Membangkitkan Data (UTS)

Skenario

Y :Keputusan menolak/menerima merental mobil (0 : Menolak, 1: Menerima) X1 :SIM (0: Tidak Memiliki, 1: Memiliki) X2 :KTP (0: Tidak Aktif, 1: Memiliki) X3 :Asuransi (0: Tidak Memiliki, 1: Memiliki) X4 :Kartu Kredit atau Debit (0: Tidak Memiliki, 1: Memiliki)

Membangkitkan Data X1

X1 :SIM (0: Tidak Memiliki, 1: Memiliki)

n <- 100
u <- runif(n)
set.seed(110)
X1 <- round(runif(n))
X1
##   [1] 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 0 0 1 0 1
##  [38] 1 0 1 1 1 0 1 1 1 0 0 1 0 1 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0
##  [75] 1 0 0 1 0 1 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0

Membangkitkan Data X2

X2 : KTP (0: Tidak Aktif, 1: Memiliki)

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

Membangkitkan Data X3

X3 :Asuransi (0: Tidak Memiliki, 1: Memiliki)

set.seed(17)
X3 <- round(runif(n))
X3
##   [1] 0 1 0 1 0 1 0 0 1 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 1
##  [38] 1 1 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1
##  [75] 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 0 1

Membangkitkan Data X4

X4 :Kartu Kredit atau Debit (0: Tidak Memiliki, 1: Memiliki)

set.seed(19)
X4 <- round(runif(n))
X4
##   [1] 0 0 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 1 1
##  [38] 1 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1
##  [75] 0 0 0 0 0 1 1 1 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 1 0

