Y: Keputusan yang bernilai 1 untuk membeli dan 0 untuk tidak membeli X1: usia X2: pendapatan bulanan X3: Pekerjaan yang terdiri 8 kategori X4: Tingkat Pendidikan bernilai 1 untuk lulus kuliah dan 0 untuk lulus SMA/SMK tidak lulus kuliah
X1 :membangkitkan 100 angka acak uniform rentang 18-55(usia) nilai tengah 20 dan banyak pembeli adalah 100
set.seed(100)
n <-100
u <-runif(n)
X1 <- round(100*(-log(1-u)/20))
X1## [1] 2 1 4 0 3 3 8 2 4 1 5 11 2 3 7 6 1 2 2 6 4 6 4 7 3
## [26] 1 7 11 4 2 3 13 2 15 6 11 1 5 23 1 2 10 8 9 5 3 8 11 1 2
## [51] 2 1 1 2 4 1 1 1 5 1 3 5 16 6 3 2 3 3 1 6 3 2 4 17 5
## [76] 5 10 7 9 0 3 5 13 20 0 4 7 1 2 7 12 1 2 3 12 2 4 1 0 7X2:membangkitkan 100 angka acak normal rataan 3000 dan varians 1000 (pendapatan)
X2 <- round(rnorm(n,100,3000),1000)
X2## [1] -1241.18655 -5115.79384 636.59455 5792.39710 -6715.77646 3041.39242
## [7] -4096.47685 5574.61727 4243.89619 -2416.55563 -685.98732 -106.53208
## [13] -1036.65067 7845.87678 489.50241 -2039.07494 2013.98273 705.07477
## [19] -109.75084 -177.46963 1446.70982 -3093.06701 -3387.25797 5045.56524
## [25] -6086.28806 138.24916 -3162.58505 911.61848 3125.35562 -6123.21426
## [31] 2790.46682 -49.98730 -3936.04793 -5693.63460 2228.74475 -373.71510
## [37] 749.10362 2552.08623 5281.52726 -211.31088 -1571.36687 4384.90429
## [43] -2578.87221 -3372.71372 -1490.88936 7437.04827 -2397.48739 1340.55955
## [49] -3436.04942 -3422.10428 -898.77005 4189.34112 -1307.44202 2628.62690
## [55] -4273.98117 -1100.91776 -2229.25186 -1007.88953 3820.30438 -222.30143
## [61] 617.78052 863.80380 -1743.60149 -4187.64529 -892.92630 485.15819
## [67] 3154.35998 -666.72107 -807.62303 4945.57205 -2221.14006 1372.00720
## [73] -1651.84094 1345.10704 -4535.78497 -1456.24851 -739.37466 3122.37215
## [79] -1308.70986 993.69112 -1153.38330 -2451.14233 2167.13858 -1280.58858
## [85] 4144.55313 1429.21415 -352.77857 1466.64657 -20.46404 1468.36313
## [91] -1125.27509 -6309.48157 570.46575 2080.14670 -2845.50324 -3240.93111
## [97] -1212.04303 -1448.33374 1356.98797 502.46631X3: membangkitkan angka acak 1 sampai 8 sebanyak 100 (kategori pekerjaan)
X3 = sample(c(1:8),size=100,replace=TRUE)
X3## [1] 7 2 6 3 3 4 8 2 4 4 7 1 3 1 3 8 2 8 7 4 8 7 7 1 6 3 7 3 1 2 1 2 5 2 6 8 4
## [38] 7 4 1 1 7 7 5 7 3 1 7 3 4 5 5 5 5 8 2 5 8 6 6 8 5 5 2 3 5 5 7 2 7 7 1 7 8
## [75] 1 7 8 3 4 4 3 5 7 4 7 3 8 8 2 1 7 2 4 1 8 3 5 3 8 7X4: Tingkat pendidikan keterangan yang digunakan (0= lulus SMA/Tidak kuliah) dan (1= lulus kuliah)
set.seed(111)
X4 <- round(runif(n))
X4## [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 1menentukan koefisien
b0 <- 11
b1 <- 3.5
b2 <- 0.05
b3 <- 1.26
b4 <- 3.2set.seed(1)
datapendukung <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datapendukung## [1] -32.039327 -235.569692 64.389727 307.599855 -310.508823 178.609621
