Analisis Pengaruh pengunjung toko dan kualitas pelayanan toko terhadap pertumbuhan jumlah penjualan di Toko Habibi di tahun 2023 menggunakan Data Bangkitan
Muhamad Abdul Qodir Dani
2024-04-04
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.3## Warning: package 'ggplot2' was built under R version 4.3.3## Warning: package 'tidyr' was built under R version 4.3.3## Warning: package 'readr' was built under R version 4.3.3## Warning: package 'purrr' was built under R version 4.3.3## Warning: package 'forcats' was built under R version 4.3.3## Warning: package 'lubridate' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#Membangkitkan data
##skenario
Y : Pertumbuhan jumlah Penjualan di Toko HABIBI di tahun 2023
X1 : pengunjung toko (perhari dalam setahun)
X2 : kualitas pelayanan toko (ratig 1- 4)
#Jumlah hari dalam setahun
hari <- 365#Membangkitkan data pengunjung toko (X1) X1 : data pengunjung toko
set.seed(123)
X1 <- round(rnorm(hari, 70, 15))
X1## [1] 62 67 93 71 72 96 77 51 60 63 88 75 76 72 62 97 77 41
## [19] 81 63 54 67 55 59 61 45 83 72 53 89 76 66 83 83 82 80
## [37] 78 69 65 64 60 67 51 103 88 53 64 63 82 69 74 70 69 91
## [55] 67 93 47 79 72 73 76 62 65 55 54 75 77 71 84 101 63 35
## [73] 85 59 60 85 66 52 73 68 70 76 64 80 67 75 86 77 65 87
## [91] 85 78 74 61 90 61 103 93 66 55 59 74 66 65 56 69 58 45
## [109] 64 84 61 79 46 69 78 75 72 60 57 55 72 56 63 66 98 60
## [127] 74 71 56 69 92 77 71 64 39 87 48 81 99 48 81 66 46 47
## [145] 46 62 48 80 102 51 82 82 75 55 68 66 78 64 85 64 86 54
## [163] 51 119 64 74 80 63 78 76 67 71 69 102 59 54 71 75 77 63
## [181] 54 89 65 57 66 67 87 71 81 63 73 65 71 57 50 100 79 51
## [199] 61 52 103 90 66 78 64 63 58 61 95 69 72 74 88 62 55 95
## [217] 63 59 51 51 61 79 87 81 65 71 59 59 83 55 99 69 73 59
## [235] 61 50 67 76 75 58 58 62 92 53 67 99 68 50 60 77 64 62
## [253] 65 71 94 69 86 79 68 47 62 63 71 90 104 93 68 44 64 71
## [271] 83 84 80 49 83 63 73 71 76 70 45 81 76 66 72 72 73 95
## [289] 67 73 88 86 87 61 100 71 98 50 70 89 59 59 56 54 63 75
## [307] 40 73 89 101 90 81 44 61 65 81 68 51 95 84 74 88 50 80
## [325] 62 80 69 79 90 70 85 52 59 93 76 39 50 67 83 68 79 84
## [343] 95 71 69 44 71 61 55 67 85 40 64 72 57 75 76 70 33 109
## [361] 67 80 74 85 82#Membangkitkan data rate kualitas pelayanan toko (X2)
set.seed(123)
X2 <- round(rnorm(hari, 4, 0.5),2)
X2## [1] 3.72 3.88 4.78 4.04 4.06 4.86 4.23 3.37 3.66 3.78 4.61 4.18 4.20 4.06 3.72
