Membangkitkan Data Pada Sepeda Motor

Skenario

Y :Keputusan menolak/menerima merental Motor X1 :KTP (0: Memiliki, 1: Tidak Memiliki) X2 :SIM (0: Aktif, 1: Tidak Memiliki) X3 :Asuransi (0: Memiliki, 1: Tidak Memiliki) X4 :Cash atau Debit (0: Memiliki, 1: Tidak Memiliki)

Membangkitkan data XI ( KETERANGAN KTP )

X1 :KTP (Tahun) Membangkitkan variabel X1 dengan memiliki Umur minimal 17 tahun dan maksimal umur 55 tahun dan banyaknya perental adalah 200

n <- 200
u <- runif(n)
set.seed(123)
X1 <- round(runif(n))
X1
##   [1] 0 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1
##  [38] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 1 0
##  [75] 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1
## [112] 0 0 1 1 0 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0
## [149] 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1
## [186] 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1

yang punya KTP aktif = 107 tidak aktif = 93

Membangkitkan data X2 ( Keterangan SIM )

X2 :Status SIM (0: Aktif, 1: Tidak Aktif)

set.seed(1234)
X2 <- round(runif(n))
X2
##   [1] 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0
##  [38] 0 1 1 1 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1
##  [75] 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1
## [112] 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 1 1 1 0 0 0 1 1 1
## [149] 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 0 1 0 1
## [186] 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1

Dari data X2 diperoleh jumlah SIM aktif = 96 jumlah SIM yang tidak aktif = 104

membangkitkan data X3 ( KETERANGAN ASURANSI )

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

set.seed(1236)
X3 <- round(runif(n))
X3
##   [1] 0 1 0 0 0 1 1 1 0 0 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0
##  [38] 1 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 1 0
##  [75] 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1
## [112] 1 0 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1
## [149] 0 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 0 1 1 0 1 0
## [186] 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0

yang mempunyai asuransi aktif = 101 yang tidak aktif = 99

membangkitkan X4 (KETERANGAN JAMINAN PEMBAYARAN CASH DAN DEBIT )

X4 :cash atau Debit (0: Aktif, 1: Tidak Aktif)

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 1 0 0 1 0 1 0 0 1 1 0
## [112] 0 0 1 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 1 1
## [149] 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 0 0
## [186] 0 0 0 0 1 1 0 0 0 1 0 0 1 1 0

yang memiliki jaminanan metode pembayaran cash atau debit aktif = 91 yang tidak aktif = 109

