Y : Keputusan menolak/menerima pelamar anggota tentara X1 : Lama Pengalaman Daftar sebelumnya (kali) X2 : Status pendidikan saat ini (0: Tidak Lulus 1: Lulus) X3 : Tingkat pendidikan (0: Lulus Sekolah Menengah Atas 1: Pekerja) X4 : Nilai Tes (Skala 100)
X1 : Lama Pengalaman Daftar sebelumnya (kali) Membangkitkan Variabel XL dengan lama pengalaman daftar kali dengan nilai tengah 4 dan banyak pelamar adalah 370
set.seed(85)
n <- 370
u <- runif(n)
x1 <- round (7*(-(log(1-u)/12)))
x1## [1] 0 0 0 1 0 1 0 0 0 1 0 1 0 2 0 0 1 2 0 1 0 0 0 1 0 0 0 0 1 0 1 1 0 2 0 1 0
## [38] 1 0 1 0 1 0 0 0 1 1 3 0 0 0 0 1 1 1 1 2 2 1 1 0 0 0 0 0 1 0 0 2 1 1 0 0 0
## [75] 1 1 1 0 0 1 0 1 0 0 1 1 1 0 0 1 0 0 0 0 2 2 0 1 0 2 0 0 0 1 0 1 1 2 0 0 1
## [112] 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 2 1 1 1 0 0 0 1 0 1 0 0 0
## [149] 0 0 0 0 0 1 0 1 1 3 2 1 1 0 1 0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0
## [186] 0 0 2 0 0 0 0 0 0 0 2 0 0 0 1 1 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1
## [223] 0 1 0 0 1 1 0 0 0 3 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 1 0
## [260] 0 2 2 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 2 0 1 1 1 0 1 0 0 0 3 0 1 0 1 1
## [297] 0 1 0 0 0 0 0 0 0 1 1 0 2 0 2 1 0 1 1 2 1 0 1 1 1 0 0 0 0 1 1 0 0 1 1 1 0
## [334] 0 0 0 0 0 0 0 0 0 1 1 0 2 0 1 1 0 0 0 1 0 1 1 0 2 0 1 1 1 0 0 0 1 1 0 0 0X2 : Status pendidikan Keterangan yang digunakan (0=Tidak Lulus) dan (1=Lulua)
set.seed(15)
x2 <- round(runif(n))
x2## [1] 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0
## [38] 1 0 1 1 0 1 0 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 1
## [75] 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 0
## [112] 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 0 0 0 1
## [149] 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 1 1 1 0 0 0 0 0 0 1
## [186] 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 0 0
## [223] 1 1 1 1 1 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 1 0
## [260] 1 0 0 0 0 1 1 0 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 1 1 1 1 1 1
## [297] 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0
## [334] 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 0 0X3 : Tingkat Pendidikan Keterangan yang digunakan (0: Lulus Sekolah Menengah Atas, 1: Pekerja)
set.seed(60)
x3 <- round(runif(n))
x3## [1] 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0
