Analisis Pengaruh Karakteristik Penumpang dan Layanan Penerbangan terhadap Tingkat Seat Comfort Menggunakan Analisis Diskriminan dan Ordinal Logistic Regression
Kevin Foreman Hutahaean (24031554095)
Mutiara Nur Fadhilatun Ni’mah (24031554116)
Athiyyatus Salisah (24031554223)
2026-05-09
Tugas Modul 2 - ANALISIS DISKRIMINAN & ORDINAL LOGISTIC REGRESSION
Dataset –> Airline Passenger Satisfaction Link Data –> https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction
library(MASS)## Warning: package 'MASS' was built under R version 4.5.3library(car)## Warning: package 'car' was built under R version 4.5.3## Loading required package: carDatalibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.5.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.3## Warning: package 'dplyr' was built under R version 4.5.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.1 ✔ tidyr 1.3.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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## ✖ dplyr::select() masks MASS::select()
## ✖ purrr::some() masks car::some()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(corrplot)## Warning: package 'corrplot' was built under R version 4.5.3## corrplot 0.95 loadedlibrary(gridExtra)## Warning: package 'gridExtra' was built under R version 4.5.3##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combinelibrary(nnet)## Warning: package 'nnet' was built under R version 4.5.3library(lmtest)## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(haven)## Warning: package 'haven' was built under R version 4.5.3library(caret)## Warning: package 'caret' was built under R version 4.5.3## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftlibrary(dplyr)df <- read.csv("train.csv")head(df)## X id Gender Customer.Type Age Type.of.Travel Class
## 1 0 70172 Male Loyal Customer 13 Personal Travel Eco Plus
## 2 1 5047 Male disloyal Customer 25 Business travel Business
## 3 2 110028 Female Loyal Customer 26 Business travel Business
## 4 3 24026 Female Loyal Customer 25 Business travel Business
## 5 4 119299 Male Loyal Customer 61 Business travel Business
## 6 5 111157 Female Loyal Customer 26 Personal Travel Eco
## Flight.Distance Inflight.wifi.service Departure.Arrival.time.convenient
## 1 460 3 4
## 2 235 3 2
## 3 1142 2 2
## 4 562 2 5
## 5 214 3 3
## 6 1180 3 4
## Ease.of.Online.booking Gate.location Food.and.drink Online.boarding
## 1 3 1 5 3
## 2 3 3 1 3
## 3 2 2 5 5
## 4 5 5 2 2
## 5 3 3 4 5
## 6 2 1 1 2
## Seat.comfort Inflight.entertainment On.board.service Leg.room.service
## 1 5 5 4 3
## 2 1 1 1 5
## 3 5 5 4 3
## 4 2 2 2 5
## 5 5 3 3 4
## 6 1 1 3 4
## Baggage.handling Checkin.service Inflight.service Cleanliness
## 1 4 4 5 5
## 2 3 1 4 1
## 3 4 4 4 5
## 4 3 1 4 2
## 5 4 3 3 3
## 6 4 4 4 1
## Departure.Delay.in.Minutes Arrival.Delay.in.Minutes satisfaction
## 1 25 18 neutral or dissatisfied
## 2 1 6 neutral or dissatisfied
## 3 0 0 satisfied
## 4 11 9 neutral or dissatisfied
## 5 0 0 satisfied
## 6 0 0 neutral or dissatisfiedcolSums(is.na(df))## X id
## 0 0
## Gender Customer.Type
## 0 0
## Age Type.of.Travel
## 0 0
## Class Flight.Distance
## 0 0
## Inflight.wifi.service Departure.Arrival.time.convenient
## 0 0
## Ease.of.Online.booking Gate.location
## 0 0
## Food.and.drink Online.boarding
## 0 0
## Seat.comfort Inflight.entertainment
## 0 0
## On.board.service Leg.room.service
## 0 0
## Baggage.handling Checkin.service
## 0 0
## Inflight.service Cleanliness
## 0 0
## Departure.Delay.in.Minutes Arrival.Delay.in.Minutes
## 0 310
## satisfaction
## 0str(df)## 'data.frame': 103904 obs. of 25 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ id : int 70172 5047 110028 24026 119299 111157 82113 96462 79485 65725 ...
