] 0.4061269 0.8160783 0.7521291 0.8975230 0.6524895 0.6942363 0.6570105
##  [57] 0.5866176 0.9333920 0.8053384 0.8629487 0.7211152 0.9399133 0.5962827
##  [64] 0.6921095 0.8652969 0.7349726 0.3208213 0.7521291 0.3775407 0.7109495
##  [71] 0.7941296 0.9227278 0.8807971 0.5199893 0.8069013 0.7595109 0.8947306
##  [78] 0.8544577 0.6318124 0.6341356 0.9037845 0.2141650 0.6813537 0.8519528
##  [85] 0.4625702 0.9308616 0.7989910 0.5962827 0.6769959 0.6034832 0.8859476
##  [92] 0.6856801 0.5324543 0.6248065 0.1544653 0.3143199 0.9302152 0.7576796
##  [99] 0.8205385 0.8652969 0.9516624 0.8069013 0.8334114 0.6726070 0.8899274
## [106] 0.9502635 0.6984652 0.7595109 0.2829247 0.7088902 0.1597620 0.8361696
## [113] 0.6524895 0.9113313 0.7026607 0.8130574 0.9020312 0.9002495 0.6791787
## [120] 0.3893608 0.6726070 0.8099984 0.8468363 0.6106392 0.4725277 0.7170753
## [127] 0.7068222 0.7211152 0.2689414 0.8532097 0.9080455 0.8859476 0.8347951
## [134] 0.2869998 0.9308616 0.6177479 0.7005671 0.7807427 0.5719961 0.2931778
## [141] 0.7310586 0.6659669 0.6878313 0.7649478 0.8617617 0.5399149 0.8687555
## [148] 0.7388500 0.7290879 0.7720635 0.8442242 0.9720774 0.7772999 0.6570105
## [155] 0.8828810 0.5099987 0.7738186 0.6410674 0.8190612 0.8947306 0.6547535
## [162] 0.8234647 0.8918713 0.8145726 0.6835209 0.7667411 0.9726152 0.2689414
## [169] 0.3989121 0.9677045 0.9410330 0.6878313 0.7502601 0.4206757 0.6479408
## [176] 0.9105199 0.6835209 0.2768782 0.5597136 0.2748805 0.2931778 0.5374298
## [183] 0.5914590 0.6034832 0.8220063 0.7858350 0.7271082 0.8361696 0.7755640
## [190] 0.8234647 0.3230041 0.8641271 0.6224593 0.8617617 0.9581134 0.2010090
## [197] 0.4501660 0.5324543 0.5074994 0.1946616 0.7649478 0.5274723 0.9387738
## [204] 0.3143199 0.3036451 0.8807971 0.6106392 0.5249792 0.4775152 0.7170753
## [211] 0.7539887 0.8519528 0.9667537 0.5793243 0.8084547 0.7613327 0.8037659
## [218] 0.8263534 0.8899274 0.9516624 0.6835209 0.7576796 0.4900013 0.9421330
## [225] 0.8468363 0.8292045 0.7426905 0.8375349 0.8909032 0.9327675 0.7483817
## [232] 0.2460113 0.6153838 0.4725277 0.7790261 0.8754466 0.5099987 0.5249792
## [239] 0.4750208 0.7005671 0.5124974 0.9931047 0.7738186 0.8859476 0.9906223
## [246] 0.5523079 0.7539887 0.6524895 0.7685248 0.8005922 0.7464940 0.7738186
## [253] 0.5324543 0.6058737 0.9585129 0.9364340 0.2573095 0.5938731 0.9160616
## [260] 0.2768782 0.7170753 0.6592604 0.4725277 