## [38] 0 1 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0
## [75] 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 1 0 1 1 0 0
## [112] 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1
## [149] 1 1 1 1 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1
## [186] 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 0 0 0 1 0
## [223] 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0 1 1 1
## [260] 1 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 1 1
## [297] 1 1 1 1 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0
## [334] 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0X4 adalah data nilai tes dengan skala 100
set.seed(100)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 2.75 3.07 2.96 3.44 3.06 3.16 2.71 3.36 2.59 2.82 3.04 3.05 2.90 3.37 3.06
## [16] 2.99 2.81 3.26 2.54 4.16 2.78 3.38 3.13 3.39 2.59 2.78 2.64 3.12 2.42 3.12
## [31] 2.95 3.88 2.93 2.94 2.65 2.89 3.09 3.21 3.53 3.49 2.95 3.70 2.11 3.31 2.74
## [46] 3.66 2.82 3.66 3.02 2.06 2.78 2.13 3.09 3.95 1.86 3.49 2.30 3.91 3.69 2.58
## [61] 2.87 2.97 2.81 4.29 3.06 2.64 3.32 3.10 2.97 2.95 3.22 2.47 2.42 3.82 1.97
## [76] 3.01 2.46 3.14 3.50 1.96 3.45 2.98 2.33 2.03 3.35 2.92 3.11 3.41 3.86 2.95
## [91] 2.72 3.71 2.55 2.42 2.73 4.22 2.58 3.21 2.41 2.41 2.83 3.68 2.77 3.42 2.27
## [106] 2.80 2.61 2.82 3.62 2.95 3.09 3.13 2.69 2.29 2.83 3.06 3.51 2.87 2.85 3.81
## [121] 2.61 3.21 2.71 3.21 2.23 2.74 2.86 3.50 2.77 3.15 2.79 2.57 3.34 2.77 3.67
## [136] 3.22 2.92 3.23 2.98 3.23 2.80 1.93 3.08 3.33 2.51 2.44 2.78 2.74 3.21 3.07
## [151] 3.52 3.83 2.99 2.99 3.13 2.83 2.94 2.95 3.13 3.07 2.88 3.03 2.91 3.40 3.00
## [166] 2.69 2.87 2.65 3.10 3.42 3.32 3.10 2.95 3.14 2.97 1.98 3.18 2.81 3.63 4.08
## [181] 2.38 3.29 3.06 2.74 3.31 3.35 2.95 2.85 2.46 2.69 2.88 2.87 3.48 2.87 3.95
## [196] 2.79 3.79 3.08 2.46 3.29 3.01 2.82 3.43 3.26 3.51 2.49 2.72 2.49 1.49 3.17
## [211] 3.62 3.34 2.33 2.57 2.11 3.62 2.83 2.52 3.44 2.87 2.24 2.99 3.01 3.08 3.21
## [226] 2.69 3.32 2.40 3.67 2.70 3.22 3.31 2.85 2.20 2.83 2.71 2.80 3.42 3.34 3.12
## [241] 2.64 4.31 2.19 2.20 3.17 4.36 2.84 2.37 2.78 4.27 2.61 2.75 2.86 3.37 3.33
## [256] 2.77 2.04 3.94 2.38 2.76 3.11 3.21 2.39 3.16 3.44 3.24 3.43 2.56 2.94 3.55
## [271] 3.26 3.20 3.53 2.78 3.31 2.59 2.87 3.08 3.28 1.67 3.57 3.21 3.68 3.55 3.32
## [286] 3.09 3.33 2.97 3.32 3.18 3.42 2.66 2.80 3.20 2.45 2.81 3.15 3.90 3.41 2.34
## [301] 2.07 2.49 2.48 2.67 3.19 3.52 3.07 3.19 2.06 3.26 3.43 2.60 2.75 2.89 2.77
## [316] 3.15 2.70 2.78 2.64 3.39 2.39 3.45 3.13 2.97 3.10 4.24 3.24 3.66 3.33 2.93
## [331] 2.29 3.60 3.15 3.23 3.22 2.89 2.84 2.65 1.80 2.21 3.88 1.82 3.20 3.28 2.48
## [346] 3.51 2.74 3.02 3.70 2.78 2.93 3.65 2.96 2.79 3.57 2.79 3.70 2.85 3.29 1.66
## [361] 2.30 4.65 3.43 3.58 3.14 2.99 1.66 3.06 2.75 3.69b0 <- -9
b1 <- 3.5
b2 <- 2.5
b3 <- 4.6
b4 <- 1.2set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 1.400 -5.316 1.652 5.728 -5.328 0.792 -3.248 -0.368 -3.392 4.984