## $ Gender : chr "Male" "Male" "Female" "Female" ...
## $ Customer.Type : chr "Loyal Customer" "disloyal Customer" "Loyal Customer" "Loyal Customer" ...
## $ Age : int 13 25 26 25 61 26 47 52 41 20 ...
## $ Type.of.Travel : chr "Personal Travel" "Business travel" "Business travel" "Business travel" ...
## $ Class : chr "Eco Plus" "Business" "Business" "Business" ...
## $ Flight.Distance : int 460 235 1142 562 214 1180 1276 2035 853 1061 ...
## $ Inflight.wifi.service : int 3 3 2 2 3 3 2 4 1 3 ...
## $ Departure.Arrival.time.convenient: int 4 2 2 5 3 4 4 3 2 3 ...
## $ Ease.of.Online.booking : int 3 3 2 5 3 2 2 4 2 3 ...
## $ Gate.location : int 1 3 2 5 3 1 3 4 2 4 ...
## $ Food.and.drink : int 5 1 5 2 4 1 2 5 4 2 ...
## $ Online.boarding : int 3 3 5 2 5 2 2 5 3 3 ...
## $ Seat.comfort : int 5 1 5 2 5 1 2 5 3 3 ...
## $ Inflight.entertainment : int 5 1 5 2 3 1 2 5 1 2 ...
## $ On.board.service : int 4 1 4 2 3 3 3 5 1 2 ...
## $ Leg.room.service : int 3 5 3 5 4 4 3 5 2 3 ...
## $ Baggage.handling : int 4 3 4 3 4 4 4 5 1 4 ...
## $ Checkin.service : int 4 1 4 1 3 4 3 4 4 4 ...
## $ Inflight.service : int 5 4 4 4 3 4 5 5 1 3 ...
## $ Cleanliness : int 5 1 5 2 3 1 2 4 2 2 ...
## $ Departure.Delay.in.Minutes : int 25 1 0 11 0 0 9 4 0 0 ...
## $ Arrival.Delay.in.Minutes : num 18 6 0 9 0 0 23 0 0 0 ...
## $ satisfaction : chr "neutral or dissatisfied" "neutral or dissatisfied" "satisfied" "neutral or dissatisfied" ...set.seed(123)
df_model <- df %>%
select(
Seat.comfort,
Age,
Flight.Distance,
Departure.Delay.in.Minutes,
Arrival.Delay.in.Minutes,
Class,
Type.of.Travel,
Gender,
Customer.Type,
Food.and.drink,
Inflight.wifi.service
) %>%
na.omit() %>%
slice_sample(n = 10000)colSums(is.na(df_model))## Seat.comfort Age
## 0 0
## Flight.Distance Departure.Delay.in.Minutes
## 0 0
## Arrival.Delay.in.Minutes Class
## 0 0
## Type.of.Travel Gender
## 0 0
## Customer.Type Food.and.drink
## 0 0
## Inflight.wifi.service
## 0Handling Outlier
detect_outliers <- function(x, var_name) {
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower <- Q1 - 1.5 * IQR_val
upper <- Q3 + 1.5 * IQR_val
n_out <- sum(x < lower | x > upper, na.rm = TRUE)
data.frame(
Variabel = var_name,
Batas_Bawah = round(lower, 3), Batas_Atas = round(upper, 3),
Jumlah_Outlier = n_out,
Persen = round(n_out / length(x) * 100, 2))
}
continuous_vars_for_outlier_check <- df_model %>%
select(Flight.Distance, Departure.Delay.in.Minutes, Arrival.Delay.in.Minutes)
outlier_tbl <- do.call(rbind,
lapply(names(continuous_vars_for_outlier_check), function(v_name) {
detect_outliers(continuous_vars_for_outlier_check[[v_name]], v_name)
}))
rownames(outlier_tbl) <- NULL
knitr::kable(outlier_tbl,
caption = "Deteksi Outlier")| Variabel | Batas_Bawah | Batas_Atas | Jumlah_Outlier | Persen |
|---|---|---|---|---|
| Flight.Distance | -1530.25 | 3635.75 | 293 | 2.93 |
| Departure.Delay.in.Minutes | -18.00 | 30.00 | 1368 | 13.68 |
| Arrival.Delay.in.Minutes | -19.50 | 32.50 | 1326 | 13.26 |
plots_before <- lapply(names(continuous_vars_for_outlier_check), function(v) {
ggplot(continuous_vars_for_outlier_check, aes(y = .data[[v]])) +
geom_boxplot(fill = "#4292C6", color = "#084594",
outlier.color = "red", outlier.size = 2) +
labs(title = v) +
theme_minimal(base_size = 14) +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
})
grid.arrange(grobs = plots_before, ncol = 2,
top = "Boxplot")
cap_outliers <- function(x) {
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower <- Q1 - 1.5 * IQR_val
upper <- Q3 + 1.5 * IQR_val
x[x < lower] <- lower
x[x > upper] <- upper
x
}
cols_to_cap <- c("Flight.Distance", "Departure.Delay.in.Minutes", "Arrival.Delay.in.Minutes")
df_model[cols_to_cap] <- lapply(df_model[cols_to_cap], cap_outliers)No Outlier
hasil_perbandingan <- do.call(rbind, lapply(cols_to_cap, function(v_name) {
# sebelum capping
out_before <- detect_outliers(continuous_vars_for_outlier_check[[v_name]], v_name)
# sesudah capping
out_after <- detect_outliers(df_model[[v_name]], v_name)
data.frame(
Variabel = v_name,
Outlier_Sebelum = out_before$Jumlah_Outlier,
Outlier_Sesudah = out_after$Jumlah_Outlier
)
}))knitr::kable(hasil_perbandingan,
caption = "Perbandingan Outlier Sebelum dan Sesudah Capping")| Variabel | Outlier_Sebelum | Outlier_Sesudah |
|---|---|---|
| Flight.Distance | 293 | 0 |
| Departure.Delay.in.Minutes | 1368 | 0 |
| Arrival.Delay.in.Minutes | 1326 | 0 |
plots_after <- lapply(cols_to_cap, function(v) {
ggplot(df_model, aes(y = .data[[v]])) +
geom_boxplot(fill = "#9ecae1", color = "#08519c",
outlier.color = "red", outlier.size = 2) +
labs(title = v) +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
})
grid.arrange(grobs = plots_after, ncol = 2,
top = "Boxplot (Sesudah Capping)")
df_model <- df_model[!duplicated(df_model), ]df_model$Departure.Delay.in.Minutes <- df_model$Departure.Delay.in.Minutes + 1
df_model$Arrival.Delay.in.Minutes <- df_model$Arrival.Delay.in.Minutes + 1df_model$Seat.comfort <- as.ordered(df_model$Seat.comfort)
df_model$y_num <- as.numeric(df_model$Seat.comfort)df_model <- df_model %>%
mutate(
Class = as.factor(Class),
Type.of.Travel = as.factor(Type.of.Travel),
Gender = as.factor(Gender),
Customer.Type = as.factor(Customer.Type)
)df_model <- df_model %>%
mutate(
Food.and.drink = as.factor(Food.and.drink),
Inflight.wifi.service = as.factor(Inflight.wifi.service)
)summary(df_model)## Seat.comfort Age Flight.Distance Departure.Delay.in.Minutes
## 1:1146 Min. : 7.0 Min. : 31 Min. : 1.00
## 2:1471 1st Qu.:27.0 1st Qu.: 407 1st Qu.: 1.00
## 3:1815 Median :40.0 Median : 842 Median : 1.00
## 4:3074 Mean :39.2 Mean :1166 Mean : 8.34
## 5:2490 3rd Qu.:50.0 3rd Qu.:1698 3rd Qu.:13.00
## Max. :80.0 Max. :3636 Max. :31.00
## Arrival.Delay.in.Minutes Class Type.of.Travel Gender
## Min. : 1.000 Business:4720 Business travel:6923 Female:5069
## 1st Qu.: 1.000 Eco :4599 Personal Travel:3073 Male :4927
## Median : 1.000 Eco Plus: 677
## Mean : 8.853
## 3rd Qu.:14.000
## Max. :33.500
## Customer.Type Food.and.drink Inflight.wifi.service y_num
## disloyal Customer:1864 0: 11 0: 271 Min. :1.000
## Loyal Customer :8132 1:1221 1:1700 1st Qu.:2.000
## 2:2135 2:2489 Median :4.000
## 3:2206 3:2531 Mean :3.429
## 4:2357 4:1926 3rd Qu.:4.000
## 5:2066 5:1079 Max. :5.000library(psych)## Warning: package 'psych' was built under R version 4.5.3##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:car':
##
## logitdescribe(df_model[, c("Age", "Flight.Distance", "Departure.Delay.in.Minutes", "Arrival.Delay.in.Minutes")])## vars n mean sd median trimmed mad min
## Age 1 9996 39.20 14.96 40 39.16 17.79 7
## Flight.Distance 2 9996 1166.34 970.92 842 1024.69 757.61 31
## Departure.Delay.in.Minutes 3 9996 8.34 11.16 1 6.43 0.00 1
## Arrival.Delay.in.Minutes 4 9996 8.85 11.89 1 6.76 0.00 1
## max range skew kurtosis se
## Age 80.00 73.00 0.02 -0.71 0.15
## Flight.Distance 3635.75 3604.75 1.09 0.15 9.71
## Departure.Delay.in.Minutes 31.00 30.00 1.23 -0.16 0.11
## Arrival.Delay.in.Minutes 33.50 32.50 1.25 -0.06 0.12Uji Asumsi Ordinal Logistic Regression
# Transformasi variabel Age & Arrival Delay in Minutes
df_model$Age_log <- log(df_model$Age)
df_model$Arrival.Delay.in.Minutes_log <- log(df_model$Arrival.Delay.in.Minutes)Interpretasi Variabel
model_olr <- polr(
Seat.comfort ~ Age_log + Flight.Distance +
Departure.Delay.in.Minutes +
Arrival.Delay.in.Minutes_log +
Class + Type.of.Travel +
Gender + Customer.Type,
data = df_model,
Hess = TRUE
)
summary(model_olr)## Call:
## polr(formula = Seat.comfort ~ Age_log + Flight.Distance + Departure.Delay.in.Minutes +
## Arrival.Delay.in.Minutes_log + Class + Type.of.Travel + Gender +
## Customer.Type, data = df_model, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## Age_log 4.709e-01 1.820e-02 25.8678
## Flight.Distance 3.808e-05 2.545e-05 1.4958
## Departure.Delay.in.Minutes -2.403e-03 2.605e-03 -0.9222
## Arrival.Delay.in.Minutes_log -3.378e-02 2.033e-02 -1.6616
## ClassEco -5.264e-01 4.736e-02 -11.1163
## ClassEco Plus -6.324e-01 7.704e-02 -8.2084
## Type.of.TravelPersonal Travel -1.853e-01 5.232e-02 -3.5417
## GenderMale -1.448e-01 3.566e-02 -4.0612
## Customer.TypeLoyal Customer 5.757e-01 5.589e-02 10.3021
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.4155 0.0091 -45.8215
## 2|3 0.6413 0.0269 23.8564
## 3|4 1.5127 0.0317 47.7605
## 4|5 2.9221 0.0378 77.3862
##
## Residual Deviance: 30145.72
## AIC: 30171.72variabel usia, keterlambatan kedatangan, kelas penerbangan, tipe perjalanan, gender, dan tipe pelanggan berpengaruh signifikan terhadap tingkat kenyamanan kursi (Seat Comfort). Sementara itu, jarak penerbangan dan keterlambatan keberangkatan tidak menunjukkan pengaruh signifikan.