0.4109596 0.6224593 0.4576021
## [267] 0.8306161 0.8277836 0.7521291 0.9160616 0.2788848 0.7502601 0.5473576
## [274] 0.7407749 0.6835209 0.7426905 0.7369159 0.9282425 0.8455347 0.6681878
## [281] 0.8005922 0.8918713 0.6704012 0.6318124 0.9774674 0.7026607 0.9393461
## [288] 0.8807971 0.9473499 0.5744425 0.5914590 0.7875132 0.7445969 0.7426905
## [295] 0.9698220 0.7109495 0.6248065 0.6791787 0.1809388 0.7807427 0.6294831
## [302] 0.7369159 0.5866176 0.3543437 0.8629487 0.9376141 0.2460113 0.9020312
## [309] 0.8084547 0.4476921 0.6433651 0.7369159 0.8277836 0.1531637 0.4476921
## [316] 0.8786812 0.8220063 0.6704012 0.7047457 0.8220063 0.3869858 0.9370266
## [323] 0.8743521 0.7190997 0.9442756 0.8918713 0.8175745 0.9686291 0.6479408
## [330] 0.3612368 0.4133824 0.6963549 0.9055096 0.8956688 0.9088770 0.9002495
## [337] 0.8115327 0.8975230 0.9088770 0.6106392 0.6921095 0.9152894 0.8519528
## [344] 0.6410674 0.5149955 0.8053384 0.5962827 0.8839111 0.9198271 0.6769959
## [351] 0.6058737 0.9220118 0.7891817 0.9262184 0.9175867 0.7502601 0.1665886
## [358] 0.8899274 0.9534695 0.8099984 0.9190865 0.9072070 0.3822521 0.3057637
## [365] 0.9063618 0.7464940 0.5670929 0.6570105 0.8402380 0.6813537 0.8021839
## [372] 0.7026607 0.8037659 0.9468489 0.7790261 0.5597136 0.7231218 0.8676111
## [379] 0.7464940 0.9692311 0.4158095 0.4133824 0.5793243 0.5841905 0.6791787
## [386] 0.3846162 0.7941296 0.5719961 0.6878313 0.7502601 0.3251947 0.7702989
## [393] 0.8937850 0.7211152 0.7957597 0.3846162 0.6615032 0.4850045 0.6984652
## [400] 0.8205385 0.8797431 0.3164791 0.8721384 0.3917410 0.8234647 0.3078905
## [407] 0.8375349 0.5399149 0.8455347 0.9105199 0.7369159 0.6984652 0.5670929
## [414] 0.4452208 0.3822521 0.4109596 0.8234647 0.6318124 0.8629487 0.9152894
## [421] 0.7211152 0.7483817 0.8306161 0.5744425 0.8984391 0.4800107 0.4950002
## [428] 0.7502601 0.7858350 0.4950002 0.7290879 0.4109596 0.7330201 0.9453187
## [435] 0.4427521 0.7150421 0.5744425 0.8375349 0.9020312 0.4576021 0.6106392
## [442] 0.7407749 0.3208213 0.5025000 0.8732494 0.6456563 0.1358729 0.6726070
## [449] 0.6615032 0.9072070 0.5349429 0.8292045 0.7369159 0.4725277 0.8468363
## [456] 0.3384968 0.4061269 0.8909032 0.9811090 0.9160616 0.9145109 0.9037845
## [463] 0.6130142 0.3543437 0.9183397 0.4775152 0.7026607 0.9227278 0.9381965
## [470] 0.8160783 0.9346247 0.7941296 0.9037845 0.5423979 0.8190612 0.5025000
## [477] 0.5695462 0.7170753 0.2297011 0.4576021 0.5000000 0.9268988 0.5448789
## [484] 0.6615032 0.9410330 0.4304538 0.8130574 0.6410674 0.6410674 0.6570105
## [491] 0.6878313 0.2497399 0.9364340 0.8593619 0.5914590 0.6224593 0.5621765
## [498] 0.3340331 0.8388911 0.9088770

Daftar siswa yang diterima SMP A