## [16] 4.89 4.25 3.02 4.35 3.76 3.47 3.89 3.49 3.64 3.69 3.16 4.42 4.08 3.43 4.63
## [31] 4.21 3.85 4.45 4.44 4.41 4.34 4.28 3.97 3.85 3.81 3.65 3.90 3.37 5.08 4.60
## [46] 3.44 3.80 3.77 4.39 3.96 4.13 3.99 3.98 4.68 3.89 4.76 3.23 4.29 4.06 4.11
## [61] 4.19 3.75 3.83 3.49 3.46 4.15 4.22 4.03 4.46 5.03 3.75 2.85 4.50 3.65 3.66
## [76] 4.51 3.86 3.39 4.09 3.93 4.00 4.19 3.81 4.32 3.89 4.17 4.55 4.22 3.84 4.57
## [91] 4.50 4.27 4.12 3.69 4.68 3.70 5.09 4.77 3.88 3.49 3.64 4.13 3.88 3.83 3.52
## [106] 3.98 3.61 3.17 3.81 4.46 3.71 4.30 3.19 3.97 4.26 4.15 4.05 3.68 3.58 3.49
## [121] 4.06 3.53 3.75 3.87 4.92 3.67 4.12 4.04 3.52 3.96 4.72 4.23 4.02 3.79 2.97
## [136] 4.57 3.27 4.37 4.95 3.28 4.35 3.87 3.21 3.24 3.20 3.73 3.27 4.34 5.05 3.36
## [151] 4.39 4.38 4.17 3.50 3.94 3.86 4.28 3.81 4.49 3.81 4.53 3.48 3.37 5.62 3.79
## [166] 4.15 4.32 3.76 4.26 4.18 3.89 4.03 3.98 5.06 3.63 3.45 4.02 4.16 4.22 3.77
## [181] 3.47 4.63 3.83 3.57 3.88 3.90 4.55 4.04 4.38 3.75 4.11 3.84 4.05 3.55 3.34
## [196] 5.00 4.30 3.37 3.69 3.41 5.10 4.66 3.87 4.27 3.79 3.76 3.61 3.70 4.83 3.97
## [211] 4.06 4.12 4.62 3.74 3.50 4.84 3.78 3.64 3.38 3.36 3.71 4.31 4.55 4.35 3.82
## [226] 4.03 3.65 3.64 4.44 3.49 4.98 3.95 4.11 3.63 3.71 3.34 3.91 4.21 4.16 3.61
## [241] 3.61 3.75 4.75 3.43 3.91 4.95 3.95 3.32 3.67 4.24 3.81 3.72 3.83 4.05 4.80
## [256] 3.96 4.54 4.32 3.94 3.23 3.74 3.76 4.02 4.65 5.15 4.77 3.93 3.12 3.81 4.04
## [271] 4.42 4.48 4.34 3.30 4.42 3.78 4.09 4.04 4.21 4.01 3.17 4.37 4.19 3.87 4.06
## [286] 4.07 4.11 4.82 3.89 4.08 4.58 4.53 4.57 3.71 5.00 4.03 4.93 3.32 4.01 4.62
## [301] 3.64 3.62 3.53 3.47 3.78 4.17 2.99 4.11 4.62 5.02 4.65 4.38 3.14 3.70 3.82
## [316] 4.35 3.95 3.37 4.84 4.46 4.12 4.61 3.33 4.33 3.74 4.34 3.97 4.32 4.67 4.00
## [331] 4.51 3.41 3.64 4.76 4.19 2.97 3.32 3.90 4.43 3.95 4.31 4.48 4.84 4.03 3.97
## [346] 3.12 4.05 3.71 3.51 3.91 4.51 3.00 3.79 4.06 3.55 4.17 4.21 3.98 2.77 5.29
## [361] 3.90 4.33 4.14 4.51 4.41#Membangkitkan data jumlah penjualan
Y <- round(0.5*X1+2*X2+rnorm(hari, 50, 7))
Y## [1] 87 94 99 100 91 125 85 79 93 93 99 89 97 94 76 109 98 78
## [19] 92 92 94 94 77 84 90 74 85 100 78 100 107 85 106 92 97 98
## [37] 89 88 90 94 76 89 88 108 105 87 91 94 99 90 77 92 95 109
## [55] 87 118 82 99 103 90 93 105 90 96 74 94 100 96 94 111 81 78
## [73] 109 71 96 93 94 87 93 87 94 99 96 84 80 106 109 100 95 109
## [91] 83 105 92 89 102 94 109 110 88 77 95 100 103 91 93 106 84 70
## [109] 88 99 89 110 77 95 96 96 96 97 87 89 100 91 85 102 106 87
## [127] 105 103 77 86 96 98 95 92 79 98 74 106 115 70 99 84 65 81
## [145] 79 88 82 105 113 78 95 99 98 77 91 97 97 85 