Membangkitkan Data Y

Menentukan Koef

b0 <- -8
b1 <- 3
b2 <- 3
b3 <- 3
b4 <- 3
set.seed(103)
datas <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datas
##   [1] -2 -2 -2  1 -5 -5 -5  1  1 -2 -2 -5 -2  1 -2 -2 -5  1 -2  1  1  1  1 -2  4
##  [26] -5 -2  1  4 -2 -5 -2 -5 -5  1 -2  1  4 -2  1  4 -2 -5  4  1 -2 -2 -2 -2 -2
##  [51]  4  1 -5 -5  1 -2 -2 -2 -5 -5 -2  1 -5 -2 -2  1 -5 -5 -5 -2 -2  4 -2 -2 -2
##  [76] -5 -5 -2 -8  4 -2 -2 -2  1 -5 -5 -2  1 -2 -5 -2  1  4 -2 -2 -2  4 -5  1 -2
p <- exp(datas)/(1+exp(datas))
p
##   [1] 0.1192029220 0.1192029220 0.1192029220 0.7310585786 0.0066928509
##   [6] 0.0066928509 0.0066928509 0.7310585786 0.7310585786 0.1192029220
##  [11] 0.1192029220 0.0066928509 0.1192029220 0.7310585786 0.1192029220
##  [16] 0.1192029220 0.0066928509 0.7310585786 0.1192029220 0.7310585786
##  [21] 0.7310585786 0.7310585786 0.7310585786 0.1192029220 0.9820137900
##  [26] 0.0066928509 0.1192029220 0.7310585786 0.9820137900 0.1192029220
##  [31] 0.0066928509 0.1192029220 0.0066928509 0.0066928509 0.7310585786
##  [36] 0.1192029220 0.7310585786 0.9820137900 0.1192029220 0.7310585786
##  [41] 0.9820137900 0.1192029220 0.0066928509 0.9820137900 0.7310585786
##  [46] 0.1192029220 0.1192029220 0.1192029220 0.1192029220 0.1192029220
##  [51] 0.9820137900 0.7310585786 0.0066928509 0.0066928509 0.7310585786
##  [56] 0.1192029220 0.1192029220 0.1192029220 0.0066928509 0.0066928509
##  [61] 0.1192029220 0.7310585786 0.0066928509 0.1192029220 0.1192029220
##  [66] 0.7310585786 0.0066928509 0.0066928509 0.0066928509 0.1192029220
##  [71] 0.1192029220 0.9820137900 0.1192029220 0.1192029220 0.1192029220
##  [76] 0.0066928509 0.0066928509 0.1192029220 0.0003353501 0.9820137900
##  [81] 0.1192029220 0.1192029220 0.1192029220 0.7310585786 0.0066928509
##  [86] 0.0066928509 0.1192029220 0.7310585786 0.1192029220 0.0066928509
##  [91] 0.1192029220 0.7310585786 0.9820137900 0.1192029220 0.1192029220
##  [96] 0.1192029220 0.9820137900 0.0066928509 0.7310585786 0.1192029220
set.seed(2)
Y <- ifelse(X1 == 1 & X2 == 1 & X3 == 1 & X4 == 1, 1, 0)
Y
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
##  [38] 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
##  [75] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
datagab <- data.frame(Y,X1,X2,X3,X4)
datagab
##     Y X1 X2 X3 X4
## 1   0  1  1  0  0
## 2   0  0  1  1  0
## 3   0  1  0  0  1
## 4   0  1  1  1  0
## 5   0  1  0  0  0
## 6   0  0  0  1  0
## 7   0  1  0  0  0
## 8   0  1  1  0  1
## 9   0  1  0  1  1
## 10  0  1  0  0  1
## 11  0  1  1  0  0
## 12  0  0  1  0  0
## 13  0  0  0  1  1
## 14  0  1  0  1  1
## 15  0  1  0  1  0
## 16  0  0  0  1  1
## 17  0  0  0  1  0
## 18  0  1  1  0  1
## 19  0  1  0  1  0
## 20  0  0  1  1  1
## 21  0  1  0  1  1
## 22  0  1  0  1  1
## 23  0  1  0  1  1
## 24  0  0  0  1  1
## 25  1  1  1  1  1
## 26  0  0  0  0  1
## 27  0  0  1  1  0
## 28  0  1  0  1  1
## 29  1  1  1  1  1
## 30  0  1  1  0  0
## 31  0  1  0  0  0
## 32  0  0  1  0  1
## 33  0  0  0  1  0
## 34  0  0  0  0  1
## 35  0  1  0  1  1
## 36  0  0  1  0  1
## 37  0  1  0  1  1
## 38  1  1  1  1  1
## 39  0  0  1  1  0
## 40  0  1  1  1  0
## 41  1  1  1  1  1
## 42  0  1  0  1  0
## 43  0  0  1  0  0
## 44  1  1  1  1  1
## 45  0  1  1  0  1
## 46  0  1  0  0  1
## 47  0  0  0  1  1
## 48  0  0  1  1  0
## 49  0  1  1  0  0
## 50  0  0  1  1  0
## 51  1  1  1  1  1
## 52  0  1  1  0  1
## 53  0  0  0  1  0
## 54  0  1  0  0  0
## 55  0  0  1  1  1
## 56  0  0  1  1  0
## 57  0  0  1  1  0
## 58  0  1  0  0  1
## 59  0  0  0  0  1
## 60  0  0  1  0  0
## 61  0  1  0  0  1
## 62  0  1  0  1  1
## 63  0  1  0  0  0
## 64  0  1  0  0  1
## 65  0  0  1  0  1
## 66  0  1  0  1  1
## 67  0  0  0  0  1
## 68  0  0  0  0  1
## 69  0  0  0  0  1
## 70  0  0  0  1  1
## 71  0  1  1  0  0
## 72  1  1  1  1  1
## 73  0  1  0  0  1
## 74  0  0  0  1  1
## 75  0  1  1  0  0
## 76  0  0  0  1  0
## 77  0  0  0  1  0
## 78  0  1  1  0  0
## 79  0  0  0  0  0
## 80  1  1  1  1  1
## 81  0  0  0  1  1
## 82  0  0  1  0  1
## 83  0  1  0  0  1
## 84  0  1  0  1  1
## 85  0  1  0  0  0
## 86  0  0  0  0  1
## 87  0  0  1  1  0
## 88  0  0  1  1  1
## 89  0  0  1  1  0
## 90  0  0  1  0  0
## 91  0  0  1  1  0
## 92  0  0  1  1  1
## 93  1  1  1  1  1
## 94  0  1  1  0  0
## 95  0  0  1  0  1
## 96  0  0  0  1  1
## 97  1  1  1  1  1
## 98  0  0  0  1  0
## 99  0  1  1  0  1
## 100 0  0  1  1  0

Analisis Regresi Logistik

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 occurred
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)   -164.57  102980.78  -0.002    0.999
## X1              46.82   42627.67   0.001    0.999
## X2              47.94   44123.64   0.001    0.999
## X3              47.16   44347.35   0.001    0.999
## X4              46.17   43185.98   0.001    0.999
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017e+01  on 99  degrees of freedom
## Residual deviance: 3.4965e-09  on 95  degrees of freedom
## AIC: 10
## 
## Number of Fisher Scoring iterations: 25

Kesimpulan

Semua Variabel X1,X2,X3,X4 berpengaruh dalam keputusan pada variabel Y. Karena tiap variabel memiliki nilai yang sama penting, Jika ada 1 variabel X yang tidak memenuhi (Bernilai 0) maka variabel Y akan bernilai 0 (Menolak), dan variabel Y akan bernilai 1 (Menerima) jika tidak ada variabel X yang bernilai 0.