## [11] -5.352 0.660 1.580 4.544 1.772 1.688 -2.128 4.412 -1.352 1.992
## [21] -3.164 -2.444 -5.244 3.168 1.208 1.436 -5.832 -5.256 -0.096 -0.656
## [31] 2.640 -0.844 -5.484 1.528 1.280 5.068 -5.292 0.852 -0.164 1.188
## [41] -2.960 -1.060 -3.968 -0.428 -1.112 3.492 2.484 12.992 1.724 -6.528
## [51] 1.436 0.656 0.708 1.740 -3.268 1.188 7.860 5.192 6.028 2.196
## [61] -5.556 -2.936 1.472 -1.352 -2.828 0.168 -2.516 -0.680 8.664 0.540
## [71] 0.864 1.064 -6.096 -1.916 -0.636 5.212 2.052 -0.632 -4.800 3.952
## [81] 2.240 5.176 0.896 -4.064 5.620 5.104 2.832 2.192 0.232 5.140
## [91] 1.364 -4.548 -5.940 -1.496 8.376 3.064 1.196 -1.648 0.992 0.892
## [101] 1.496 -2.084 1.424 3.204 -1.676 4.960 0.132 8.484 2.444 -5.460
## [111] -1.792 1.856 -5.772 -6.252 -3.104 0.672 -2.288 -3.056 2.520 2.672
## [121] 0.132 -0.548 -5.748 -5.148 -6.324 2.388 -5.568 -0.200 -1.076 0.780
## [131] -1.052 1.184 -4.992 -3.176 -2.096 6.464 2.604 0.876 5.176 1.976
## [141] -1.040 -2.084 2.796 2.096 -2.488 -1.472 -1.064 1.388 1.952 1.784
## [151] -0.176 2.696 1.688 0.588 -2.744 4.996 -1.972 7.540 4.256 5.284
## [161] 5.056 -2.864 5.092 2.180 1.700 -3.272 -0.956 4.780 -5.280 1.104
## [171] -0.416 5.320 -1.960 -2.732 -2.936 3.976 1.916 -3.128 -4.644 -0.604
## [181] -6.144 -5.052 -0.728 -5.712 2.072 -4.980 1.640 3.920 -1.448 1.328
## [191] 1.556 -0.956 -0.224 -5.556 2.840 1.348 -4.452 -2.804 -6.048 3.048
## [201] 2.712 4.984 -0.284 2.012 -1.288 1.088 -3.236 4.588 3.388 1.904
## [211] -4.656 -0.392 -3.704 1.184 -3.968 2.444 -5.604 -3.476 1.128 -2.056
## [221] 1.788 -1.912 -2.888 5.296 -2.648 -3.272 0.984 -0.120 -2.096 -5.760
## [231] -2.636 12.572 -5.580 0.740 -1.004 -5.748 0.360 -0.296 -1.492 1.844
## [241] 2.268 0.772 -3.872 -3.860 -0.596 0.832 0.408 1.944 2.436 -0.376
## [251] -3.368 1.400 5.032 3.144 5.596 -5.676 1.548 6.328 -1.544 1.412
## [261] 6.332 1.852 -1.532 -1.708 -2.372 1.988 3.216 4.672 -2.972 5.860
## [271] 3.012 5.440 2.336 1.436 -0.428 -5.892 -0.956 -2.804 -2.564 0.104
## [281] 2.284 -2.648 -1.084 1.260 3.084 -5.292 -1.504 -0.836 2.084 -5.184
## [291] 12.704 1.292 4.960 1.940 4.540 4.972 -0.620 6.280 2.192 0.908
## [301] -1.916 -6.012 -1.424 -3.296 1.928 5.824 0.684 -2.672 5.072 -2.588
## [311] 2.116 0.120 -5.700 -2.032 -2.176 4.280 0.240 1.436 2.268 3.168
## [321] -2.632 -2.360 1.856 1.664 -0.680 4.188 0.888 -0.008 -0.404 2.616
## [331] -0.252 3.420 -5.220 -2.624 -5.136 -0.932 -3.092 -5.820 -4.340 0.752
## [341] 0.256 -6.816 0.840 -1.564 -1.424 6.812 -3.212 -1.876 3.540 1.436
## [351] -5.484 -4.620 0.552 1.448 1.284 4.948 2.540 6.020 2.048 -1.008
## [361] 4.360 7.180 2.216 -4.704 1.868 5.188 1.092 1.772 -1.100 -4.572p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.802183889 0.004888351 0.839161173 0.996756974 0.004830323 0.688260608
## [7] 0.037398821 0.409024380 0.032546422 0.993199936 0.004716315 0.659260388
## [13] 0.829204518 0.989481027 0.854706214 