Y : Keputusan Kenaikan Gaji Karyawan PT ADE X1 : Lama bekerja (Tahun.Hari) X2 : Persetujuan dari atasan (0 = Disetujui ; 1 = Tidak disetujui) X3 : Besaran Kenaikan Gaji + THR (Rupiah)
X1 : Lama bekerja (Tahun.Hari)
set.seed(1)
n <- 100
x1 <- round(rnorm(n,3,0.5),2)
x1## [1] 2.69 3.09 2.58 3.80 3.16 2.59 3.24 3.37 3.29 2.85 3.76 3.19 2.69 1.89 3.56
## [16] 2.98 2.99 3.47 3.41 3.30 3.46 3.39 3.04 2.01 3.31 2.97 2.92 2.26 2.76 3.21
## [31] 3.68 2.95 3.19 2.97 2.31 2.79 2.80 2.97 3.55 3.38 2.92 2.87 3.35 3.28 2.66
## [46] 2.65 3.18 3.38 2.94 3.44 3.20 2.69 3.17 2.44 3.72 3.99 2.82 2.48 3.28 2.93
## [61] 4.20 2.98 3.34 3.01 2.63 3.09 2.10 3.73 3.08 4.09 3.24 2.65 3.31 2.53 2.37
## [76] 3.15 2.78 3.00 3.04 2.71 2.72 2.93 3.59 2.24 3.30 3.17 3.53 2.85 3.19 3.13
## [91] 2.73 3.60 3.58 3.35 3.79 3.28 2.36 2.71 2.39 2.76X2: Persetujuan dari atasan Keterangan yang digunakan (0 = Disetujui ; 1 = Tidak disetujui)
set.seed(2)
x2 <- round(runif(n))
x2## [1] 0 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1
## [38] 0 1 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0
## [75] 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0X3 : Besaran Kenaikan Gaji + THR (Rupiah) Keterangan yang digunakan (Juta)
set.seed(3)
x3 <- runif(100, 10, 25)
x3## [1] 12.52062 22.11275 15.77414 14.91601 19.03151 19.06591 11.86950 14.41901
## [9] 18.66415 19.46469 17.68024 17.57536 18.01053 18.35874 23.01879 22.44563
## [17] 11.67174 20.55533 23.46232 14.19599 13.42303 10.22995 11.93472 11.40073
## [25] 13.55328 21.86721 18.99597 23.65222 18.40637 21.33557 15.68758 15.59921
## [33] 12.55436 16.79961 13.87621 15.04399 23.34375 13.02919 18.68779 13.11448
## [41] 14.22203 21.79422 12.59529 18.56121 16.28924 14.01433 10.71714 11.55240
## [49] 14.71047 22.00962 13.43987 13.19498 23.15651 24.89833 22.66371 23.65655
## [57] 17.06905 13.36628 11.91722 14.19525 22.24159 10.86419 22.04244 11.56582
## [65] 21.49909 14.57216 21.53931 18.10984 15.43556 11.38835 21.39633 21.41230
## [73] 23.54891 24.49424 17.72885 18.24221 12.45580 12.46895 21.79499 21.26670
## [81] 21.76322 19.81532 15.67157 10.12850 24.32994 22.57924 13.20137 17.42070
## [89] 19.54366 23.81637 10.17616 14.01104 16.53358 22.44201 23.06416 13.76603
## [97] 14.86537 14.59357 12.76423 20.19966b0 <- -10
b1 <- 3.5
b2 <- 2.5
b3 <- 1.5
set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)
datapendukung## [1] 18.19593 36.48412 25.19120 25.67402 32.10727 30.16387 19.14425 25.92352
## [9] 29.51122 31.67203 32.18036 27.52804 28.93080 24.15311 36.98819 36.59845
## [17] 20.47261 32.97799 37.12849 22.84398 24.74454 17.20992 21.04209 14.13609
## [25] 21.91491 33.19582 28.71396 33.38832 29.76955 33.23836 26.41137 23.72382
## [33] 22.49654 28.09441 21.39931 24.83098 37.31562 19.93879 32.95669 21.50172
## [41] 24.05305 32.73633 20.61794 29.32182 26.24387 22.79650 19.70571 19.15859
## [49] 24.85571 37.55442 21.35981 19.20746 38.32977 38.38749 37.01556 41.94982
## [57] 27.97357 21.22941 21.85583 24.04788 40.56239 19.22629 37.25366 17.88373
## [65] 33.95364 22.67324 29.65897 30.21975 23.93334 21.39753 33.43449 31.39345
## [73] 36.90837 35.59636 24.88827 30.88832 18.41370 21.70343 33.33249 33.88505
## [81] 32.16483 32.47799 26.07235 13.03276 38.04491 37.46386 24.65706 26.10606
## [89] 32.98050 36.67956 17.31924 23.61657 27.33037 37.88801 37.86124 22.12905
## [97] 20.55806 21.37535 17.51135 29.95949p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [22] 1.0000000 1.0000000 0.9999993 1.0000000 1.0000000 1.0000000 1.0000000
## [29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [50] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [57] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [64] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [71] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [78] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9999978
## [85] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [92] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## [99] 1.0000000 1.0000000set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1datagab <- data.frame(y,x1,x2,x3)
datagab## y x1 x2 x3
