Membangkitkan Data Pada Penjualan Puding Sedot
Rosita D
2024-04-04
#Membangkitkan Data ## skenario Y : Keputusan rugi/untung penjualan puding sedot X1 :Lama penjualan (minggu) X2 :Diskon penjualan (bulan) X3 :Penilaian produk dari pembeli(1= nyegerin, 2= nyegerin banget, 3=biasa) X4 :Usia pembeli puding sedot (15 hingga 30 tahun)
Menjalankan data X1
X1 : Lama penjualan (minggu) membangkitkan variabel x1 dengan diskon penjualan 1-3 bulan dengan nilai tengah 2 dan banyak pembeli 150(minggu)
set.seed(150)
n<- 150
u<- runif(n)
x1 <- round(3*(-(log(1-u)/2)))
x1## [1] 0 1 1 3 0 1 1 1 1 3 1 4 2 1 2 5 1 0 0 1 1 0 0 0 1 2 0 2 0 1 6 2 1 0 4 2 2
## [38] 0 0 1 1 2 4 1 2 1 1 0 7 1 3 3 4 2 0 1 5 0 1 3 3 4 0 1 1 1 3 1 1 2 1 6 3 1
## [75] 0 3 2 1 0 6 0 1 1 1 0 0 2 0 1 0 0 0 4 1 3 1 1 2 1 0 2 2 1 0 0 1 1 0 1 3 1
## [112] 0 0 4 1 5 1 2 0 1 1 0 0 0 2 1 1 1 6 2 0 1 0 3 1 1 1 0 2 2 1 1 2 1 1 1 1 4
## [149] 2 0##Menjalankan data ke 2 X2 :Diskon penjualan (bulan)
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##Menjalankan data ke 3 X3 :Penilaian produk dari pembeli keterangan yang digunakan (1= nyegerin, 2= nyegerin banget, 3=biasa)
set.seed(123)
X3 <- round(runif(n))
X3## [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 1Menjalankan data ke 4
X4 :Usia pembeli puding sedot usia (15 hingga 30 tahun)
set.seed(1)
x4 <- round(rnorm(n,3, 0.3),2)
x4## [1] 2.81 3.06 2.75 3.48 3.10 2.75 3.15 3.22 3.17 2.91 3.45 3.12 2.81 2.34 3.34
## [16] 2.99 3.00 3.28 3.25 3.18 3.28 3.23 3.02 2.40 3.19 2.98 2.95 2.56 2.86 3.13
## [31] 3.41 2.97 3.12 2.98 2.59 2.88 2.88 2.98 3.33 3.23 2.95 2.92 3.21 3.17 2.79
## [46] 2.79 3.11 3.23 2.97 3.26 3.12 2.82 3.10 2.66 3.43 3.59 2.89 2.69 3.17 2.96
## [61] 3.72 2.99 3.21 3.01 2.78 3.06 2.46 3.44 3.05 3.65 3.14 2.79 3.18 2.72 2.62
## [76] 3.09 2.87 3.00 3.02 2.82 2.83 2.96 3.35 2.54 3.18 3.10 3.32 2.91 3.11 3.08
## [91] 2.84 3.36 3.35 3.21 3.48 3.17 2.62 2.83 2.63 2.86 2.81 3.01 2.73 3.05 2.80
## [106] 3.53 3.22 3.27 3.12 3.50 2.81 2.86 3.43 2.80 2.94 2.88 2.90 2.92 3.15 2.95
## [121] 2.85 3.40 2.94 2.95 2.97 3.21 2.98 2.99 2.80 2.90 3.02 2.82 3.16 2.54 3.09
## [136] 2.54 2.91 2.84 2.80 2.98 2.43 3.35 2.50 2.86 2.67 2.77 3.63 3.01 2.61 2.51Menentukan beta
Membangkitkan data Y
Melakukan koefisien
b0 <- -15
b1 <- 1.7
b2 <- 0.5
b3 <- 1.1
b4 <- 3.5set.seed(1)
xb <- b0+(b1*x1)+(b2*x2)+(b3*X3)+(b4*x4)
xb## [1] -5.165 -0.990 -3.175 3.880 -2.550 -3.175 -1.175 -0.930 -0.605 0.785