0.843960959 0.106405008 0.988014503
## [19] 0.205543589 0.879954567 0.040543170 0.079878426 0.005251376 0.959612142
## [25] 0.769944881 0.807834464 0.002923634 0.005189061 0.476018415 0.341638728
## [31] 0.933391964 0.300693007 0.004135512 0.821713502 0.782449776 0.993744382
## [37] 0.005006496 0.700986520 0.459091648 0.766383175 0.049266006 0.257309455
## [43] 0.018560221 0.394604014 0.247498215 0.970459286 0.923012521 0.999997722
## [49] 0.848643344 0.001459793 0.807834464 0.658361272 0.669959082 0.850687065
## [55] 0.036685442 0.766383175 0.999614275 0.994469878 0.997595486 0.899889734
## [61] 0.003849328 0.050402377 0.813361186 0.205543589 0.055829730 0.541901494
## [67] 0.074744105 0.336261303 0.999827338 0.631812418 0.703495691 0.743454208
## [73] 0.002246798 0.128308283 0.346151304 0.994578788 0.886149552 0.347057182
## [79] 0.008162571 0.981146071 0.903784458 0.994381186 0.710126808 0.016889988
## [85] 0.996388449 0.993964243 0.944380747 0.899528804 0.557741243 0.994176423
## [91] 0.796409033 0.010477421 0.002625120 0.183022868 0.999769724 0.955383113
## [97] 0.767812442 0.161379439 0.729482779 0.709302730 0.816977132 0.110661690
## [103] 0.805964724 0.960984527 0.157625862 0.993035911 0.532952167 0.999793293
## [109] 0.920121574 0.004235540 0.142827693 0.864830034 0.003103860 0.001922892
## [115] 0.042942560 0.661950848 0.092121684 0.044959142 0.925532055 0.935354070
## [121] 0.532952167 0.366328549 0.003179014 0.005777442 0.001789550 0.915907655
## [127] 0.003803587 0.450166003 0.254263726 0.685680114 0.258841231 0.765666252
## [133] 0.006746246 0.040078942 0.109486208 0.998443876 0.931118570 0.705992638
## [139] 0.994381186 0.878254114 0.261149994 0.110661690 0.942459289 0.890513792
## [145] 0.076703718 0.186638814 0.256545792 0.800272763 0.875664558 0.856190082
## [151] 0.456113228 0.936790200 0.843960959 0.642906121 0.060426403 0.993280504
## [157] 0.122174229 0.999468885 0.986019326 0.994953495 0.993669340 0.053962135
## [163] 0.993891823 0.898439072 0.845534735 0.036544345 0.277679776 0.991673907
## [169] 0.005066629 0.751008835 0.397474310 0.995131069 0.123467048 0.061111309
## [175] 0.050402377 0.981584945 0.871691717 0.041966945 0.009527498 0.353429091
## [181] 0.002141724 0.006355872 0.325633767 0.003295160 0.888151792 0.006827132
## [187] 0.837534937 0.980544915 0.190309559 0.790509619 0.825778630 0.277679776
## [193] 0.444232986 0.003849328 0.944799462 0.793802460 0.011520954 0.057108406
## [199] 0.002357014 0.954696102 0.937731034 0.993199936 0.429473397 0.882051255
## [205] 0.216191524 0.748004922 0.037833229 0.989929267 0.967327394 0.870343577
## [211] 0.009414921 0.403235934 0.024033021 0.765666252 0.018560221 0.920121574
## [217] 0.003669586 0.030002872 0.755469617 0.113447517 