Linearity (p-value > 0.05)
model_lin <- glm(
y_num ~ Age_log + Flight.Distance +
Departure.Delay.in.Minutes +
Arrival.Delay.in.Minutes_log +
I(Age_log * log(Age_log)) +
I(Flight.Distance * log(Flight.Distance)) +
I(Departure.Delay.in.Minutes * log(Departure.Delay.in.Minutes + 1)) +
I(Arrival.Delay.in.Minutes_log * log(Arrival.Delay.in.Minutes_log + 1)),
data = df_model
)
summary(model_lin)##
## Call:
## glm(formula = y_num ~ Age_log + Flight.Distance + Departure.Delay.in.Minutes +
## Arrival.Delay.in.Minutes_log + I(Age_log * log(Age_log)) +
## I(Flight.Distance * log(Flight.Distance)) + I(Departure.Delay.in.Minutes *
## log(Departure.Delay.in.Minutes + 1)) + I(Arrival.Delay.in.Minutes_log *
## log(Arrival.Delay.in.Minutes_log + 1)), data = df_model)
##
## Coefficients:
## Estimate
## (Intercept) 2.615e+00
## Age_log -2.200e-01
## Flight.Distance -2.973e-04
## Departure.Delay.in.Minutes 1.415e-02
## Arrival.Delay.in.Minutes_log 4.922e-02
## I(Age_log * log(Age_log)) 3.231e-01
## I(Flight.Distance * log(Flight.Distance)) 5.580e-05
## I(Departure.Delay.in.Minutes * log(Departure.Delay.in.Minutes + 1)) -4.238e-03
## I(Arrival.Delay.in.Minutes_log * log(Arrival.Delay.in.Minutes_log + 1)) -5.469e-02
## Std. Error
## (Intercept) 8.966e-01
## Age_log 5.992e-01
## Flight.Distance 3.177e-04
## Departure.Delay.in.Minutes 1.573e-02
## Arrival.Delay.in.Minutes_log 7.245e-02
## I(Age_log * log(Age_log)) 2.725e-01
## I(Flight.Distance * log(Flight.Distance)) 3.835e-05
## I(Departure.Delay.in.Minutes * log(Departure.Delay.in.Minutes + 1)) 4.532e-03
## I(Arrival.Delay.in.Minutes_log * log(Arrival.Delay.in.Minutes_log + 1)) 5.344e-02
## t value
## (Intercept) 2.916
## Age_log -0.367
## Flight.Distance -0.936
## Departure.Delay.in.Minutes 0.900
## Arrival.Delay.in.Minutes_log 0.679
## I(Age_log * log(Age_log)) 1.186
## I(Flight.Distance * log(Flight.Distance)) 1.455
## I(Departure.Delay.in.Minutes * log(Departure.Delay.in.Minutes + 1)) -0.935
## I(Arrival.Delay.in.Minutes_log * log(Arrival.Delay.in.Minutes_log + 1)) -1.023
## Pr(>|t|)
## (Intercept) 0.00355
## Age_log 0.71355
## Flight.Distance 0.34943
## Departure.Delay.in.Minutes 0.36827
## Arrival.Delay.in.Minutes_log 0.49690
## I(Age_log * log(Age_log)) 0.23578
## I(Flight.Distance * log(Flight.Distance)) 0.14567
## I(Departure.Delay.in.Minutes * log(Departure.Delay.in.Minutes + 1)) 0.34973
## I(Arrival.Delay.in.Minutes_log * log(Arrival.Delay.in.Minutes_log + 1)) 0.30610
##
## (Intercept) **
## Age_log
## Flight.Distance
## Departure.Delay.in.Minutes
## Arrival.Delay.in.Minutes_log
## I(Age_log * log(Age_log))
## I(Flight.Distance * log(Flight.Distance))
## I(Departure.Delay.in.Minutes * log(Departure.Delay.in.Minutes + 1))
## I(Arrival.Delay.in.Minutes_log * log(Arrival.Delay.in.Minutes_log + 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.638498)
##
## Null deviance: 17247 on 9995 degrees of freedom
## Residual deviance: 16364 on 9987 degrees of freedom
## AIC: 33314
##
## Number of Fisher Scoring iterations: 2Berdasarkan uji Box-Tidwell, seluruh variabel numerik memiliki nilai p-value > 0.05 pada komponen interaksi dengan logaritmanya. Hal ini menunjukkan bahwa hubungan antara prediktor kontinu dengan logit bersifat linear, sehingga asumsi linearitas pada model Ordinal Logistic Regression telah terpenuhi.
Independence Of Observation
names(df_model)## [1] "Seat.comfort" "Age"
## [3] "Flight.Distance" "Departure.Delay.in.Minutes"
## [5] "Arrival.Delay.in.Minutes" "Class"
## [7] "Type.of.Travel" "Gender"
## [9] "Customer.Type" "Food.and.drink"
## [11] "Inflight.wifi.service" "y_num"
## [13] "Age_log" "Arrival.Delay.in.Minutes_log"sum(duplicated(df_model))## [1] 0nrow(df)## [1] 103904Berdasarkan pemeriksaan terhadap data, tidak ditemukan observasi duplikat (sum duplicated = 0), sehingga setiap pengamatan dianggap independen. Dengan demikian, asumsi independence of observation pada model Ordinal Logistic Regression telah terpenuhi.
No Multicollinearity (GVIF<10)
cont_vars <- df_model %>%
dplyr::select(Age, Flight.Distance, Arrival.Delay.in.Minutes)
cor_mat <- cor(cont_vars, use = "complete.obs")
corrplot(cor_mat,
method = "color",
type = "full",
addCoef.col = "black",
number.cex = 0.8,
tl.cex = 0.9,
tl.col = "black",
col = colorRampPalette(c("#2166AC", "white", "#B2182B"))(200),
title = "Correlation Matrix Variabel Kontinu",
mar = c(0, 0, 2, 0))
Semua nilai korelasi antar variabel numerik (independen) tersebut berada jauh di bawah ambang batas kritis. Secara umum, multikolinearitas dianggap serius jika nilai korelasi antar variabel independen melebihi 0.7 atau 0.8. Dari Correlation matrix diatas, variabel numerik berkorelasi < 0.1 sehingga asumsi No Multicollinearity terpenuhi.
model_vif <- lm(
y_num ~ Age + Flight.Distance +
Arrival.Delay.in.Minutes +
Class + Type.of.Travel +
Gender + Customer.Type +
Food.and.drink + Inflight.wifi.service,
data = df_model
)vif(model_vif)## GVIF Df GVIF^(1/(2*Df))
## Age 1.116954 1 1.056860
## Flight.Distance 1.349677 1 1.161756
## Arrival.Delay.in.Minutes 1.007879 1 1.003932
## Class 1.819921 2 1.161484
## Type.of.Travel 1.841931 1 1.357178
## Gender 1.003236 1 1.001617
## Customer.Type 1.451381 1 1.204733
## Food.and.drink 1.129553 5 1.012257
## Inflight.wifi.service 1.165839 5 1.015462Berdasarkan hasil uji Variance Inflation Factor (VIF), seluruh variabel independen memiliki nilai GVIF yang berkisar antara 1,00 hingga 1,84. Karena nilai tersebut jauh di bawah ambang batas 10, maka dapat disimpulkan bahwa asumsi no-multicollinearity terpenuhi. Model tidak mengandung hubungan linear yang kuat antar variabel independen, sehingga estimasi parameter yang dihasilkan bersifat valid.