## [11] 0.375 3.220 -0.665 -3.510 0.090 5.565 -2.800 -3.520 -3.625 -1.070
## [21] -0.720 -2.595 -3.330 -5.500 -1.035 0.430 -3.075 -1.040 -4.490 -2.345
## [31] 8.235 -0.105 -1.280 -2.970 0.865 -1.020 -0.420 -4.570 -2.845 -1.495
## [41] -2.475 -0.880 3.035 -1.705 -1.835 -3.035 -1.915 -3.695 7.295 -0.290
## [51] 1.020 -0.030 4.250 -1.790 -1.895 -0.235 3.615 -3.985 -1.105 0.960
## [61] 4.720 2.265 -3.765 -2.765 -2.470 -2.090 -0.190 0.340 -1.525 1.675
## [71] -1.210 6.565 2.330 -3.280 -5.830 1.415 -1.555 -1.700 -4.430 5.570
## [81] -4.595 -1.840 -1.575 -2.810 -3.870 -3.650 1.120 -3.715 -1.315 -3.720
## [91] -5.060 -1.640 3.525 -0.965 2.280 -1.705 -3.030 -1.695 -4.095 -3.390
## [101] -0.665 -0.565 -3.745 -3.225 -5.200 0.155 -0.930 -2.455 -2.380 2.350
## [111] -1.865 -4.990 -2.495 2.700 -1.910 4.080 -1.550 -0.280 -2.875 -2.475
## [121] -1.725 -2.600 -4.210 -4.175 -1.205 -0.965 -2.870 -2.335 5.000 -0.350
## [131] -2.830 -1.830 -2.840 0.090 -0.885 -3.310 -1.515 -3.460 -0.200 -0.670
## [141] -4.295 -1.075 -2.850 -3.290 -2.855 -3.105 -0.095 2.835 -1.965 -4.615p <- exp(xb)/(1+exp(xb))
p## [1] 0.005680609 0.270912078 0.040117432 0.979767003 0.072426485 0.040117432
## [7] 0.235952400 0.282924715 0.353200607 0.686756727 0.592666600 0.961580014
## [13] 0.339617327 0.029029036 0.522484825 0.996185029 0.057324176 0.028748496
## [19] 0.025957357 0.255403084 0.327392983 0.069460906 0.034556230 0.004070138
## [25] 0.262115899 0.605873668 0.044150341 0.261149994 0.011096138 0.087464020
## [31] 0.999734863 0.473774091 0.217550224 0.048799723 0.703704238 0.265027401
## [37] 0.396516750 0.010251772 0.054940350 0.183172441 0.077629463 0.293177779
## [43] 0.954130498 0.153813364 0.137643708 0.045869502 0.128420169 0.024245026
## [49] 0.999321536 0.428003867 0.734972599 0.492500562 0.985936373 0.143072723
## [55] 0.130675423 0.441518888 0.973788605 0.018253075 0.248804218 0.723121805
## [61] 0.991163600 0.905936574 0.022643027 0.059245076 0.077988235 0.110072574
## [67] 0.452642382 0.584190523 0.178726424 0.842241313 0.229701051 0.998593158
## [73] 0.911331337 0.036263716 0.002929470 0.804553377 0.174365286 0.154465265
## [79] 0.011774206 0.996203984 0.010001187 0.137051293 0.171504770 0.056786181
## [85] 0.020432187 0.025332703 0.753988716 0.023776357 0.211651366 0.023660578
## [91] 0.006305547 0.162465063 0.971390786 0.275878229 0.907207047 0.153813364
## [97] 0.046088827 0.155119423 0.016382877 0.032609455 0.339617327 0.362391347
## [103] 0.023089885 0.038235692 0.005486299 0.538672605 0.282924715 0.079073677
## [109] 0.084710566 0.912934228 