0.856681896 0.128756329
## [223] 0.052749964 0.995013391 0.066112385 0.036544345 0.727901182 0.470035948
## [229] 0.109486208 0.003141213 0.066857153 0.999996532 0.003758387 0.676995856
## [235] 0.268155701 0.003179014 0.589040434 0.426535606 0.183621728 0.863421094
## [241] 0.906191910 0.683953375 0.020392196 0.020633297 0.355259363 0.696777653
## [247] 0.600608220 0.874790928 0.919531611 0.407092010 0.033310651 0.802183889
## [253] 0.993516563 0.958671653 0.996301048 0.003415534 0.824624682 0.998217581
## [259] 0.175954545 0.804081205 0.998224684 0.864361755 0.177701250 0.153423305
## [265] 0.085332908 0.879531386 0.961431966 0.990733134 0.048706970 0.997156863
## [271] 0.953113312 0.995679266 0.911814981 0.807834464 0.394604014 0.002753844
## [277] 0.277679776 0.057108406 0.071491565 0.525976591 0.907543229 0.066112385
## [283] 0.252749800 0.779026108 0.956227915 0.005006496 0.181829696 0.302377898
## [289] 0.889338310 0.005574292 0.999996961 0.784485518 0.993035911 0.874352143
## [295] 0.989439312 0.993118409 0.349781451 0.998130102 0.899528804 0.712590727
## [301] 0.128308283 0.002443201 0.194035276 0.035708668 0.873027885 0.997052952
## [307] 0.664630870 0.064645930 0.993769198 0.069914724 0.892448595 0.529964052
## [313] 0.003334807 0.115883855 0.101926495 0.986346341 0.559713649 0.807834464
## [319] 0.906191910 0.959612142 0.067107135 0.086274194 0.864830034 0.840774225
## [325] 0.336261303 0.985050278 0.708477270 0.498000011 0.400351677 0.931884241
## [331] 0.437331292 0.968323772 0.005378247 0.067609703 0.005846781 0.282519134
## [337] 0.043438456 0.002958825 0.012868764 0.679614333 0.563652750 0.001094896
## [343] 0.698465216 0.173073422 0.194035276 0.998900720 0.038716630 0.132848998
## [349] 0.971804712 0.807834464 0.004135512 0.009756666 0.634599482 0.809690441
## [355] 0.783129896 0.992952431 0.926898827 0.997576219 0.885745374 0.267371436
## [361] 0.987382839 0.999238912 0.901677143 0.008977640 0.866226691 0.994447837
## [367] 0.748758148 0.854706214 0.249739894 0.010231499set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 0 1 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 0 1 1 0
## [38] 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
## [75] 0 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 0
## [112] 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 0 0 1
## [149] 1 1 0 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1
## [186] 0 1 1 0 1 1 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 1 1
## [223] 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0
## [260] 1 1 1 0 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1
## [297] 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 0
## [334] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 