ORDINAL LOGISTIC REGRESSION
model_ordinallogisticregression <- polr(
Seat.comfort ~ Age_log +
Flight.Distance +
Departure.Delay.in.Minutes +
Arrival.Delay.in.Minutes_log +
Class +
Type.of.Travel +
Gender +
Customer.Type +
Food.and.drink +
Inflight.wifi.service,
data = df_model,
Hess = TRUE
)summary(model_ordinallogisticregression)## Call:
## polr(formula = Seat.comfort ~ Age_log + Flight.Distance + Departure.Delay.in.Minutes +
## Arrival.Delay.in.Minutes_log + Class + Type.of.Travel + Gender +
## Customer.Type + Food.and.drink + Inflight.wifi.service, data = df_model,
## Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## Age_log 5.658e-01 1.955e-02 28.9429
## Flight.Distance 4.027e-05 2.588e-05 1.5557
## Departure.Delay.in.Minutes -4.183e-03 2.698e-03 -1.5503
## Arrival.Delay.in.Minutes_log -9.397e-03 2.107e-02 -0.4460
## ClassEco -5.105e-01 4.858e-02 -10.5087
## ClassEco Plus -6.649e-01 7.763e-02 -8.5646
## Type.of.TravelPersonal Travel -7.592e-02 5.378e-02 -1.4118
## GenderMale -2.167e-01 3.716e-02 -5.8315
## Customer.TypeLoyal Customer 4.950e-01 5.597e-02 8.8454
## Food.and.drink1 -2.974e+00 5.811e-02 -51.1850
## Food.and.drink2 -8.266e-01 3.785e-02 -21.8409
## Food.and.drink3 -1.180e-01 3.611e-02 -3.2670
## Food.and.drink4 6.857e-01 3.681e-02 18.6287
## Food.and.drink5 2.099e+00 4.702e-02 44.6442
## Inflight.wifi.service1 -1.919e-01 4.145e-02 -4.6303
## Inflight.wifi.service2 -3.072e-01 3.568e-02 -8.6091
## Inflight.wifi.service3 -2.625e-01 3.544e-02 -7.4078
## Inflight.wifi.service4 -2.784e-02 3.865e-02 -0.7204
## Inflight.wifi.service5 1.645e-01 5.135e-02 3.2026
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.0440 0.0092 -113.7470
## 2|3 0.4573 0.0374 12.2348
## 3|4 1.6625 0.0445 37.3873
## 4|5 3.5277 0.0533 66.2369
##
## Residual Deviance: 25603.67
## AIC: 25649.67ctable <- coef(summary(model_ordinallogisticregression))
p_value <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
result <- cbind(
Estimate = ctable[, "Value"],
"Odds Ratio" = exp(ctable[, "Value"]),
"p value" = p_value
)
round(result, 4)## Estimate Odds Ratio p value
## Age_log 0.5658 1.7608 0.0000
## Flight.Distance 0.0000 1.0000 0.1198
## Departure.Delay.in.Minutes -0.0042 0.9958 0.1211
## Arrival.Delay.in.Minutes_log -0.0094 0.9906 0.6556
## ClassEco -0.5105 0.6002 0.0000
## ClassEco Plus -0.6649 0.5143 0.0000
## Type.of.TravelPersonal Travel -0.0759 0.9269 0.1580
## GenderMale -0.2167 0.8052 0.0000
## Customer.TypeLoyal Customer 0.4950 1.6406 0.0000
## Food.and.drink1 -2.9745 0.0511 0.0000
## Food.and.drink2 -0.8266 0.4375 0.0000
## Food.and.drink3 -0.1180 0.8887 0.0011
## Food.and.drink4 0.6857 1.9852 0.0000
## Food.and.drink5 2.0990 8.1582 0.0000
## Inflight.wifi.service1 -0.1919 0.8254 0.0000
## Inflight.wifi.service2 -0.3072 0.7355 0.0000
## Inflight.wifi.service3 -0.2625 0.7691 0.0000
## Inflight.wifi.service4 -0.0278 0.9725 0.4713
## Inflight.wifi.service5 0.1645 1.1788 0.0014
## 1|2 -1.0440 0.3520 0.0000
## 2|3 0.4573 1.5798 0.0000
## 3|4 1.6625 5.2724 0.0000
## 4|5 3.5277 34.0454 0.0000model_null <- polr(
Seat.comfort ~ 1,
data = df_model,
Hess = TRUE
)anova(model_null, model_ordinallogisticregression)## Likelihood ratio tests of ordinal regression models
##
## Response: Seat.comfort
## Model
## 1 1
## 2 Age_log + Flight.Distance + Departure.Delay.in.Minutes + Arrival.Delay.in.Minutes_log + Class + Type.of.Travel + Gender + Customer.Type + Food.and.drink + Inflight.wifi.service
## Resid. df Resid. Dev Test Df LR stat. Pr(Chi)
## 1 9992 30966.45
## 2 9973 25603.67 1 vs 2 19 5362.782 0AIC(model_null, model_ordinallogisticregression)## df AIC
## model_null 4 30974.45
## model_ordinallogisticregression 23 25649.67library(effects)## Warning: package 'effects' was built under R version 4.5.3## Use the command