100 97 106 93
## [163] 83 124 86 100 95 79 98 110 97 102 95 107 85 82 90 101 89 95
## [181] 90 95 95 103 87 97 97 101 101 101 85 90 90 84 77 104 102 75
## [199] 98 75 112 108 95 95 90 96 76 82 109 89 104 100 104 78 85 105
## [217] 88 79 86 75 86 101 97 103 81 74 90 93 98 88 101 92 81 95
## [235] 101 89 91 96 85 92 85 84 96 81 85 107 83 93 87 105 71 85
## [253] 85 85 117 83 104 104 93 74 95 90 86 95 127 101 79 82 92 84
## [271] 87 100 107 86 97 83 97 95 101 90 85 93 94 96 101 81 95 111
## [289] 81 98 97 109 106 93 111 100 119 95 93 88 87 88 84 88 96 92
## [307] 74 98 100 111 91 91 69 82 85 102 99 77 100 94 92 102 85 96
## [325] 74 98 101 106 99 82 119 82 86 109 85 70 71 86 101 82 102 103
## [343] 101 95 98 86 92 87 84 101 109 82 88 97 88 89 84 97 61 115
## [361] 93 103 97 96 107#Memvisualisasi Data
##Plot Penjualan vs Pengunjung Toko
ggplot(data = data.frame(Y, X1), aes(x = X1, y = Y)) +
geom_point() +
labs(x = "Pengunjung Toko (perhari)", y = "Pertumbuhan Jumlah Penjualan")
##Plot Penjualan vs Kualitas Pelayanan Toko
ggplot(data = data.frame(Y, X2), aes(x = X2, y = Y)) +
geom_point() +
labs(x = "Kualitas Pelayanan Toko (rating 1-4)", y = "Pertumbuhan Jumlah Penjualan")
#Analisis Regresi Linear
model <- lm(Y ~ X1 + X2)
summary(model)##
## Call:
## lm(formula = Y ~ X1 + X2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.1216 -4.6087 0.4522 4.8197 18.0019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -34.418 58.582 -0.588 0.557
## X1 -1.194 1.166 -1.024 0.306
## X2 52.684 35.038 1.504 0.134
##
## Residual standard error: 6.837 on 362 degrees of freedom
## Multiple R-squared: 0.5874, Adjusted R-squared: 0.5851
## F-statistic: 257.7 on 2 and 362 DF, p-value: < 2.2e-16#Kesimpulan hasil analisis Berdasarkan hasil analisis regresi yang dipaparkan, dapat disimpulkan bahwa:
- Model regresi secara keseluruhan signifikan secara statistik (p-value < 2.2e-16). Ini menunjukkan bahwa ada hubungan linier antara variabel dependen (penjualan) dan setidaknya satu variabel independen (pengunjung toko atau kualitas pelayanan).
- Pengaruh individual variabel independen terhadap penjualan tidak
signifikan secara statistik.
- Koefisien regresi untuk pengunjung toko (X1) menunjukkan hubungan positif, namun nilai p-value (0.613) lebih besar dari 0.05.
- Koefisien regresi untuk kualitas pelayanan (X2) menunjukkan hubungan negatif, namun nilai p-value (0.960) jauh lebih besar dari 0.05 dan nilai standar errornya sangat besar.
- Model regresi ini menjelaskan 56.37% variasi dalam penjualan (R-squared = 0.5637)