0 1 1 2.75
## 2 0 0 0 0 3.07
## 3 1 0 1 1 2.96
## 4 1 1 1 1 3.44
## 5 0 0 0 0 3.06
## 6 0 1 1 0 3.16
## 7 0 0 1 0 2.71
## 8 1 0 0 1 3.36
## 9 0 0 1 0 2.59
## 10 1 1 1 1 2.82
## 11 0 0 0 0 3.04
## 12 1 1 1 0 3.05
## 13 1 0 1 1 2.90
## 14 1 2 1 0 3.37
## 15 1 0 1 1 3.06
## 16 0 0 1 1 2.99
## 17 1 1 0 0 2.81
## 18 1 2 1 0 3.26
## 19 0 0 0 1 2.54
## 20 1 1 1 0 4.16
## 21 0 0 1 0 2.78
## 22 0 0 1 0 3.38
## 23 0 0 0 0 3.13
## 24 1 1 0 1 3.39
## 25 1 0 1 1 2.59
## 26 1 0 1 1 2.78
## 27 0 0 0 0 2.64
## 28 0 0 0 0 3.12
## 29 1 1 1 0 2.42
## 30 0 0 0 1 3.12
## 31 1 1 0 1 2.95
## 32 0 1 0 0 3.88
## 33 0 0 0 0 2.93
## 34 0 2 0 0 2.94
## 35 1 0 1 1 2.65
## 36 1 1 1 1 2.89
## 37 0 0 0 0 3.09
## 38 1 1 1 0 3.21
## 39 1 0 0 1 3.53
## 40 1 1 1 0 3.49
## 41 1 0 1 0 2.95
## 42 0 1 0 0 3.70
## 43 0 0 1 0 2.11
## 44 0 0 0 1 3.31
## 45 1 0 0 1 2.74
## 46 1 1 0 1 3.66
## 47 0 1 0 1 2.82
## 48 1 3 1 1 3.66
## 49 1 0 1 1 3.02
## 50 0 0 0 0 2.06
## 51 1 0 1 1 2.78
## 52 1 0 1 1 2.13
## 53 0 1 1 0 3.09
## 54 0 1 1 0 3.95
## 55 0 1 0 0 1.86
## 56 0 1 1 0 3.49
## 57 1 2 1 1 2.30
## 58 1 2 1 0 3.91
## 59 1 1 1 1 3.69
## 60 1 1 0 1 2.58
## 61 0 0 0 0 2.87
## 62 0 0 1 0 2.97
## 63 1 0 1 1 2.81
## 64 0 0 1 0 4.29
## 65 0 0 1 0 3.06
## 66 1 1 1 0 2.64
## 67 0 0 1 0 3.32
## 68 0 0 0 1 3.10
## 69 1 2 1 1 2.97
## 70 1 1 1 0 2.95
## 71 1 1 1 0 3.22
## 72 1 0 1 1 2.47
## 73 0 0 0 0 2.42
## 74 0 0 1 0 3.82
## 75 0 1 1 0 1.97
## 76 1 1 1 1 3.01
## 77 1 1 0 1 2.46
## 78 1 0 0 1 3.14
## 79 0 0 0 0 3.50
## 80 1 1 1 1 1.96
## 81 1 0 1 1 3.45
## 82 1 1 1 1 2.98
## 83 1 0 1 1 2.33
## 84 0 0 1 0 2.03
## 85 1 1 1 1 3.35
## 86 1 1 1 1 2.92
## 87 0 1 0 1 3.11
## 88 1 0 1 1 3.41
## 89 0 0 0 1 3.86
## 90 1 1 1 1 2.95
## 91 0 0 1 1 2.72
## 92 0 0 0 0 3.71
## 93 0 0 0 0 2.55
## 94 0 0 0 1 2.42
## 95 1 2 1 1 2.73
## 96 1 2 0 0 4.22
## 97 1 0 1 1 2.58
## 98 0 1 0 0 3.21
## 99 1 0 1 1 2.41
## 100 1 2 0 0 2.41
## 101 1 0 1 1 2.83
## 102 0 0 1 0 3.68
## 103 0 0 1 1 2.77
## 104 1 1 0 1 3.42
## 105 0 0 0 1 2.27
## 106 1 1 1 1 2.80
## 107 0 1 1 0 2.61
## 108 1 2 1 1 2.82
## 109 1 0 1 1 3.62
## 110 0 0 0 0 2.95
## 111 0 1 0 0 3.09
## 112 1 0 1 1 3.13
## 113 0 0 0 0 2.69
## 114 0 0 0 0 2.29
## 115 0 0 1 0 2.83
## 116 0 1 1 0 3.06
## 117 0 0 1 0 3.51
## 118 0 0 1 0 2.87
## 119 0 1 0 1 2.85
## 120 1 0 1 1 3.81
## 121 1 1 1 0 2.61
## 122 0 0 0 1 3.21
## 123 0 0 0 0 2.71
## 124 0 0 0 0 3.21
## 125 0 0 0 0 2.23
## 126 1 1 0 1 2.74
## 127 0 0 0 0 2.86
## 128 0 0 0 1 3.50
## 129 0 0 0 1 2.77
## 130 1 1 1 0 3.15
## 131 0 0 0 1 2.79
## 132 0 0 1 1 2.57
## 133 0 0 0 0 3.34
## 134 1 0 1 0 2.77
## 135 0 0 1 0 3.67
## 136 1 2 0 1 3.22
## 137 1 1 0 1 2.92
## 138 0 1 1 0 3.23
## 139 1 1 1 1 2.98
## 140 1 0 1 1 3.23
## 141 0 0 0 1 2.80
## 142 0 0 0 1 1.93
## 143 1 1 0 1 