0.134121325 0.006759661 0.076209443 0.937026644
## [115] 0.128980852 0.983373644 0.175086268 0.430453776 0.053403330 0.077629463
## [121] 0.151228253 0.069138420 0.014629178 0.015142375 0.230586939 0.275878229
## [127] 0.053656652 0.088265461 0.993307149 0.413382421 0.055724398 0.138238273
## [133] 0.055200538 0.522484825 0.292142729 0.035229719 0.180198975 0.030472033
## [139] 0.450166003 0.338496841 0.013453117 0.254453386 0.054681317 0.035915846
## [145] 0.054423436 0.042901480 0.476267846 0.944538114 0.122926951 0.009805092y <- rbinom(150, 1, p)
y## [1] 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1
## [38] 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0
## [75] 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1
## [112] 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1
## [149] 0 0Menyusun Data
randData <- data.frame(X1 = x1, X2 = x2, X3 = X3, X4 = x4)
head(randData, 150)## X1 X2 X3 X4
## 1 0 0 0 2.81
## 2 1 1 1 3.06
## 3 1 1 0 2.75
## 4 3 1 1 3.48
## 5 0 1 1 3.10
## 6 1 1 0 2.75
## 7 1 0 1 3.15
## 8 1 0 1 3.22
## 9 1 1 1 3.17
## 10 3 1 0 2.91
## 11 1 1 1 3.45
## 12 4 1 0 3.12
## 13 2 0 1 2.81
## 14 1 1 1 2.34
## 15 2 0 0 3.34
## 16 5 1 1 2.99
## 17 1 0 0 3.00
## 18 0 0 0 3.28
## 19 0 0 0 3.25
## 20 1 0 1 3.18
## 21 1 0 1 3.28
## 22 0 0 1 3.23
## 23 0 0 1 3.02
## 24 0 0 1 2.40
## 25 1 0 1 3.19
## 26 2 1 1 2.98
## 27 0 1 1 2.95
## 28 2 1 1 2.56
## 29 0 1 0 2.86
## 30 1 0 0 3.13
## 31 6 0 1 3.41
## 32 2 0 1 2.97
## 33 1 0 1 3.12
## 34 0 1 1 2.98
## 35 4 0 0 2.59
## 36 2 1 0 2.88
## 37 2 0 1 2.88
## 38 0 0 0 2.98
## 39 0 1 0 3.33
## 40 1 1 0 3.23
## 41 1 1 0 2.95
## 42 2 1 0 2.92
## 43 4 0 0 3.21
## 44 1 1 0 3.17
## 45 2 0 0 2.79
## 46 1 1 0 2.79
## 47 1 1 0 3.11
## 48 0 0 0 3.23
## 49 7 0 0 2.97
## 50 1 1 1 3.26
## 51 3 0 0 3.12
## 52 3 0 0 2.82
## 53 4 1 1 3.10
## 54 2 1 0 2.66
## 55 0 0 1 3.43
## 56 1 1 0 3.59
## 57 5 0 0 2.89
## 58 0 1 1 2.69
## 59 1 0 1 3.17
## 60 3 1 0 2.96
## 61 3 1 1 3.72
## 62 4 0 0 2.99
## 63 0 0 0 3.21
## 64 1 0 0 3.01
## 65 1 0 1 2.78
## 66 1 1 0 3.06
## 67 3 0 1 2.46
## 68 1 1 1 3.44
## 69 1 0 1 3.05
## 70 2 1 0 3.65
## 71 1 0 1 3.14
## 72 6 1 1 2.79
## 73 3 0 1 3.18
## 74 1 1 0 2.72
## 75 0 0 0 2.62
## 76 3 1 0 3.09
## 77 2 0 0 2.87
## 78 1 0 1 3.00
## 79 0 0 0 3.02
## 80 6 1 0 2.82
## 81 0 1 0 2.83
## 82 1 0 1 2.96
## 83 1 0 0 3.35
## 84 1 1 1 2.54
## 85 0 0 0 3.18
## 86 0 1 0 3.10
## 87 2 0 1 3.32
## 88 0 0 1 2.91
## 89 1 0 1 3.11
## 90 0 1 0 3.08
## 91 0 0 0 2.84
## 92 0 1 1 3.36
## 93 4 0 0 3.35