## [7] -155.743842 302.450863 242.234809 -101.287781 6.220634 48.633396
## [13] -30.052533 415.053839 63.755121 -59.873747 117.719136 66.533739
## [19] 21.332458 31.366519 107.415491 -113.833351 -135.542898 289.038262
## [25] -272.054403 25.192458 -110.609252 98.860924 185.727781 -282.440713
## [31] 162.283341 59.720635 -172.502397 -218.661730 150.997238 44.094245
## [37] 56.995181 168.124311 363.816363 8.394456 -56.108344 274.065214
## [43] -77.923610 -116.635686 -34.024468 397.132414 -79.614370 128.547977
## [49] -150.322471 -144.865214 -17.438503 233.467056 -44.572101 155.731345
## [55] -175.419058 -34.825888 -87.462593 -25.814477 227.075219 14.144929
## [61] 62.469026 77.990190 -13.880074 -174.862264 -16.166315 48.557910
## [67] 185.517999 -3.016054 -23.361152 288.098602 -77.537003 91.060360
## [73] -48.772047 147.835352 -193.829248 -35.492426 19.111267 198.598607
## [79] -17.895493 68.924556 -32.389165 -84.557116 173.676929 22.010571
## [85] 227.047657 100.240708 31.141072 101.112329 22.696798 113.378157
## [91] 8.756246 -295.254078 54.763287 129.967335 -75.995162 -140.266556
## [97] -26.102152 -54.136687 92.129399 72.643316p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 1.217578e-14 4.936085e-103 1.000000e+00 1.000000e+00 1.405179e-135
## [6] 1.000000e+00 2.297781e-68 1.000000e+00 1.000000e+00 1.026303e-44
## [11] 9.980160e-01 1.000000e+00 8.878724e-14 1.000000e+00 1.000000e+00
## [16] 9.934867e-27 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [21] 1.000000e+00 3.654298e-50 1.362910e-59 1.000000e+00 7.051379e-119
## [26] 1.000000e+00 9.183580e-49 1.000000e+00 1.000000e+00 2.175489e-123
## [31] 1.000000e+00 1.000000e+00 1.211047e-75 1.087470e-95 1.000000e+00
## [36] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 9.997739e-01
## [41] 4.289987e-25 1.000000e+00 1.439481e-34 2.216997e-51 1.672481e-15
## [46] 1.000000e+00 2.654108e-35 1.000000e+00 5.197330e-66 1.218532e-63
## [51] 2.670266e-08 1.000000e+00 4.391193e-20 1.000000e+00 6.553464e-77
## [56] 7.504280e-16 1.036283e-38 6.150568e-12 1.000000e+00 9.999993e-01
## [61] 1.000000e+00 1.000000e+00 9.374754e-07 1.143625e-76 9.529245e-08
## [66] 1.000000e+00 1.000000e+00 4.670587e-02 7.151230e-11 1.000000e+00
## [71] 2.118885e-34 1.000000e+00 6.585201e-22 1.000000e+00 6.622576e-85
## [76] 3.853323e-16 1.000000e+00 1.000000e+00 1.690776e-08 1.000000e+00
## [81] 8.581523e-15 1.893699e-37 1.000000e+00 1.000000e+00 1.000000e+00
## [86] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## [91] 9.998426e-01 5.926292e-129 1.000000e+00 1.000000e+00 9.901945e-34
## [96] 1.210623e-61 4.612959e-12 3.081311e-24 1.000000e+00 1.000000e+00set.seed(1234)
y <- rbinom(n,1,p)
y## [1] 0 0 1 1 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 0 1 1 1
## [38] 1 1 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1
## [75] 0 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1datagab <- data.frame(y,X1,X2,X3,X4)
datagab## y X1 X2 X3 X4
## 1 0 2 -1241.18655 7 1
## 2 0 1 -5115.79384 2 1
## 3 1 4 636.59455 6 0
## 4 1 0 5792.39710 3 1
## 5 0 3 -6715.77646 3 0
## 6 1 3 3041.39242 4 0
## 7 0 8 -4096.47685 8 0
## 8 1 2 5574.61727 2 1
## 9 1 4 4243.89619 4 0
## 10 0 1 -2416.55563 4 0
## 11 1 5 -685.98732 7 1
## 12 1 11 -106.53208 1 1
## 13 0 2 -1036.65067 3 0
## 14 1 3 7845.87678 1 0
## 15 1 7 489.50241 3 0
## 16 0 6 -2039.07494 8 0
## 17 1 1 2013.98273 2 0
## 18 1 2 705.07477 8 1
## 19 1 2 -109.75084 7 0
## 20 1 6 -177.46963 4 1
## 21 1 4 1446.70982 8 0
## 22 0 6 -3093.06701 7 0
## 23 0 4 -3387.25797 7 0
## 24 1 7 5045.56524 1 0
## 25 0 3 -6086.28806 6 1
## 26 1 1 138.24916 3 0
## 27 0 7 -3162.58505 7 1
## 28 1 11 911.61848 3 