3.08
## 144 1 0 1 1 3.33
## 145 0 1 0 0 2.51
## 146 0 0 0 1 2.44
## 147 0 0 0 1 2.78
## 148 1 0 1 1 2.74
## 149 1 0 1 1 3.21
## 150 1 0 1 1 3.07
## 151 0 0 0 1 3.52
## 152 1 0 1 1 3.83
## 153 0 0 1 1 2.99
## 154 1 1 1 0 2.99
## 155 0 0 1 0 3.13
## 156 1 1 1 1 2.83
## 157 0 1 0 0 2.94
## 158 1 3 1 0 2.95
## 159 1 2 1 0 3.13
## 160 1 1 1 1 3.07
## 161 1 1 1 1 2.88
## 162 0 0 1 0 3.03
## 163 1 1 1 1 2.91
## 164 1 0 1 1 3.40
## 165 1 0 1 1 3.00
## 166 0 0 1 0 2.69
## 167 0 0 0 1 2.87
## 168 1 1 1 1 2.65
## 169 0 0 0 0 3.10
## 170 1 1 1 0 3.42
## 171 1 0 0 1 3.32
## 172 1 1 1 1 3.10
## 173 0 1 0 0 2.95
## 174 0 0 1 0 3.14
## 175 0 0 1 0 2.97
## 176 1 1 1 1 1.98
## 177 1 0 1 1 3.18
## 178 0 0 1 0 2.81
## 179 0 0 0 0 3.63
## 180 0 1 0 0 4.08
## 181 0 0 0 0 2.38
## 182 0 0 0 0 3.29
## 183 1 0 0 1 3.06
## 184 0 0 0 0 2.74
## 185 1 0 1 1 3.31
## 186 0 0 0 0 3.35
## 187 1 0 1 1 2.95
## 188 1 2 1 0 2.85
## 189 0 0 0 1 2.46
## 190 1 0 1 1 2.69
## 191 1 0 1 1 2.88
## 192 0 0 0 1 2.87
## 193 1 0 0 1 3.48
## 194 0 0 0 0 2.87
## 195 1 0 1 1 3.95
## 196 1 2 0 0 2.79
## 197 0 0 0 0 3.79
## 198 0 0 1 0 3.08
## 199 0 0 0 0 2.46
## 200 1 1 0 1 3.29
## 201 1 1 0 1 3.01
## 202 1 1 1 1 2.82
## 203 1 0 0 1 3.43
## 204 0 0 1 1 3.26
## 205 0 1 0 0 3.51
## 206 0 0 1 1 2.49
## 207 0 0 1 0 2.72
## 208 1 1 1 1 2.49
## 209 1 1 1 1 1.49
## 210 1 0 1 1 3.17
## 211 0 0 0 0 3.62
## 212 0 0 0 1 3.34
## 213 0 0 1 0 2.33
## 214 1 0 1 1 2.57
## 215 0 0 1 0 2.11
## 216 1 0 1 1 3.62
## 217 0 0 0 0 2.83
## 218 0 0 1 0 2.52
## 219 1 1 1 0 3.44
## 220 0 1 0 0 2.87
## 221 1 1 0 1 2.24
## 222 1 1 0 0 2.99
## 223 0 0 1 0 3.01
## 224 1 1 1 1 3.08
## 225 0 0 1 0 3.21
## 226 0 0 1 0 2.69
## 227 0 1 1 0 3.32
## 228 0 1 1 0 2.40
## 229 0 0 1 0 3.67
## 230 0 0 0 0 2.70
## 231 0 0 1 0 3.22
## 232 1 3 1 1 3.31
## 233 0 0 0 0 2.85
## 234 1 0 1 1 2.20
## 235 1 0 0 1 2.83
## 236 0 0 0 0 2.71
## 237 1 1 1 0 2.80
## 238 0 0 0 1 3.42
## 239 0 1 0 0 3.34
## 240 1 0 1 1 3.12
## 241 1 1 0 1 2.64
## 242 1 0 0 1 4.31
## 243 0 0 1 0 2.19
## 244 0 0 1 0 2.20
## 245 1 0 0 1 3.17
## 246 0 0 0 1 4.36
## 247 1 1 1 0 2.84
## 248 1 1 0 1 2.37
## 249 1 1 0 1 2.78
## 250 0 1 0 0 4.27
## 251 0 0 1 0 2.61
## 252 1 0 1 1 2.75
## 253 1 1 1 1 2.86
## 254 1 1 0 1 3.37
## 255 1 1 1 1 3.33
## 256 0 0 0 0 2.77
## 257 1 1 0 1 2.04
## 258 1 1 1 1 3.94
## 259 0 0 0 1 2.38
## 260 1 0 1 1 2.76
## 261 1 2 0 1 3.11
## 262 1 2 0 0 3.21
## 263 0 0 0 1 2.39
## 264 0 1 0 0 3.16
## 265 0 0 1 0 3.44
## 266 1 0 1 1 3.24
## 267 1 1 0 1 3.43
## 268 1 1 1 1 2.56
## 269 0 0 1 0 2.94
## 270 1 1 1 1 3.55
## 271 1 1 0 1 3.26
## 272 1 1 1 1 3.20
## 273 1 0 1 1 3.53
## 274 1 0 1 1 2.78
## 275 1 0 0 1 3.31
## 276 0 0 0 0 2.59
## 277 1 0 0 1 2.87
## 278 0 0 1 0 3.08
## 279 0 0 1 0 3.28
## 280 1 0 1 1 1.67
## 