## lattice::trellis.par.set(effectsTheme())
## to customize lattice options for effects plots.
## See ?effectsTheme for details.plot(effect("Age_log", model_ordinallogisticregression),
style = "stacked",
main = "Peluang Rating Kepuasan Berdasarkan Usia")
plot(effect("Class", model_ordinallogisticregression),
style = "stacked",
main = "Peluang Rating Kepuasan Berdasarkan Class")
plot(effect("Food.and.drink", model_ordinallogisticregression),
style = "stacked",
main = "Peluang Rating Kepuasan Berdasarkan Kualitas Makanan & Minuman")
plot(effect("Customer.Type", model_ordinallogisticregression),
style = "stacked",
main = "Peluang Rating Kepuasan Berdasarkan Tipe Pelanggan")
plot(effect("Gender", model_ordinallogisticregression),
style = "stacked",
main = "Peluang Rating Kepuasan Berdasarkan Gender")
plot(effect("Inflight.wifi.service", model_ordinallogisticregression),
style = "stacked",
main = "Peluang Rating Kepuasan Berdasarkan Layanan wifi")
Analisis Diskriminan
df_model$Seat_Group <- ifelse(df_model$Seat.comfort <= 2,
"Tidak_Nyaman",
ifelse(df_model$Seat.comfort == 3,
"Netral",
"Nyaman"))
df_model$Seat_Group <- as.factor(df_model$Seat_Group)Uji Asumsi
df_da <- df_model %>%
dplyr::select(
Seat_Group,
Age_log,
Flight.Distance,
Departure.Delay.in.Minutes,
Arrival.Delay.in.Minutes_log
) %>%
na.omit()
df_da$Seat_Group <- as.factor(df_da$Seat_Group)Uji Normalitas Multivariat
num_var <- df_da %>%
dplyr::select(
Age_log,
Flight.Distance,
Departure.Delay.in.Minutes,
Arrival.Delay.in.Minutes_log
)
result <- MVN::mvn(
data = num_var,
mvn_test = "mardia"
)
result## $multivariate_normality
## Test Statistic p.value Method MVN
## 1 Mardia Skewness 8949.789 <0.001 asymptotic ✗ Not normal
## 2 Mardia Kurtosis 10.170 <0.001 asymptotic ✗ Not normal
##
## $univariate_normality
## Test Variable Statistic p.value Normality
## 1 Anderson-Darling Age_log 164.720 <0.001 ✗ Not normal
## 2 Anderson-Darling Flight.Distance 471.420 <0.001 ✗ Not normal
## 3 Anderson-Darling Departure.Delay.in.Minutes 1493.558 <0.001 ✗ Not normal
## 4 Anderson-Darling Arrival.Delay.in.Minutes_log 1192.140 <0.001 ✗ Not normal
##
## $descriptives
## Variable n Mean Std.Dev Median Min Max
## 1 Age_log 9996 3.578 0.458 3.689 1.946 4.382
## 2 Flight.Distance 9996 1166.340 970.920 842.000 31.000 3635.750
## 3 Departure.Delay.in.Minutes 9996 8.340 11.156 1.000 1.000 31.000
## 4 Arrival.Delay.in.Minutes_log 9996 1.159 1.431 0.000 0.000 3.512
## 25th 75th Skew Kurtosis
## 1 3.296 3.912 -1.025 4.046
## 2 407.000 1698.000 1.086 3.147
## 3 1.000 13.000 1.228 2.842
## 4 0.000 2.639 0.626 1.647
##
## $data
## Age_log Flight.Distance Departure.Delay.in.Minutes
## 1 3.761200 374.00 1
## 2 4.025352 95.00 4
## 3 3.951244 1217.00 1
## 4 3.688879 2011.00 1
## 5 3.218876 1607.00 31
## 6 3.688879 2917.00 1
## 7 2.995732 2133.00 1
## 8 3.784190 1511.00 1
## 9 2.397895 239.00 1
## 10 3.496508 451.00 11
## 11 3.496508 926.00 31
## 12 2.639057 862.00 31
## 13 3.784190 463.00 1
## 14 3.044522 694.00 1
## 15 3.367296 802.00 1
## 16 3.931826 475.00 31
## 17 3.637586 2880.00 1
## 18 3.135494 425.00 31
## 19 4.234107 643.00 31
## 20 3.218876 2172.00 1
## 21 4.043051 119.00 1
## 22 3.713572 265.00 31
## 23 3.433987 1705.00 1
## 24 3.871201 413.00 31
## 25 3.433987 872.00 31
## 26 4.060443 447.00 1
## 27 3.465736 196.00 1
## 28 4.077537 425.00 1
## 29 3.688879 3105.00 1
## 30 4.219508 414.00 1
## 31 3.178054 1068.00 31
## 32 3.663562 1734.00 1
## 33 3.637586 1678.00 1
## 34 3.663562 383.00 31
## 35 3.218876 661.00 1
## 36 3.891820 1910.00 1
## 37 2.772589 3400.00 31
## 38 3.091042 3600.00 1
## 39 3.295837 1744.00 1
## 40 3.583519 707.00 31
## 41 3.135494 305.00 1
## 42 3.761200 626.00 14
## 43 4.094345 2267.00 1
## 44 3.828641 954.00 1
## 45 2.708050 802.00 3
## 46 3.637586 1846.00 1
## 47 4.158883 1984.00 31
## 48 3.828641 3536.00 1
## 49 3.737670 1558.00 9
## 50 3.970292 630.00 1
## 51 4.077537 1096.00 1
## 52 4.077537 1531.00 23
## 53 4.025352 605.00 12
## 54 3.178054 337.00 26
## 55 3.610918 163.00 1
## 56 3.806662 2454.00 11
## 57 4.025352 1744.00 1
## 58 3.988984 2218.00 2
## 59 4.043051 883.00 3
## 60 2.944439 842.00 6
## 61 3.496508 312.00 12
## 62 2.302585 74.00 1
## 63 3.583519 166.00 1
## 64 4.204693 270.00 1
## 65 3.526361 2692.00 13
## 66 4.248495 417.00 7
## 67 4.219508 3534.00 1
## 68 3.135494 3034.00 4
## 69 3.891820 719.00 8
## 70 2.639057 1105.00 1
## 71 3.737670 1825.00 1
## 72 3.218876 344.00 2
## 73 3.465736 641.00 1
## 74 4.158883 390.00 17
## 75 3.988984 2611.00 1
## 76 3.091042 3546.00 1
## 77 3.688879 913.00 6
## 78 2.484907 406.00 1
## 79 3.891820 1916.00 1
## 80 3.526361 1400.00 1
## 81 3.713572 846.00 2
## 82 3.433987 899.00 1
## 83 3.871201 867.00 1
## 84 3.295837 364.00 31
## 85 3.218876 1035.00 18
## 86 3.465736 407.00 31
## 87 3.688879 337.00 14
## 88 3.178054 862.00 7
## 89 4.007333 2037.00 12
## 90 3.951244 3068.00 1
## 91 3.850148 421.00 31
## 92 3.784190 591.00 1
## 93 3.713572 2615.00 31
## 94 3.761200 948.00 12
## 95 3.761200 2072.00 31
## 96 3.871201 2236.00 4
## 97 2.772589 1532.00 1
## 98 3.663562 373.00 1
## 99 4.234107 842.00 1
## 100 3.688879 2758.00 1
## 101 3.433987 2068.00 20
## 102 3.044522 148.00 1
## 103 3.295837 152.00 1
## 104 3.663562 287.00 1
## 105 3.637586 1189.00 4
## 106 3.761200 937.00 9
## 107 3.496508 227.00 1
## 108 3.401197 2946.00 1
## 109 3.367296 584.00 1
## 110 3.891820 236.00 1
## 111 3.891820 67.00 1
## 112 3.610918 929.00 1
## 113 3.091042 331.00 11
## 114 3.465736 1262.00 1
## 115 3.583519 391.00 1
## 116 3.135494 551.00 6
## 117 2.772589 867.00 1
## 118 3.465736 600.00 1
## 119 3.091042 235.00 1
## 120 4.007333 