(0 = Tidak diterima, 1 = Diterima)

set.seed(4)
y <- rbinom(n,1,p)
y
##   [1] 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1
##  [38] 1 1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1
##  [75] 0 1 1 1 1 1 0 0 1 0 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
## [112] 1 1 1 0 1 1 1 0 0 1 1 0 1 0 0 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 1 0 1 1 1 0
## [149] 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0 0 0 1 0 1
## [186] 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 0 0 1
## [260] 1 0 1 1 0 1 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0
## [297] 1 1 0 1 0 1 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1
## [334] 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1
## [371] 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 0 0
## [408] 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0 1 0
## [445] 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1
## [482] 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1

Tabel siswa yang diterima SMP A

datagab <- data.frame(y,x1,x2,x3)
datagab
##     y   x1   x2   x3
## 1   0 7.10 6.46 6.52
## 2   1 7.29 7.11 6.85
## 3   1 6.95 7.95 7.13
## 4   0 6.83 6.32 6.42
## 5   1 7.49 6.95 7.10
## 6   1 7.80 7.08 7.02
## 7   1 5.32 7.42 7.04
## 8   0 7.40 6.86 7.56
## 9   0 7.26 8.19 6.39
## 10  1 6.70 6.92 7.63
## 11  1 7.67 7.25 6.63
## 12  1 6.73 7.59 6.43
## 13  1 6.80 6.76 6.64
## 14  0 7.60 6.38 7.13
## 15  1 8.20 8.07 7.08
## 16  0 7.19 5.61 6.85
## 17  0 6.70 7.53 6.52
## 18  1 6.17 7.02 6.68
## 19  0 6.77 7.61 7.61
## 20  0 6.34 7.26 7.10
## 21  1 6.82 8.25 6.71
## 22  0 7.28 6.28 6.53
## 23  1 6.40 7.95 6.90
## 24  1 8.85 8.17 6.17
## 25  1 7.11 7.00 6.76
## 26  1 7.79 5.53 6.63
## 27  1 5.40 7.29 7.58
## 28  0 7.52 6.64 7.51
## 29  1 6.08 7.48 6.96
## 30  1 7.64 7.17 6.43
## 31  1 7.28 7.44 7.45
## 32  1 6.71 7.19 7.43
## 33  1 7.93 7.65 7.36
## 34  1 6.51 6.83 7.37
## 35  0 6.59 6.53 6.82
## 36  0 6.30 6.64 7.35
## 37  1 6.53 5.96 7.65
## 38  1 7.66 6.46 7.02
## 39  1 7.30 6.66 6.51
## 40  1 7.70 6.85 7.40
## 41  0 6.73 6.77 7.39
## 42  0 7.26 5.82 6.84