## 94 1 0 1 3.21
## 95 3 0 0 3.48
## 96 1 1 0 3.17
## 97 1 0 1 2.62
## 98 2 0 0 2.83
## 99 1 0 0 2.63
## 100 0 1 1 2.86
## 101 2 0 1 2.81
## 102 2 1 0 3.01
## 103 1 0 0 2.73
## 104 0 0 1 3.05
## 105 0 0 0 2.80
## 106 1 0 1 3.53
## 107 1 0 1 3.22
## 108 0 0 1 3.27
## 109 1 0 0 3.12
## 110 3 0 0 3.50
## 111 1 1 1 2.81
## 112 0 0 0 2.86
## 113 0 1 0 3.43
## 114 4 0 1 2.80
## 115 1 0 1 2.94
## 116 5 1 0 2.88
## 117 1 1 1 2.90
## 118 2 0 1 2.92
## 119 0 0 1 3.15
## 120 1 1 0 2.95
## 121 1 1 1 2.85
## 122 0 1 0 3.40
## 123 0 1 0 2.94
## 124 0 1 0 2.95
## 125 2 0 0 2.97
## 126 1 0 1 3.21
## 127 1 0 0 2.98
## 128 1 1 0 2.99
## 129 6 0 0 2.80
## 130 2 0 1 2.90
## 131 0 1 1 3.02
## 132 1 1 1 2.82
## 133 0 0 1 3.16
## 134 3 0 1 2.54
## 135 1 1 1 3.09
## 136 1 0 1 2.54
## 137 1 1 1 2.91
## 138 0 1 1 2.84
## 139 2 1 1 2.80
## 140 2 1 0 2.98
## 141 1 1 0 2.43
## 142 1 1 0 3.35
## 143 2 0 0 2.50
## 144 1 0 0 2.86
## 145 1 0 1 2.67
## 146 1 1 0 2.77
## 147 1 1 0 3.63
## 148 4 1 0 3.01
## 149 2 1 0 2.61
## 150 0 1 1 2.51Analisis Regresi Logistik
modelreglog <- glm(y~x1+x2+X3+x4, family = binomial(link="logit"), data = randData)
summary(modelreglog)##
## Call:
## glm(formula = y ~ x1 + x2 + X3 + x4, family = binomial(link = "logit"),
## data = randData)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -15.8249 3.7796 -4.187 2.83e-05 ***
## x1 1.7312 0.3211 5.391 7.01e-08 ***
## x2 -0.1740 0.5403 -0.322 0.747480
## X3 2.0054 0.6487 3.091 0.001992 **
## x4 3.6673 1.0933 3.354 0.000796 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 183.26 on 149 degrees of freedom
## Residual deviance: 97.87 on 145 degrees of freedom
## AIC: 107.87
##
## Number of Fisher Scoring iterations: 6R Markdown
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Penjelasan Hasil
Skenario Y : Keputusan rugi/untung penjualan puding sedot X1 :Lama penjualan (minggu) X2 :Diskon penjualan (bulan) X3 :Penilaian produk dari pembeli(1= nyegerin, 2= nyegerin banget, 3=biasa) X4 :Usia pembeli puding sedot (15 hingga 30 tahun)
Dari skenario diatas terkait tentang penjualan puding sedot terdapat sebuah kesimpulan setelah diola menggunakan aplikasi Rstudio.
Kesimpulan
Dari hasil analisis regresi logistik menjelaskan bahwa lama penjualan dan tingkat ketertarikan konsumen dinyatakan mulai dari anak anak, remaja dan dewasa sangat berpengaruh signifikan. Selain itu, pemberian review rating penilaian produk sangat mendukung dalam penentu banyaknya rugi atau untung penjualan dalam jangka setiap minggunya.