3381.00 1
## 121 3.828641 1977.00 2
## 122 3.637586 247.00 1
## 123 3.583519 2552.00 1
## 124 3.583519 1660.00 30
## 125 3.637586 1212.00 1
## 126 4.174387 2772.00 18
## 127 4.127134 1013.00 1
## 128 4.204693 1355.00 1
## 129 3.688879 3526.00 9
## 130 3.135494 1025.00 1
## 131 3.091042 1158.00 1
## 132 2.708050 840.00 1
## 133 3.332205 672.00 1
## 134 3.178054 822.00 1
## 135 3.637586 589.00 31
## 136 4.143135 872.00 8
## 137 3.218876 373.00 1
## 138 4.094345 305.00 1
## 139 3.784190 2761.00 7
## 140 3.891820 307.00 1
## 141 3.688879 3635.75 1
## 142 3.295837 1744.00 3
## 143 4.060443 1447.00 1
## 144 3.135494 631.00 1
## 145 2.833213 190.00 31
## 146 3.806662 1390.00 2
## 147 3.258097 1000.00 26
## 148 2.995732 888.00 1
## 149 3.806662 618.00 1
## 150 3.258097 692.00 5
## 151 3.465736 3635.75 31
## 152 2.944439 331.00 1
## 153 4.043051 1013.00 31
## 154 3.828641 1121.00 1
## 155 3.663562 402.00 1
## 156 3.258097 1334.00 11
## 157 4.094345 3135.00 1
## 158 2.708050 201.00 31
## 159 3.828641 843.00 1
## 160 3.970292 2918.00 1
## 161 3.526361 451.00 1
## 162 2.890372 948.00 1
## 163 3.871201 1866.00 1
## 164 3.828641 307.00 3
## 165 3.806662 721.00 31
## 166 3.713572 515.00 1
## 167 3.332205 838.00 1
## 168 4.043051 466.00 31
## 169 3.610918 3053.00 1
## 170 4.127134 528.00 1
## 171 4.110874 370.00 1
## 172 3.295837 936.00 10
## 173 3.091042 909.00 5
## 174 4.060443 1554.00 21
## 175 3.850148 1504.00 31
## 176 3.828641 3291.00 1
## 177 3.258097 3344.00 1
## 178 3.465736 1747.00 31
## 179 3.496508 374.00 6
## 180 3.931826 2173.00 1
## 181 2.639057 157.00 1
## 182 3.988984 3635.75 1
## 183 3.091042 359.00 4
## 184 3.912023 515.00 1
## 185 3.465736 1726.00 1
## 186 3.610918 134.00 2
## 187 3.663562 373.00 9
## 188 4.025352 337.00 1
## 189 4.077537 833.00 1
## 190 3.737670 259.00 1
## 191 3.433987 819.00 1
## 192 3.218876 438.00 2
## 193 2.944439 3197.00 1
## 194 4.007333 3635.75 14
## 195 3.332205 2219.00 19
## 196 3.951244 2584.00 1
## 197 3.988984 566.00 31
## 198 3.663562 577.00 1
## 199 2.302585 584.00 19
## 200 4.077537 377.00 31
## 201 3.761200 2104.00 1
## 202 2.833213 3635.75 1
## 203 3.931826 1129.00 11
## 204 3.496508 1652.00 1
## 205 4.094345 539.00 31
## 206 3.663562 2329.00 1
## 207 3.688879 226.00 1
## 208 3.367296 2586.00 1
## 209 3.433987 3413.00 1
## 210 4.007333 2092.00 31
## 211 3.988984 2705.00 1
## 212 3.970292 140.00 1
## 213 3.806662 447.00 1
## 214 3.367296 604.00 1
## 215 3.332205 551.00 21
## 216 3.988984 3635.75 31
## 217 3.737670 351.00 1
## 218 3.178054 566.00 1
## 219 3.784190 163.00 1
## 220 3.218876 580.00 1
## 221 3.828641 3635.75 1
## 222 2.995732 1056.00 14
## 223 3.178054 532.00 1
## 224 3.761200 116.00 6
## 225 3.401197 2475.00 1
## 226 3.891820 834.00 1
## 227 4.219508 247.00 1
## 228 3.931826 364.00 1
## 229 2.397895 731.00 31
## 230 3.951244 387.00 1
## 231 4.174387 691.00 31
## 232 3.583519 67.00 1
## 233 3.555348 2521.00 1
## 234 3.526361 3635.75 1
## 235 3.583519 1017.00 1
## 236 3.218876 1416.00 1
## 237 3.806662 912.00 1
## 238 3.688879 3635.75 1
## 239 3.784190 1733.00 31
## 240 3.850148 341.00 3
## 241 3.583519 3635.75 31
## 242 3.135494 1158.00 1
## 243 3.583519 946.00 1
## 244 3.806662 1325.00 1
## 245 3.737670 1631.00 31
## 246 3.044522 409.00 1
## 247 3.761200 998.00 1
## 248 4.007333 1390.00 31
## 249 3.663562 448.00 1
## 250 3.610918 405.00 31
## 251 3.610918 3588.00 9
## 252 3.688879 73.00 2
## 253 3.891820 912.00 1
## 254 3.871201 296.00 1
## 255 3.737670 1751.00 2
## 256 3.784190 2521.00 1
## 257 3.610918 887.00 10
## 258 3.555348 337.00 6
## 259 3.784190 2588.00 29
## 260 4.025352 1440.00 13
## 261 3.367296 812.00 31
## 262 2.302585 409.00 31
## 263 3.951244 1773.00 1
## 264 3.688879 1204.00 10
## 265 3.465736 814.00 1
## 266 4.060443 792.00 16
## 267 3.784190 821.00 1
## 268 3.218876 544.00 1
## 269 3.912023 759.00 1
## 270 4.127134 1258.00 1
## 271 3.891820 543.00 31
## 272 2.772589 587.00 8
## 273 3.637586 270.00 1
## 274 3.091042 235.00 23
## 275 3.688879 2361.00 7
## 276 3.433987 2342.00 24
## 277 4.248495 306.00 1
## 278 3.332205 358.00 1
## 279 3.295837 507.00 31
## 280 3.218876 1744.00 10
## 281 2.302585 300.00 31
## 282 4.043051 448.00 1
## 283 3.465736 1504.00 18
## 284 3.828641 433.00 1
## 285 3.496508 1966.00 11
## 286 3.496508 145.00 1
## 287 3.970292 2291.00 5
## 288 3.178054 733.00 1
## 289 3.295837 3461.00 1
## 290 3.713572 500.00 12
## 291 3.850148 375.00 9
## 292 3.970292 1605.00 1
## 293 3.912023 2577.00 1
## 294 3.637586 696.00 1
## 295 3.496508 1119.00 1
## 296 3.891820 571.00 10
## 297 3.784190 1721.00 31
## 298 3.912023 86.00 1
## 299 3.761200 192.00 22
## 300 2.833213 341.00 1
## 301 3.663562 399.00 1
## 302 3.784190 2348.00 1
## 303 3.737670 337.00 18
## 304 3.496508 188.00 1
## 305 3.295837 1074.00 1
## 306 4.043051 475.00 31
## 307 4.174387 632.00 1
## 308 3.218876 1023.00 6
## 309 4.060443 639.00 15
## 310 3.912023 862.00 1
## 311 3.555348 200.00 30
## 312 3.737670 1262.00 8
## 313 3.713572 3328.00 1
## 314 2.639057 1562.00 12
## 315 2.995732 1674.00 6
## 316 3.555348 737.00 1
## 317 3.970292 2697.00 7
## 318 4.127134 407.00 17
## 319 3.465736 3635.75 1
## 320 3.258097 1744.00 1
## 321 3.465736 733.00 1
## 322 3.091042 761.00 11
## 323 3.526361 994.00 1
## 324 3.663562 913.00 22
## 325 4.007333 302.00 31
## 326 3.091042 790.00 1
## 327 3.850148 2651.00 1
## 328 4.060443 1208.00 1
## 329 3.850148 1677.00 31
## 330 3.091042 493.00 1
## 331 4.189655 383.00 1
## 332 3.091042 561.00 31
## 333 3.218876 488.00 31
## 334 3.663562 627.00 31
## 335 4.204693 147.00 1
## 336 4.043051 2614.00 31
## 337 2.397895 1440.00 1
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## 339 3.555348 137.00 1