## 43  1 7.17 6.49 7.85
## 44  1 6.00 8.14 6.60
## 45  1 8.24 7.37 7.17
## 46  1 7.09 8.19 5.87
## 47  0 7.54 6.82 6.92
## 48  1 7.67 6.95 7.57
## 49  0 6.96 6.89 6.77
## 50  0 6.79 6.28 6.55
## 51  1 7.63 6.50 7.36
## 52  1 6.27 8.24 6.60
## 53  1 8.38 6.66 7.13
## 54  0 6.73 7.77 6.13
## 55  0 8.16 6.37 6.29
## 56  1 8.06 5.82 6.77
## 57  1 7.06 6.81 6.48
## 58  1 7.40 7.56 7.68
## 59  0 6.28 7.68 7.46
## 60  1 7.23 8.00 6.61
## 61  1 7.73 5.93 7.29
## 62  1 7.07 8.22 7.46
## 63  0 6.68 6.58 7.13
## 64  0 6.54 7.09 7.18
## 65  1 6.97 7.30 7.59
## 66  1 7.75 6.51 6.76
## 67  0 6.66 5.80 6.79
## 68  1 6.92 6.71 7.48
## 69  1 6.09 7.05 6.36
## 70  0 7.35 6.46 7.09
## 71  1 7.92 6.45 6.98
## 72  1 8.05 7.20 7.23
## 73  1 7.57 6.92 7.51
## 74  1 5.69 7.26 7.13
## 75  0 7.34 6.97 7.12
## 76  1 7.32 6.46 7.37
## 77  1 6.75 7.78 7.61
## 78  1 7.12 7.46 7.19
## 79  1 6.40 7.63 6.51
## 80  1 7.48 6.15 6.92
## 81  0 6.77 7.60 7.87
## 82  0 5.90 5.98 6.82
## 83  1 6.74 6.68 7.34
## 84  0 7.96 6.18 7.61
## 85  1 6.77 5.68 7.40
## 86  1 7.51 8.09 7.00
## 87  1 7.66 6.61 7.11
## 88  0 7.00 6.83 6.56
## 89  1 6.75 6.77 7.22
## 90  0 6.63 7.23 6.56
## 91  1 7.52 7.96 6.57
## 92  1 6.26 8.01 6.51
## 93  0 7.17 6.29 6.67
## 94  1 6.80 6.18 7.53
## 95  0 5.41 6.09 6.80
## 96  0 6.01 6.25 6.96
## 97  1 7.64 8.18 6.77
## 98  1 6.87 7.00 7.27
## 99  1 7.56 6.49 7.47
## 100 1 8.32 6.64 6.90
## 101 1 8.03 7.64 7.31
## 102 1 7.47 7.16 6.80
## 103 1 7.27 6.81 7.53
## 104 1 6.87 6.55 7.30
## 105 1 8.10 6.48 7.51
## 106 1 7.42 8.23 7.30
## 107 1 6.18 7.56 7.10
## 108 1 6.89 8.21 6.05
## 109 1 5.66 6.75 6.66
## 110 0 6.86 6.79 7.24
## 111 0 5.19 6.38 6.77
## 112 1 7.92 6.85 6.86
## 113 1 6.56 7.28 6.79
## 114 1 6.70 7.82 7.81
## 115 0 6.88 7.34 6.64
## 116 1 7.43 7.27 6.77
## 117 1 7.47 7.74 7.01
## 118 1 7.40 7.69 7.11
## 119 0 6.60 7.06 7.09
## 120 0 6.05 6.53 6.97
## 121 1 6.73 7.74 6.25
## 122 1 7.19 7.08 7.18
## 123 0 6.42 8.03 7.26
## 124 1 6.95 6.74 6.76
## 125 0 6.18 6.37 7.34
## 126 0 6.99 7.32 6.62
## 127 0 7.09 6.60 7.19
## 128 1 6.90 7.38 6.67
## 129 0 6.89 5.97 6.14
## 130 1 8.23 5.95 7.58
## 131 1 7.53 7.41 7.35
## 132 1 7.78 7.20 7.07
## 133 1 6.35 7.52 7.75
## 134 0 7.12 5.79 6.18
## 135 1 7.81 7.73 7.06
## 136 0 6.96 7.72 5.80
## 137 0 5.51 7.62 7.72
## 138 1 7.24 7.47 6.56
## 139 1 5.67 8.27 6.35
## 140 0 6.43 6.13 6.56
## 141 1 7.93 6.65 6.42
## 142 0 7.43 7.25 6.01
## 143 1 7.76 