Membangkitkan data Y

Menentukan Koefisien

b0 <- -8
b1 <- 3
b2 <- 4
b3 <- 2
b4 <- 1
set.seed(103)
datas <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datas
##   [1] -8  1 -3 -1 -1 -2 -3 -2  0 -3  1 -2 -4  2 -6  0 -8 -7 -6 -4 -2 -2 -2 -4 -2
##  [26]  0 -1  0 -1 -8 -5 -2 -3  0 -7 -3 -4 -5 -4 -2 -3 -4 -8 -3 -5 -3 -3 -6 -6 -1
##  [51] -5 -5  1 -4 -2 -4 -6  0 -4 -2  2 -7 -6 -7 -4 -1 -2  0 -4 -3 -5  0 -2 -3 -8
##  [76] -2 -6 -5 -6 -3 -1 -4 -5  0 -6 -1 -5 -2 -3 -2 -6  0 -7 -3 -5 -3 -4 -6 -5  1
## [101] -4 -4 -6 -2 -8 -4 -5 -5 -5 -5  1 -6 -4 -4 -2 -4 -1 -4 -2 -4  2 -1 -1 -1 -6
## [126] -2 -7 -1 -6 -4  2 -1 -4 -4  1 -5 -1  1 -1 -1 -2 -2 -7 -6 -4 -1 -1 -1 -4  1
## [151] -2 -8 -6 -4 -7 -3  1 -2 -7 -2 -3 -2 -3 -7 -7  2 -3 -5 -3 -5 -1 -1  0 -3 -2
## [176] -1 -5  0 -2 -4 -3 -5  0 -6 -1 -8 -4 -1 -3  0 -3 -4 -5 -1  2 -4 -4  2 -1 -1
p <- exp(datas)/(1+exp(datas))
p
##   [1] 0.0003353501 0.7310585786 0.0474258732 0.2689414214 0.2689414214
##   [6] 0.1192029220 0.0474258732 0.1192029220 0.5000000000 0.0474258732
##  [11] 0.7310585786 0.1192029220 0.0179862100 0.8807970780 0.0024726232
##  [16] 0.5000000000 0.0003353501 0.0009110512 0.0024726232 0.0179862100
##  [21] 0.1192029220 0.1192029220 0.1192029220 0.0179862100 0.1192029220
##  [26] 0.5000000000 0.2689414214 0.5000000000 0.2689414214 0.0003353501
##  [31] 0.0066928509 0.1192029220 0.0474258732 0.5000000000 0.0009110512
##  [36] 0.0474258732 0.0179862100 0.0066928509 0.0179862100 0.1192029220
##  [41] 0.0474258732 0.0179862100 0.0003353501 0.0474258732 0.0066928509
##  [46] 0.0474258732 0.0474258732 0.0024726232 0.0024726232 0.2689414214
##  [51] 0.0066928509 0.0066928509 0.7310585786 0.0179862100 0.1192029220
##  [56] 0.0179862100 0.0024726232 0.5000000000 0.0179862100 0.1192029220
##  [61] 0.8807970780 0.0009110512 0.0024726232 0.0009110512 0.0179862100
##  [66] 0.2689414214 0.1192029220 0.5000000000 0.0179862100 0.0474258732
##  [71] 0.0066928509 0.5000000000 0.1192029220 0.0474258732 0.0003353501
##  [76] 0.1192029220 0.0024726232 0.0066928509 0.0024726232 0.0474258732
##  [81] 0.2689414214 0.0179862100 0.0066928509 0.5000000000 0.0024726232
##  [86] 0.2689414214 0.0066928509 0.1192029220 0.0474258732 0.1192029220
##  [91] 0.0024726232 0.5000000000 0.0009110512 0.0474258732 0.0066928509
##  [96] 0.0474258732 0.0179862100 0.0024726232 0.0066928509 0.7310585786
## [101] 0.0179862100 0.0179862100 0.0024726232 0.1192029220 0.0003353501
## [106] 0.0179862100 0.0066928509 0.0066928509 0.0066928509 0.0066928509
## [111] 0.7310585786 0.0024726232 0.0179862100 0.0179862100 0.1192029220
## [116] 0.0179862100 0.2689414214 0.0179862100 0.1192029220 0.0179862100
## [121] 0.8807970780 0.2689414214 0.2689414214 0.2689414214 0.0024726232
## [126] 0.1192029220 0.0009110512 0.2689414214 0.0024726232 0.0179862100
## [131] 0.8807970780 0.2689414214 0.0179862100 0.0179862100 0.7310585786
## [136] 0.0066928509 0.2689414214 0.7310585786 0.2689414214 0.2689414214
## [141] 0.1192029220 0.1192029220 0.0009110512 0.0024726232 0.0179862100
## [146] 0.2689414214 0.2689414214 0.2689414214 0.0179862100 0.7310585786
## [151] 0.1192029220 0.0003353501 0.0024726232 0.0179862100 0.0009110512
## [156] 0.0474258732 0.7310585786 0.1192029220 0.0009110512 0.1192029220
## [161] 0.0474258732 0.1192029220 0.0474258732 0.0009110512 0.0009110512
## [166] 0.8807970780 0.0474258732 0.0066928509 0.0474258732 0.0066928509
## [171] 0.2689414214 0.2689414214 0.5000000000 0.0474258732 0.1192029220
## [176] 0.2689414214 0.0066928509 0.5000000000 0.1192029220 0.0179862100
## [181] 0.0474258732 0.0066928509 0.5000000000 0.0024726232 0.2689414214
## [186] 0.0003353501 0.0179862100 0.2689414214 0.0474258732 0.5000000000
## [191] 0.0474258732 0.0179862100 0.0066928509 0.2689414214 0.8807970780
## [196] 0.0179862100 0.0179862100 0.8807970780 0.2689414214 0.2689414214
set.seed(2345)
Y <- rbinom(n,1,p)
Y
##   [1] 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0
##  [38] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
##  [75] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1
## [149] 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0
## [186] 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0
datagab <- data.frame(Y,X1,X2,X3,X4)
datagab