## 340 3.970292 1947.00 1
## 341 3.713572 337.00 1
## 342 3.401197 762.00 1
## 343 3.583519 414.00 1
## 344 3.610918 235.00 31
## 345 3.713572 1222.00 12
## 346 2.708050 1089.00 31
## 347 4.094345 1117.00 31
## 348 3.988984 160.00 1
## 349 3.332205 1452.00 1
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## 351 4.127134 3463.00 1
## 352 3.663562 1121.00 1
## 353 4.060443 1532.00 31
## 354 3.465736 954.00 1
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## 356 3.555348 1428.00 1
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## 359 3.737670 302.00 1
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## 361 2.397895 177.00 1
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## 363 3.931826 1182.00 25
## 364 3.583519 258.00 31
## 365 2.564949 944.00 1
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## 367 3.496508 551.00 1
## 368 3.891820 3171.00 31
## 369 3.891820 2322.00 1
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## 371 3.713572 3309.00 1
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## 373 3.367296 806.00 1
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## 376 3.713572 1506.00 8
## 377 3.850148 429.00 7
## 378 3.663562 323.00 31
## 379 3.091042 763.00 1
## 380 2.197225 507.00 1
## 381 3.737670 187.00 1
## 382 2.890372 1005.00 30
## 383 3.784190 602.00 1
## 384 3.688879 2270.00 1
## 385 2.772589 1167.00 1
## 386 3.332205 175.00 1
## 387 3.850148 1849.00 10
## 388 3.737670 3635.75 31
## 389 3.218876 719.00 1
## 390 4.110874 1235.00 1
## 391 3.332205 2440.00 1
## 392 2.833213 1089.00 11
## 393 3.850148 337.00 30
## 394 2.833213 3165.00 1
## 395 3.806662 1773.00 9
## 396 4.007333 1269.00 1
## 397 3.496508 406.00 1
## 398 2.944439 296.00 1
## 399 3.637586 649.00 1
## 400 3.332205 109.00 1
## 401 3.828641 258.00 18
## 402 3.912023 1235.00 1
## 403 4.317488 898.00 25
## 404 3.295837 299.00 16
## 405 3.951244 687.00 28
## 406 3.891820 1044.00 1
## 407 3.713572 760.00 1
## 408 3.401197 1995.00 1
## 409 3.610918 160.00 1
## 410 3.713572 2632.00 27
## 411 3.806662 1886.00 10
## 412 3.688879 2404.00 1
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## 414 3.465736 650.00 2
## 415 3.218876 358.00 1
## 416 3.091042 474.00 31
## 417 2.708050 641.00 1
## 418 3.713572 531.00 1
## 419 2.302585 1372.00 1
## 420 4.204693 399.00 1
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## 422 3.496508 1976.00 11
## 423 3.663562 1172.00 31
## 424 3.218876 2583.00 18
## 425 3.044522 626.00 1
## 426 2.944439 522.00 16
## 427 3.713572 446.00 7
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## 2003 3.044522 377.00 31
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## 9664 3.178054 588.00 1
## 9665 3.688879 1608.00 1
## 9666 3.688879 2890.00 1
## 9667 3.637586 1739.00 1
## 9668 3.688879 3588.00 1
## 9669 4.248495 315.00 31
## 9670 3.663562 2790.00 1
## 9671 3.044522 1005.00 2
## 9672 3.988984 1346.00 1
## 9673 3.610918 1290.00 1
## 9674 2.079442 1096.00 1
## 9675 3.761200 2285.00 1
## 9676 3.295837 354.00 1
## 9677 4.094345 280.00 1
## 9678 2.564949 337.00 2
## 9679 2.944439 674.00 1
## 9680 3.988984 2611.00 1
## 9681 3.912023 994.00 31
## 9682 4.007333 936.00 1
## 9683 3.737670 2402.00 1
## 9684 3.135494 771.00 8
## 9685 3.135494 197.00 5
## 9686 3.583519 2556.00 3
## 9687 2.995732 296.00 1
## 9688 3.465736 109.00 1
## 9689 2.197225 1750.00 31
## 9690 3.663562 1031.00 1
## 9691 3.367296 414.00 1
## 9692 3.688879 410.00 1
## 9693 3.218876 373.00 31
## 9694 3.091042 496.00 1
## 9695 3.931826 1222.00 5
## 9696 3.912023 964.00 31
## 9697 3.496508 3635.75 8
## 9698 3.688879 867.00 1
## 9699 1.945910 611.00 31
## 9700 3.761200 2408.00 1
## 9701 3.555348 693.00 19
## 9702 3.178054 2118.00 31
## 9703 3.891820 163.00 1
## 9704 3.091042 588.00 1
## 9705 3.610918 594.00 1
## 9706 3.688879 441.00 1
## 9707 2.639057 1046.00 31
## 9708 3.828641 957.00 3
## 9709 3.091042 414.00 1
## 9710 2.302585 1747.00 31
## 9711 3.784190 2337.00 1
## 9712 3.850148 282.00 1
## 9713 4.189655 1372.00 1
## 9714 3.332205 3151.00 10
## 9715 3.218876 600.00 1
## 9716 3.806662 334.00 1
## 9717 3.761200 357.00 1
## 9718 3.871201 529.00 8
## 9719 3.218876 2926.00 18
## 9720 3.988984 3635.75 1
## 9721 3.258097 370.00 1
## 9722 3.526361 3635.75 1
## 9723 3.091042 1189.00 31
## 9724 3.367296 644.00 1
## 9725 2.708050 919.00 7
## 9726 3.912023 690.00 1
## 9727 3.761200 3635.75 20
## 9728 3.850148 1871.00 1
## 9729 3.583519 2390.00 1
## 9730 3.970292 594.00 1
## 9731 3.044522 762.00 3
## 9732 3.761200 187.00 31
## 9733 3.828641 1886.00 1
## 9734 3.610918 508.00 1
## 9735 3.044522 925.00 2
## 9736 4.158883 946.00 3
## 9737 3.850148 711.00 1
## 9738 3.737670 1851.00 31
## 9739 3.784190 1014.00 31
## 9740 3.135494 829.00 1
## 9741 3.433987 852.00 1
## 9742 3.806662 2741.00 1
## 9743 3.850148 1513.00 13
## 9744 3.332205 1212.00 1
## 9745 3.688879 672.00 1
## 9746 3.663562 332.00 31
## 9747 4.127134 740.00 1
## 9748 3.526361 929.00 1
## 9749 2.995732 861.00 1
## 9750 4.043051 333.00 1
## 9751 3.135494 489.00 31
## 9752 4.007333 486.00 1
## 9753 3.871201 2475.00 28
## 9754 3.828641 135.00 1
## 9755 3.332205 2048.00 22
## 9756 3.688879 3186.00 23
## 9757 2.890372 1262.00 1
## 9758 3.433987 337.00 25
## 9759 3.663562 861.00 1
## 9760 4.025352 251.00 31
## 9761 3.713572 2436.00 1
## 9762 4.060443 1566.00 15
## 9763 2.772589 1379.00 3
## 9764 3.663562 277.00 1
## 9765 3.465736 2305.00 1
## 9766 3.367296 1793.00 18
## 9767 3.332205 641.00 13
## 9768 4.110874 227.00 1
## 9769 4.189655 944.00 1
## 9770 3.912023 281.00 1
## 9771 3.367296 965.00 4
## 9772 