0
## 29 1 4 3125.35562 1 1
## 30 0 2 -6123.21426 2 1
## 31 1 3 2790.46682 1 0
## 32 1 13 -49.98730 2 1
## 33 0 2 -3936.04793 5 0
## 34 0 15 -5693.63460 2 0
## 35 1 6 2228.74475 6 0
## 36 1 11 -373.71510 8 1
## 37 1 1 749.10362 4 0
## 38 1 5 2552.08623 7 1
## 39 1 23 5281.52726 4 1
## 40 1 1 -211.31088 1 1
## 41 0 2 -1571.36687 1 1
## 42 1 10 4384.90429 7 0
## 43 0 8 -2578.87221 7 1
## 44 0 9 -3372.71372 5 1
## 45 0 5 -1490.88936 7 1
## 46 1 3 7437.04827 3 0
## 47 0 8 -2397.48739 1 0
## 48 1 11 1340.55955 7 1
## 49 0 1 -3436.04942 3 1
## 50 0 2 -3422.10428 4 1
## 51 0 2 -898.77005 5 1
## 52 1 1 4189.34112 5 1
## 53 0 1 -1307.44202 5 0
## 54 1 2 2628.62690 5 0
## 55 0 4 -4273.98117 8 1
## 56 0 1 -1100.91776 2 1
## 57 0 1 -2229.25186 5 1
## 58 0 1 -1007.88953 8 0
## 59 1 5 3820.30438 6 0
## 60 1 1 -222.30143 6 1
## 61 1 3 617.78052 8 0
## 62 1 5 863.80380 5 0
## 63 0 16 -1743.60149 5 0
## 64 0 6 -4187.64529 2 0
## 65 0 3 -892.92630 3 1
## 66 1 2 485.15819 5 0
## 67 1 3 3154.35998 5 0
## 68 0 3 -666.72107 7 0
## 69 0 1 -807.62303 2 0
## 70 1 6 4945.57205 7 0
## 71 0 3 -2221.14006 7 1
## 72 1 2 1372.00720 1 1
## 73 0 4 -1651.84094 7 0
## 74 1 17 1345.10704 8 0
## 75 0 5 -4535.78497 1 1
## 76 0 5 -1456.24851 7 0
## 77 1 10 -739.37466 8 0
## 78 1 7 3122.37215 3 1
## 79 0 9 -1308.70986 4 0
## 80 1 0 993.69112 4 1
## 81 0 3 -1153.38330 3 0
## 82 0 5 -2451.14233 5 1
## 83 1 13 2167.13858 7 0
## 84 1 20 -1280.58858 4 0
## 85 1 0 4144.55313 7 0
## 86 1 4 1429.21415 3 0
## 87 1 7 -352.77857 8 1
## 88 1 1 1466.64657 8 1
## 89 1 2 -20.46404 2 1
## 90 1 7 1468.36313 1 1
## 91 1 12 -1125.27509 7 1
## 92 0 1 -6309.48157 2 1
## 93 1 2 570.46575 4 1
## 94 1 3 2080.14670 1 1
## 95 0 12 -2845.50324 8 1
## 96 0 2 -3240.93111 3 0
## 97 0 4 -1212.04303 5 1
## 98 0 1 -1448.33374 3 0
## 99 1 0 1356.98797 8 1
## 100 1 7 502.46631 7 1datasim = data.frame(y,X1,X2,X3)
View(datasim)modelreglog <- glm(y ~ X1 + X2 + X3 + X4, family = binomial(link="logit"),data=datagab)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(modelreglog)##
## Call:
## glm(formula = y ~ X1 + X2 + X3 + X4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.604e-01 2.461e+04 0.000 1.000
## X1 6.356e+00 3.766e+03 0.002 0.999
## X2 8.327e-02 2.206e+01 0.004 0.997
## X3 2.459e+00 4.109e+03 0.001 1.000
## X4 2.906e+01 1.951e+04 0.001 0.999
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.3719e+02 on 99 degrees of freedom
## Residual deviance: 2.6550e-08 on 95 degrees of freedom
## AIC: 10
##
## Number of Fisher Scoring iterations: 25Kesimpulan : Kesimpulan dari skenario di atas yang menunjukan bahwa X1 dan X4 signifikan terhadap model adalah bahwa usia dan tingkat pendidikan pembeli memiliki hubungan signifikan dengan keputusan pembelian. Usia pembeli juga memiliki hubungan signifikan dengan keputusan pembelian. Hal ini mengindikasikan bahwa usia dan tingkat pendidikan pembeli adalah faktor yang mempengaruhi keputusan pembelian. Pendapatan bulanan, yang ditunjukkan dalam X2, tidak signifikan terhadap model, sehingga tidak memiliki hubungan signifikan dengan keputusan pembelian. Pekerjaan, yang ditunjukkan dalam X3, juga tidak signifikan terhadap model, sehingga tidak memiliki hubungan signifikan dengan keputusan pembelian.