##     Y X1 X2 X3 X4
## 1   0  0  0  0  0
## 2   1  1  1  1  0
## 3   0  0  1  0  1
## 4   0  1  1  0  0
## 5   0  1  1  0  0
## 6   0  0  1  1  0
## 7   0  1  0  1  0
## 8   0  1  0  1  1
## 9   0  1  1  0  1
## 10  0  0  1  0  1
## 11  1  1  1  1  0
## 12  0  0  1  1  0
## 13  0  1  0  0  1
## 14  1  1  1  1  1
## 15  0  0  0  1  0
## 16  0  1  1  0  1
## 17  0  0  0  0  0
## 18  0  0  0  0  1
## 19  0  0  0  1  0
## 20  0  1  0  0  1
## 21  1  1  0  1  1
## 22  0  1  0  1  1
## 23  0  1  0  1  1
## 24  0  1  0  0  1
## 25  0  1  0  1  1
## 26  0  1  1  0  1
## 27  1  1  1  0  0
## 28  1  1  1  0  1
## 29  0  0  1  1  1
## 30  0  0  0  0  0
## 31  0  1  0  0  0
## 32  0  1  0  1  1
## 33  1  1  0  1  0
## 34  1  1  1  0  1
## 35  0  0  0  0  1
## 36  0  0  1  0  1
## 37  0  1  0  0  1
## 38  0  0  0  1  1
## 39  0  0  1  0  0
## 40  0  0  1  1  0
## 41  0  0  1  0  1
## 42  0  0  1  0  0
## 43  0  0  0  0  0
## 44  0  0  1  0  1
## 45  0  0  0  1  1
## 46  1  0  1  0  1
## 47  0  0  1  0  1
## 48  0  0  0  1  0
## 49  0  0  0  1  0
## 50  0  1  1  0  0
## 51  0  0  0  1  1
## 52  0  0  0  1  1
## 53  1  1  1  1  0
## 54  0  0  1  0  0
## 55  0  1  0  1  1
## 56  0  0  1  0  0
## 57  0  0  0  1  0
## 58  1  1  1  0  1
## 59  0  1  0  0  1
## 60  0  0  1  1  0
## 61  1  1  1  1  1
## 62  0  0  0  0  1
## 63  0  0  0  1  0
## 64  0  0  0  0  1
## 65  0  1  0  0  1
## 66  1  0  1  1  1
## 67  0  1  0  1  1
## 68  0  1  1  0  1
## 69  0  1  0  0  1
## 70  0  0  1  0  1
## 71  0  1  0  0  0
## 72  0  1  1  0  1
## 73  0  1  0  1  1
## 74  0  0  1  0  1
## 75  0  0  0  0  0
## 76  0  0  1  1  0
## 77  0  0  0  1  0
## 78  0  1  0  0  0
## 79  0  0  0  1  0
## 80  0  0  1  0  1
## 81  1  0  1  1  1
## 82  0  1  0  0  1
## 83  0  0  0  1  1
## 84  0  1  1  0  1
## 85  0  0  0  1  0
## 86  0  0  1  1  1
## 87  0  1  0  0  0
## 88  0  1  0  1  1
## 89  0  1  0  1  0
## 90  1  0  1  1  0
## 91  0  0  0  1  0
## 92  1  1  1  0  1
## 93  0  0  0  0  1
## 94  1  1  0  1  0
## 95  0  0  0  1  1
## 96  0  0  1  0  1
## 97  0  1  0  0  1
## 98  0  0  0  1  0
## 99  0  0  0  1  1
## 100 1  1  1  1  0
## 101 0  1  0  0  1
## 102 0  0  1  0  0
## 103 0  0  0  1  0
## 104 0  1  0  1  1
## 105 0  0  0  0  0
## 106 0  1  0  0  1
## 107 0  1  0  0  0
## 108 0  1  0  0  0
## 109 0  0  0  1  1
## 110 0  0  0  1  1
## 111 0  1  1  1  0
## 112 0  0  0  1  0
## 113 0  0  1  0  0
## 114 0  1  0  0  1
## 115 0  1  0  1  1
## 116 0  0  1  0  0
## 117 0  1  1  0  0
## 118 0  1  0  0  1
## 119 0  1  0  1  1
## 120 0  0  1  0  0
## 121 0  1  1  1  1
## 122 0  0  1  1  1
## 123 0  0  1  1  1
## 124 1  0  1  1  1
## 125 0  0  0  1  0
## 126 0  1  0  1  1
## 127 0  0  0  0  1
## 128 1  0  1  1  1
## 129 0  0  0  1  0
## 130 0  1  0  0  1
## 131 1  1  1  1  1
## 132 1  1  1  0  0
## 133 0  1  0  0  1
## 134 0  1  0  0  1
## 135 1  1  1  1  0
## 136 0  1  0  0  0
## 137 0  1  1  0  0
## 138 1  1  1  1  0
## 139 0  1  1  0  0
## 140 0  0  1  1  1
## 141 0  0  1  1  0
## 142 0  0  1  1  0
## 143 0  0  0  0  1
## 144 0  0  0  1  0
## 145 0  1  0  0  1
## 146 0  0  1  1  1
## 147 1  0  1  1  1
## 148 1  0  1  1  1
## 149 0  0  1  0  0
## 150 0  1  1  1  0
## 151 0  1  0  1  1
## 152 0  0  0  0  0
## 153 0  0  0  1  0
## 154 0  0  1  0  0
## 155 0  0  0  0  1
## 156 0  0  1  0  1
## 157 1  1  1  1  0
## 158 0  0  1  1  0
## 159 0  0  0  0  1
## 160 1  0  1  1  0
## 161 0  1  0  1  0
## 162 0  0  1  1  0
## 163 0  1  0  1  0
## 164 0  0  0  0  1
## 165 0  0  0  0  1
## 166 1  1  1  1  1
## 167 0  1  0  1  0
## 168 0  0  0  1  1
## 169 1  0  1  0  1
## 170 0  0  0  1  1
## 171 0  1  1  0  0
## 172 1  0  1  1  1
## 173 1  1  1  0  1
## 174 0  1  0  1  0
## 175 0  1  0  1  1
## 176 0  1  1  0  0
## 177 0  0  0  1  1
## 178 0  1  1  0  1
## 179 0  1  0  1  1
## 180 0  1  0  0  1
## 181 1  1  0  1  0
## 182 0  0  0  1  1
## 183 0  1  1  0  1
## 184 0  0  0  1  0
## 185 0  1  1  0  0
## 186 0  0  0  0  0
## 187 0  0  1  0  0
## 188 0  1  1  0  0
## 189 0  1  0  1  0
## 190 0  1  1  0  1
## 191 0  0  1  0  1
## 192 0  0  1  0  0
## 193 0  1  0  0  0
## 194 1  1  1  0  0
## 195 1  1  1  1  1
## 196 0  0  1  0  0
## 197 0  0  1  0  0
## 198 1  1  1  1  1
## 199 1  0  1  1  1
## 200 0  1  1  0  0