6.52 6.51
## 144 0 7.21 7.05 6.92
## 145 1 6.92 7.45 7.46
## 146 1 6.35 6.61 7.20
## 147 1 8.12 7.39 6.38
## 148 0 7.03 7.33 6.68
## 149 0 6.50 6.52 7.97
## 150 1 7.61 6.40 7.21
## 151 1 7.75 7.59 6.35
## 152 1 8.33 6.90 8.32
## 153 1 6.58 7.43 7.24
## 154 0 6.73 6.49 7.43
## 155 1 6.71 7.77 7.54
## 156 0 6.74 6.19 7.11
## 157 1 6.74 7.46 7.03
## 158 1 6.79 7.28 6.51
## 159 1 8.01 7.16 6.34
## 160 1 6.51 7.40 8.23
## 161 0 6.73 7.24 6.67
## 162 0 7.46 6.62 7.46
## 163 1 7.79 6.84 7.48
## 164 1 6.46 7.22 7.80
## 165 1 6.64 6.21 7.92
## 166 1 7.37 6.47 7.35
## 167 1 7.71 8.25 7.61
## 168 0 6.82 5.74 6.44
## 169 0 6.00 6.26 7.33
## 170 1 8.20 7.59 7.61
## 171 1 8.00 7.65 7.12
## 172 1 6.50 7.50 6.79
## 173 1 6.95 7.03 7.12
## 174 0 5.77 7.19 6.72
## 175 1 7.40 6.46 6.75
## 176 1 8.13 6.61 7.58
## 177 1 5.85 6.84 8.08
## 178 0 6.45 6.44 6.15
## 179 1 6.55 7.49 6.20
## 180 0 6.52 6.03 6.48
## 181 0 5.58 6.38 7.16
## 182 0 7.35 6.24 6.56
## 183 1 5.93 7.24 7.20
## 184 0 6.98 6.32 7.12
## 185 1 7.42 7.33 6.78
## 186 1 6.86 7.71 6.73
## 187 0 7.62 7.02 6.34
## 188 1 6.98 7.31 7.34
## 189 1 6.55 6.61 8.08
## 190 0 7.45 7.30 6.79
## 191 0 6.70 6.24 6.32
## 192 0 8.24 6.95 6.66
## 193 0 6.99 6.19 7.32
## 194 1 7.60 6.84 7.39
## 195 1 7.14 7.65 8.34
## 196 0 4.89 7.42 6.31
## 197 1 6.04 6.73 7.03
## 198 1 6.70 6.53 6.90
## 199 1 7.17 6.49 6.37
## 200 0 5.36 6.55 6.67
## 201 1 7.67 7.18 6.33
## 202 0 6.58 6.39 7.14
## 203 1 6.47 8.72 7.54
## 204 0 5.91 7.13 6.18
## 205 0 5.98 6.42 6.77
## 206 1 7.04 7.23 7.73
## 207 1 7.36 6.93 6.16
## 208 1 5.53 6.79 7.78
## 209 0 6.30 7.36 6.25
## 210 1 7.38 7.14 6.41
## 211 1 6.68 7.62 6.82
## 212 0 8.52 6.69 6.54
## 213 1 7.87 8.08 7.42
## 214 0 7.42 6.14 6.76
## 215 1 7.00 7.08 7.36
## 216 1 7.20 7.27 6.69
## 217 1 6.51 7.73 7.17
## 218 1 7.44 6.21 7.91
## 219 1 8.04 6.32 7.73
## 220 1 7.76 8.01 7.21
## 221 1 6.43 7.24 7.10
## 222 1 5.87 7.43 7.84
## 223 1 6.92 6.52 6.52
## 224 1 7.31 8.15 7.33
## 225 1 7.95 7.04 6.72
## 226 1 6.08 7.28 8.22
## 227 1 7.26 6.95 6.85
## 228 1 7.16 7.54 6.94
## 229 1 7.84 6.78 7.48
## 230 0 6.98 8.35 7.30
## 231 1 6.75 7.94 6.40
## 232 1 6.20 5.83 6.85
## 233 1 6.64 6.63 7.20
## 234 0 6.75 6.98 6.16
## 235 1 8.65 5.97 6.64
## 236 1 8.71 6.37 6.87
## 237 0 6.88 6.66 6.50
## 238 1 6.27 7.29 6.54
## 239 0 5.62 7.06 7.22
## 240 1 7.36 6.65 6.84
## 241 1 6.24 6.95 6.86
## 242 1 8.60 8.73 7.64
## 243 1 6.38 7.70 7.15
## 