## 1 1 2.69 0 12.52062
## 2 1 3.09 1 22.11275
## 3 1 2.58 1 15.77414
## 4 1 3.80 0 14.91601
## 5 1 3.16 1 19.03151
## 6 1 2.59 1 19.06591
## 7 1 3.24 0 11.86950
## 8 1 3.37 1 14.41901
## 9 1 3.29 0 18.66415
## 10 1 2.85 1 19.46469
## 11 1 3.76 1 17.68024
## 12 1 3.19 0 17.57536
## 13 1 2.69 1 18.01053
## 14 1 1.89 0 18.35874
## 15 1 3.56 0 23.01879
## 16 1 2.98 1 22.44563
## 17 1 2.99 1 11.67174
## 18 1 3.47 0 20.55533
## 19 1 3.41 0 23.46232
## 20 1 3.30 0 14.19599
## 21 1 3.46 1 13.42303
## 22 1 3.39 0 10.22995
## 23 1 3.04 1 11.93472
## 24 1 2.01 0 11.40073
## 25 1 3.31 0 13.55328
## 26 1 2.97 0 21.86721
## 27 1 2.92 0 18.99597
## 28 1 2.26 0 23.65222
## 29 1 2.76 1 18.40637
## 30 1 3.21 0 21.33557
## 31 1 3.68 0 15.68758
## 32 1 2.95 0 15.59921
## 33 1 3.19 1 12.55436
## 34 1 2.97 1 16.79961
## 35 1 2.31 1 13.87621
## 36 1 2.79 1 15.04399
## 37 1 2.80 1 23.34375
## 38 1 2.97 0 13.02919
## 39 1 3.55 1 18.68779
## 40 1 3.38 0 13.11448
## 41 1 2.92 1 14.22203
## 42 1 2.87 0 21.79422
## 43 1 3.35 0 12.59529
## 44 1 3.28 0 18.56121
## 45 1 2.66 1 16.28924
## 46 1 2.65 1 14.01433
## 47 1 3.18 1 10.71714
## 48 1 3.38 0 11.55240
## 49 1 2.94 1 14.71047
## 50 1 3.44 1 22.00962
## 51 1 3.20 0 13.43987
## 52 1 2.69 0 13.19498
## 53 1 3.17 1 23.15651
## 54 1 2.44 1 24.89833
## 55 1 3.72 0 22.66371
## 56 1 3.99 1 23.65655
## 57 1 2.82 1 17.06905
## 58 1 2.48 1 13.36628
## 59 1 3.28 1 11.91722
## 60 1 2.93 1 14.19525
## 61 1 4.20 1 22.24159
## 62 1 2.98 1 10.86419
## 63 1 3.34 1 22.04244
## 64 1 3.01 0 11.56582
## 65 1 2.63 1 21.49909
## 66 1 3.09 0 14.57216
## 67 1 2.10 0 21.53931
## 68 1 3.73 0 18.10984
## 69 1 3.08 0 15.43556
## 70 1 4.09 0 11.38835
## 71 1 3.24 0 21.39633
## 72 1 2.65 0 21.41230
## 73 1 3.31 0 23.54891
## 74 1 2.53 0 24.49424
## 75 1 2.37 0 17.72885
## 76 1 3.15 1 18.24221
## 77 1 2.78 0 12.45580
## 78 1 3.00 1 12.46895
## 79 1 3.04 0 21.79499
## 80 1 2.71 1 21.26670
## 81 1 2.72 0 21.76322
## 82 1 2.93 1 19.81532
## 83 1 3.59 0 15.67157
## 84 1 2.24 0 10.12850
## 85 1 3.30 0 24.32994
## 86 1 3.17 1 22.57924
## 87 1 3.53 1 13.20137
## 88 1 2.85 0 17.42070
## 89 1 3.19 1 19.54366
## 90 1 3.13 0 23.81637
## 91 1 2.73 1 10.17616
## 92 1 3.60 0 14.01104
## 93 1 3.58 0 16.53358
## 94 1 3.35 1 22.44201
## 95 1 3.79 0 23.06416
## 96 1 3.28 0 13.76603
## 97 1 2.36 0 14.86537
## 98 1 2.71 0 14.59357
## 99 1 2.39 0 12.76423
## 100 1 2.76 0 20.19966kesimpulan <- glm(y~x1+x2+x3, family = binomial(link = "logit"),data = datagab)
summary(kesimpulan)##
## Call:
## glm(formula = y ~ x1 + x2 + x3, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.657e+01 2.795e+05 0 1
## x1 6.474e-10 7.996e+04 0 1
## x2 1.924e-09 7.163e+04 0 1
## x3 2.833e-10 8.355e+03 0 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 0.0000e+00 on 99 degrees of freedom
## Residual deviance: 5.8016e-10 on 96 degrees of freedom
## AIC: 8
##
## Number of Fisher Scoring iterations: 25=> Beberapa karyawan disetujui oleh atasan dan ada beberapa karyawan yang tidak disetujui oleh atasan untuk mendapatkan kenaikan gaji + THR tetapi dikarenakan terdapat faktor yang mendukung sehingga seluruh karyawan mendapatkan kenaikan gaji + THR