Analisis Regresi Logistik

modelreglog <- 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)  -7.1255     1.1252  -6.333 2.41e-10 ***
## X1            2.0884     0.5653   3.694 0.000221 ***
## X2            3.7763     0.6995   5.399 6.71e-08 ***
## X3            2.5408     0.5741   4.426 9.60e-06 ***
## X4            0.7261     0.4802   1.512 0.130550    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 191.56  on 199  degrees of freedom
## Residual deviance: 119.47  on 195  degrees of freedom
## AIC: 129.47
## 
## Number of Fisher Scoring iterations: 6

Kesimpulan Analisis Data

XI = Keterangan KTP ( yang aktif = 107, Tidak aktif = 93 ) X2 = keterangan SIM ( yang aktif = 96 , Tidak aktif = 104 ) X3 = keterangan Asuransi ( yang aktif = 101 , Tidak aktif = 99 ) X4 = Keterangan jaminan pembayaran ( Yang aktif = 91 , Tidak aktif = 109)

Bahwa pada analisis regresinya sangat mempengaruhi pada X1, X2, X3, DAN x4. Karena dengan Kofisien nilai Bo= -8, BI= 3, B2= 4, B3= 2, B4= 1 dan pengujian dengan rumus B0+(B1 x X1)+(B2 x X2)+(B3 x X3)+(B4 x X4) semua data berpengaruh. Jadi data semua data ini saling keterkaitan pengaruh satu sama lain.