244 1 7.08 7.33 7.64
## 245 1 9.67 7.47 7.52
## 246 1 6.22 6.66 7.33
## 247 1 7.22 7.06 6.84
## 248 0 6.23 7.17 7.23
## 249 0 7.24 6.72 7.24
## 250 1 6.39 6.73 8.27
## 251 0 7.05 7.30 6.73
## 252 1 6.79 6.76 7.68
## 253 1 6.17 6.89 7.07
## 254 1 7.01 6.81 6.61
## 255 1 7.69 7.51 7.94
## 256 1 8.12 7.29 7.28
## 257 0 6.04 6.82 6.08
## 258 0 6.83 6.83 6.72
## 259 1 7.81 7.59 6.99
## 260 1 6.22 6.90 5.92
## 261 0 5.23 7.81 7.89
## 262 1 6.34 6.94 7.38
## 263 1 6.32 6.81 6.76
## 264 0 7.03 5.85 6.76
## 265 1 6.72 6.61 7.17
## 266 0 7.16 6.16 6.51
## 267 1 6.70 8.16 6.73
## 268 1 7.26 7.28 7.03
## 269 1 6.74 7.32 7.05
## 270 0 7.83 6.98 7.58
## 271 1 6.48 5.37 7.20
## 272 0 7.20 6.97 6.93
## 273 0 6.38 7.00 6.81
## 274 1 7.15 6.26 7.64
## 275 1 6.97 7.15 6.65
## 276 0 5.82 7.96 7.28
## 277 1 6.90 6.98 7.15
## 278 1 7.83 7.62 7.11
## 279 0 7.48 6.76 7.46
## 280 1 7.10 7.04 6.56
## 281 0 6.17 8.22 7.00
## 282 1 7.82 7.39 6.90
## 283 0 7.06 7.77 5.88
## 284 1 6.68 6.62 7.24
## 285 1 8.15 7.81 7.81
## 286 1 6.46 8.00 6.40
## 287 1 7.21 7.70 7.83
## 288 1 7.90 7.00 7.10
## 289 1 7.42 7.78 7.69
## 290 1 6.79 6.94 6.57
## 291 1 6.71 6.32 7.34
## 292 1 7.25 7.35 6.71
## 293 1 7.36 7.05 6.66
## 294 1 7.01 6.86 7.19
## 295 1 7.92 7.89 7.66
## 296 0 6.95 6.79 7.16
## 297 1 6.51 7.25 6.75
## 298 1 7.38 5.74 7.63
## 299 0 5.46 6.18 6.85
## 300 1 7.27 6.59 7.41
## 301 0 7.35 6.81 6.37
## 302 1 6.84 6.81 7.38
## 303 1 6.22 7.53 6.60
## 304 0 6.72 5.87 6.81
## 305 0 8.08 7.44 6.32
## 306 0 6.48 7.47 8.76
## 307 1 5.37 7.12 6.39
## 308 0 7.57 7.55 7.10
## 309 1 6.65 7.74 7.05
## 310 1 6.64 6.59 6.56
## 311 0 6.15 7.31 7.13
## 312 1 6.98 6.72 7.33
## 313 1 7.49 6.70 7.38
## 314 1 6.59 5.82 5.88
## 315 0 6.58 6.66 6.55
## 316 1 7.77 7.29 6.92
## 317 1 6.83 7.73 6.97
## 318 1 6.89 7.07 6.75
## 319 0 6.56 7.05 7.26
## 320 1 7.63 6.88 7.02
## 321 1 6.30 6.70 6.54
## 322 1 7.59 7.65 7.46
## 323 1 7.56 7.55 6.83
## 324 1 6.67 7.22 7.05
## 325 1 7.59 7.18 8.06
## 326 1 7.69 7.32 7.10
## 327 1 7.40 7.58 6.52
## 328 1 8.42 8.15 6.86
## 329 0 5.63 7.90 7.08
## 330 1 6.18 6.81 6.44
## 331 0 6.04 7.57 6.04
## 332 1 7.12 6.95 6.76
## 333 1 8.11 6.88 7.27
## 334 1 8.17 7.44 6.54
## 335 1 7.34 7.62 7.34
## 336 1 7.62 6.82 7.76
## 337 1 6.90 7.46 7.10
## 338 0 7.33 6.99 7.85
## 339 1 7.26 7.90 7.14
## 340 1 6.47 7.27 6.71
## 341 0 6.79 7.10 6.92
## 342 1 6.91 7.23 8.24
## 343 1 7.98 6.23 7.54
## 344 1 6.27 