281 1 2 0 0 3.57
## 282 1 0 1 0 3.21
## 283 0 1 0 0 3.68
## 284 1 1 1 0 3.55
## 285 1 1 0 1 3.32
## 286 0 0 0 0 3.09
## 287 0 1 0 0 3.33
## 288 0 0 0 1 2.97
## 289 1 0 1 1 3.32
## 290 0 0 0 0 3.18
## 291 1 3 1 1 3.42
## 292 1 0 1 1 2.66
## 293 1 1 1 1 2.80
## 294 1 0 1 1 3.20
## 295 1 1 1 1 2.45
## 296 1 1 1 1 2.81
## 297 0 0 0 1 3.15
## 298 1 1 1 1 3.90
## 299 1 0 1 1 3.41
## 300 1 0 1 1 2.34
## 301 0 0 0 1 2.07
## 302 0 0 0 0 2.49
## 303 0 0 0 1 2.48
## 304 0 0 1 0 2.67
## 305 1 0 1 1 3.19
## 306 1 1 1 1 3.52
## 307 1 1 1 0 3.07
## 308 0 0 1 0 3.19
## 309 1 2 0 1 2.06
## 310 0 0 1 0 3.26
## 311 1 2 0 0 3.43
## 312 1 1 1 0 2.60
## 313 0 0 0 0 2.75
## 314 0 1 0 0 2.89
## 315 0 1 0 0 2.77
## 316 1 2 1 0 3.15
## 317 0 1 1 0 2.70
## 318 1 0 1 1 2.78
## 319 1 1 0 1 2.64
## 320 1 1 0 1 3.39
## 321 0 1 0 0 2.39
## 322 0 0 1 0 3.45
## 323 1 0 1 1 3.13
## 324 1 0 1 1 2.97
## 325 0 0 0 1 3.10
## 326 1 1 0 1 4.24
## 327 1 1 1 0 3.24
## 328 0 0 0 1 3.66
## 329 0 0 0 1 3.33
## 330 1 1 0 1 2.93
## 331 0 1 1 0 2.29
## 332 1 1 0 1 3.60
## 333 0 0 0 0 3.15
## 334 0 0 1 0 3.23
## 335 0 0 0 0 3.22
## 336 0 0 0 1 2.89
## 337 0 0 1 0 2.84
## 338 0 0 0 0 2.65
## 339 0 0 1 0 1.80
## 340 0 0 1 1 2.21
## 341 0 0 0 1 3.88
## 342 0 0 0 0 1.82
## 343 0 1 1 0 3.20
## 344 0 1 0 0 3.28
## 345 0 0 0 1 2.48
## 346 1 2 0 1 3.51
## 347 0 0 1 0 2.74
## 348 1 1 0 0 3.02
## 349 1 1 0 1 3.70
## 350 1 0 1 1 2.78
## 351 0 0 0 0 2.93
## 352 0 0 0 0 3.65
## 353 1 1 1 0 2.96
## 354 0 0 1 1 2.79
## 355 1 1 1 0 3.57
## 356 1 1 1 1 2.79
## 357 1 0 1 1 3.70
## 358 1 2 0 1 2.85
## 359 1 0 1 1 3.29
## 360 0 1 1 0 1.66
## 361 1 1 1 1 2.30
## 362 1 1 1 1 4.65
## 363 1 0 1 1 3.43
## 364 0 0 0 0 3.58
## 365 1 0 1 1 3.14
## 366 1 1 1 1 2.99
## 367 1 1 0 1 1.66
## 368 1 0 1 1 3.06
## 369 0 0 0 1 2.75
## 370 0 0 0 0 3.69modelreglog <- 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) -8.4080 1.3635 -6.167 6.98e-10 ***
## x1 3.5236 0.4443 7.930 2.19e-15 ***
## x2 2.9228 0.4008 7.293 3.04e-13 ***
## x3 4.8412 0.5464 8.860 < 2e-16 ***
## x4 0.8480 0.3503 2.421 0.0155 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 512.54 on 369 degrees of freedom
## Residual deviance: 218.68 on 365 degrees of freedom
## AIC: 228.68
##
## Number of Fisher Scoring iterations: 6Output yang diberikan adalah hasil dari pemodelan regresi logistik (logit) dengan menggunakan paket statistik dalam bahasa R. Berikut adalah kesimpulan dari output tersebut:
Kesimpulan dari output tersebut adalah bahwa model regresi logistik yang dihasilkan cukup signifikan secara statistik, dengan variabel x1, x2, x3 memiliki pengaruh yang signifikan terhadap variabel dependen y. Variabel x4 juga memiliki pengaruh yang signifikan, meskipun pada tingkat signifikansi yang lebih rendah (α = 0.05).