7.14 7.17
## 345 1 5.52 7.43 7.11
## 346 1 7.54 6.85 7.03
## 347 1 6.43 6.81 7.15
## 348 1 6.69 7.59 7.75
## 349 0 7.63 7.50 7.31
## 350 1 6.47 7.23 7.04
## 351 1 6.76 7.87 5.80
## 352 1 8.05 7.40 7.02
## 353 1 7.37 6.78 7.17
## 354 1 7.38 7.93 7.22
## 355 1 6.90 8.40 7.11
## 356 1 6.20 7.84 7.06
## 357 0 5.95 6.44 6.00
## 358 1 6.84 7.50 7.75
## 359 1 8.40 7.44 7.18
## 360 1 7.16 7.07 7.22
## 361 1 7.12 7.93 7.38
## 362 1 7.23 7.85 7.20
## 363 0 6.73 6.78 6.01
## 364 0 6.02 6.70 6.46
## 365 1 8.87 5.87 7.53
## 366 1 6.70 6.32 8.06
## 367 1 6.79 6.73 6.75
## 368 1 5.75 7.70 7.20
## 369 0 6.83 6.81 8.02
## 370 1 6.83 7.03 6.90
## 371 1 6.82 6.89 7.69
## 372 1 5.75 8.37 6.74
## 373 1 8.25 6.52 6.64
## 374 1 8.23 7.74 6.91
## 375 1 7.48 6.98 6.80
## 376 1 6.23 6.60 7.41
## 377 1 7.50 6.85 6.61
## 378 1 6.83 8.52 6.53
## 379 1 6.78 7.09 7.21
## 380 1 7.95 7.45 8.05
## 381 0 6.14 6.74 6.78
## 382 1 6.64 6.56 6.45
## 383 1 6.49 7.09 6.74
## 384 0 7.01 6.57 6.76
## 385 0 5.90 7.79 7.06
## 386 1 6.51 6.06 6.96
## 387 1 7.50 7.03 6.82
## 388 0 7.33 6.98 5.98
## 389 1 6.32 7.85 6.62
## 390 1 7.39 7.50 6.21
## 391 0 5.30 7.11 6.86
## 392 1 6.76 7.80 6.65
## 393 1 7.50 7.72 6.91
## 394 0 6.54 7.52 6.89
## 395 0 6.97 6.93 7.46
## 396 0 5.88 7.21 6.44
## 397 1 7.59 6.41 6.67
## 398 0 5.70 7.68 6.56
## 399 0 6.77 7.14 6.93
## 400 1 6.82 7.55 7.15
## 401 0 7.04 6.87 8.08
## 402 0 6.42 5.37 7.44
## 403 1 8.28 6.39 7.25
## 404 1 6.00 6.50 7.06
## 405 1 7.18 7.51 6.85
## 406 0 4.94 6.86 7.39
## 407 0 7.00 6.58 8.06
## 408 0 7.36 5.53 7.27
## 409 1 6.24 7.69 7.77
## 410 1 7.49 7.00 7.83
## 411 0 7.23 7.04 6.76
## 412 1 7.68 6.34 6.82
## 413 1 6.41 7.12 6.74
## 414 0 6.32 6.26 7.20
## 415 1 5.76 6.92 6.84
## 416 0 6.77 6.21 6.66
## 417 0 6.06 7.66 7.82
## 418 1 7.48 6.40 6.66
## 419 1 7.05 7.95 6.84
## 420 1 8.53 7.17 6.68
## 421 1 6.19 7.44 7.32
## 422 1 7.83 6.25 7.01
## 423 1 6.63 7.85 7.11
## 424 1 5.98 7.44 6.88
## 425 1 7.40 7.77 7.01
## 426 1 6.00 7.54 6.38
## 427 1 6.26 7.32 6.40
## 428 1 6.49 7.47 7.14
## 429 0 7.15 6.29 7.86
## 430 1 6.30 7.21 6.47
## 431 1 7.75 6.30 6.94
## 432 0 6.16 6.80 6.68
## 433 1 7.15 6.39 7.47
## 434 1 7.10 7.79 7.96
## 435 0 6.25 7.02 6.50
## 436 0 6.70 6.95 7.27
## 437 1 6.54 6.74 7.02
## 438 1 7.67 7.13 6.84
## 439 1 8.09 6.99 7.14
## 440 0 6.27 6.73 6.83
## 441 1 7.65 5.74 7.06
## 442 0 6.95 6.83 7.27
## 443 1 5.62 6.06 7.57
## 444 0 6.47 7.18 6.36
## 445 1 7.32 7.15 7.46
## 446 0 7.10 6.92 6.58
## 447 0 5.29 6.57 6.29
## 448 1 7.41 7.64 5.67
## 449 0 7.46 5.79 7.42
## 450 1 6.79 7.30 8.19
## 451 1 6.50 6.63 7.01
## 452 0 8.38 7.02 6.18
## 453 0 6.94 6.85 7.24
## 454 0 6.99 6.41 6.49
## 455 1 6.21 7.95 7.55
## 456 0 6.06 6.84 6.43
## 457 1 5.93 6.70 6.99
## 458 1 6.70 8.25 7.15
## 459 1 7.95 7.40 8.60
## 460 1 8.23 7.12 7.04
## 461 1 8.10 6.98 7.29
## 462 1 7.91 7.07 7.26
## 463 0 6.83 6.85 6.78
## 464 1 6.14 6.60 6.66
## 465 1 6.77 7.57 8.08
## 466 1 5.31 7.30 7.30
## 467 0 6.78 7.27 6.81
## 468 1 8.16 6.96 7.36
## 469 1 7.09 7.78 7.85
## 470 1 7.77 6.18 7.54
## 471 1 7.34 7.93 7.39
## 472 1 6.45 7.87 7.03
## 473 1 8.22 7.56 6.46
## 474 0 6.95 6.22 7.00
## 475 1 6.32 7.17 8.02
## 476 1 7.05 6.52 6.44
## 477 1 5.94 6.77 7.57
## 478 1 7.60 6.64 6.69
## 479 0 7.35 5.54 5.90
## 480 0 6.75 6.69 6.39
## 481 1 6.66 6.36 6.98
## 482 1 7.66 7.08 7.80
## 483 1 6.26 6.46 7.46
## 484 0 6.31 6.95 7.41
## 485 1 7.30 7.96 7.51
## 486 1 6.68 6.28 6.76
## 487 1 7.65 6.00 7.82
## 488 1 6.86 7.04 6.68
## 489 1 7.84 6.43 6.31
## 490 1 7.35 6.43 6.87
## 491 0 5.43 7.52 7.84
## 492 0 6.07 7.10 5.73
## 493 1 7.90 7.78 7.01
## 494 1 7.48 6.35 7.98
## 495 1 6.32 7.24 6.81
## 496 1 6.06 6.98 7.46
## 497 0 7.72 6.25 6.28
## 498 1 6.43 6.44 6.44
## 499 1 8.26 6.88 6.51
## 500 1 8.24 7.09 6.97

Analisis Regresi Logistik

modelreglog <- glm(y~x1+x2+x3, family = binomial(link = "logit"), data=datagab)
summary(modelreglog)
## 
## Call:
## glm(formula = y ~ x1 + x2 + x3, family = binomial(link = "logit"), 
##     data = datagab)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2900  -0.9899   0.5477   0.8346   1.8979  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -18.4078     2.4228  -7.598 3.01e-14 ***
## x1            0.8799     0.1547   5.689 1.28e-08 ***
## x2            1.0406     0.1832   5.680 1.35e-08 ***
## x3            0.8388     0.2149   3.903 9.50e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 622.26  on 499  degrees of freedom
## Residual deviance: 537.29  on 496  degrees of freedom
## AIC: 545.29
## 
## Number of Fisher Scoring iterations: 4

Kesimpulan

Kesimpulan dari analisis regresi logistik ini akan memberikan pemahaman yang lebih mendalam tentang hubungan antara nilai ujian nasional dengan keputusan diterima atau tidak diterima di SMP A berdasarkan pada NEM dari mata pelajaran Matematika, IPA, dan Bahasa Indonesia.