2.833213 544.00 31
## 9773 2.639057 130.00 1
## 9774 3.970292 3011.00 1
## 9775 4.127134 2079.00 2
## 9776 3.091042 451.00 1
## 9777 3.637586 391.00 31
## 9778 3.951244 853.00 8
## 9779 3.295837 680.00 3
## 9780 3.761200 991.00 9
## 9781 3.806662 2948.00 31
## 9782 3.258097 2562.00 17
## 9783 2.995732 2026.00 31
## 9784 3.828641 391.00 1
## 9785 3.871201 1895.00 7
## 9786 4.060443 489.00 1
## 9787 4.060443 697.00 1
## 9788 4.094345 116.00 1
## 9789 3.970292 236.00 11
## 9790 3.688879 2475.00 15
## 9791 3.784190 1688.00 14
## 9792 3.828641 2475.00 1
## 9793 4.174387 1521.00 2
## 9794 3.871201 1181.00 17
## 9795 3.951244 3635.75 1
## 9796 3.663562 624.00 27
## 9797 2.890372 1846.00 31
## 9798 3.737670 930.00 1
## 9799 4.025352 2153.00 17
## 9800 3.178054 1014.00 31
## 9801 3.583519 259.00 1
## 9802 4.127134 213.00 1
## 9803 3.806662 253.00 1
## 9804 3.091042 369.00 1
## 9805 3.970292 328.00 1
## 9806 2.995732 692.00 1
## 9807 3.951244 817.00 1
## 9808 2.995732 1250.00 31
## 9809 4.143135 296.00 1
## 9810 4.007333 2860.00 11
## 9811 4.248495 701.00 31
## 9812 3.713572 2084.00 1
## 9813 3.218876 569.00 31
## 9814 1.945910 181.00 8
## 9815 3.135494 130.00 9
## 9816 3.178054 140.00 7
## 9817 4.007333 3635.75 1
## 9818 3.931826 1778.00 1
## 9819 4.158883 918.00 31
## 9820 3.663562 399.00 31
## 9821 3.496508 1971.00 28
## 9822 3.610918 483.00 6
## 9823 3.737670 3347.00 12
## 9824 2.833213 591.00 1
## 9825 3.713572 2500.00 1
## 9826 3.401197 1139.00 25
## 9827 3.091042 1514.00 1
## 9828 3.688879 266.00 1
## 9829 3.713572 190.00 31
## 9830 3.988984 1065.00 5
## 9831 3.871201 2422.00 6
## 9832 3.737670 2586.00 31
## 9833 3.218876 692.00 31
## 9834 3.737670 546.00 1
## 9835 3.663562 3498.00 1
## 9836 3.761200 1237.00 1
## 9837 3.044522 316.00 1
## 9838 3.871201 3042.00 6
## 9839 3.178054 347.00 15
## 9840 3.737670 293.00 1
## 9841 3.891820 109.00 1
## 9842 3.555348 1642.00 1
## 9843 2.944439 1843.00 11
## 9844 3.912023 2402.00 14
## 9845 3.218876 1005.00 1
## 9846 3.737670 806.00 27
## 9847 2.995732 332.00 31
## 9848 3.218876 534.00 1
## 9849 3.258097 534.00 1
## 9850 3.610918 361.00 1
## 9851 3.178054 546.00 10
## 9852 1.945910 1927.00 1
## 9853 2.564949 481.00 2
## 9854 3.871201 696.00 1
## 9855 3.555348 867.00 5
## 9856 3.912023 479.00 2
## 9857 3.663562 296.00 1
## 9858 3.583519 209.00 1
## 9859 3.091042 468.00 7
## 9860 3.610918 967.00 31
## 9861 4.174387 200.00 1
## 9862 3.583519 802.00 1
## 9863 3.218876 937.00 1
## 9864 4.127134 991.00 1
## 9865 3.295837 680.00 1
## 9866 3.806662 507.00 1
## 9867 4.077537 1819.00 31
## 9868 3.931826 1521.00 1
## 9869 3.332205 362.00 25
## 9870 4.234107 222.00 1
## 9871 4.007333 926.00 1
## 9872 3.178054 1846.00 1
## 9873 3.610918 484.00 3
## 9874 2.995732 137.00 27
## 9875 4.174387 760.00 31
## 9876 3.784190 649.00 31
## 9877 3.871201 2640.00 16
## 9878 3.332205 3635.75 31
## 9879 3.806662 2610.00 10
## 9880 3.178054 95.00 1
## 9881 3.713572 1620.00 7
## 9882 3.218876 689.00 31
## 9883 2.639057 1089.00 27
## 9884 3.295837 868.00 4
## 9885 3.850148 773.00 9
## 9886 3.871201 1023.00 4
## 9887 3.688879 566.00 1
## 9888 2.197225 644.00 2
## 9889 3.637586 1164.00 16
## 9890 4.248495 1013.00 1
## 9891 3.135494 978.00 1
## 9892 3.637586 3369.00 1
## 9893 2.708050 524.00 1
## 9894 3.688879 986.00 1
## 9895 3.891820 1235.00 4
## 9896 3.332205 1218.00 31
## 9897 3.761200 102.00 1
## 9898 3.688879 562.00 31
## 9899 3.295837 1431.00 19
## 9900 3.931826 680.00 1
## 9901 3.218876 1246.00 5
## 9902 3.806662 2394.00 1
## 9903 3.044522 462.00 12
## 9904 3.951244 468.00 1
## 9905 3.761200 666.00 31
## 9906 3.178054 258.00 13
## 9907 4.043051 436.00 2
## 9908 3.737670 3466.00 1
## 9909 3.784190 2306.00 1
## 9910 3.526361 462.00 1
## 9911 4.143135 550.00 1
## 9912 4.174387 814.00 2
## 9913 3.761200 1276.00 1
## 9914 3.218876 489.00 1
## 9915 3.091042 164.00 1
## 9916 3.737670 2721.00 1
## 9917 4.110874 835.00 31
## 9918 3.828641 302.00 1
## 9919 4.276666 2586.00 1
## 9920 3.761200 3162.00 1
## 9921 2.079442 762.00 5
## 9922 3.295837 844.00 4
## 9923 2.302585 666.00 1
## 9924 3.178054 922.00 1
## 9925 3.401197 455.00 1
## 9926 3.737670 480.00 1
## 9927 2.833213 1744.00 1
## 9928 3.806662 937.00 1
## 9929 3.332205 2457.00 1
## 9930 2.772589 967.00 1
## 9931 4.127134 1747.00 1
## 9932 3.784190 266.00 1
## 9933 3.178054 2475.00 12
## 9934 3.044522 2425.00 1
## 9935 2.890372 337.00 1
## 9936 4.174387 581.00 5
## 9937 3.583519 163.00 1
## 9938 3.912023 2296.00 31
## 9939 3.931826 2688.00 1
## 9940 2.708050 3300.00 1
## 9941 2.564949 1558.00 1
## 9942 2.944439 2990.00 1
## 9943 3.610918 405.00 1
## 9944 3.637586 282.00 31
## 9945 3.713572 444.00 4
## 9946 4.219508 867.00 23
## 9947 3.091042 1258.00 1
## 9948 3.951244 767.00 1
## 9949 3.663562 1587.00 8
## 9950 3.044522 666.00 31
## 9951 4.094345 628.00 24
## 9952 4.094345 2105.00 1
## 9953 3.784190 448.00 1
## 9954 4.007333 1372.00 31
## 9955 3.295837 390.00 19
## 9956 2.484907 216.00 17
## 9957 3.367296 1085.00 1
## 9958 3.091042 222.00 1
## 9959 3.465736 2053.00 31
## 9960 3.135494 641.00 1
## 9961 3.465736 1073.00 1
## 9962 3.784190 1044.00 1
## 9963 3.135494 315.00 1
## 9964 4.110874 268.00 1
## 9965 3.135494 501.00 1
## 9966 3.218876 547.00 1
## 9967 4.219508 2328.00 31
## 9968 3.871201 116.00 1
## 9969 3.663562 328.00 1
## 9970 3.044522 733.00 31
## 9971 3.295837 3454.00 10
## 9972 3.367296 844.00 5
## 9973 2.079442 247.00 2
## 9974 4.143135 1440.00 1
## 9975 2.995732 2191.00 31
## 9976 3.465736 1013.00 1
## 9977 4.043051 842.00 1
## 9978 4.158883 488.00 1
## 9979 3.332205 1166.00 29
## 9980 3.784190 1587.00 1
## 9981 3.367296 1400.00 1
## 9982 2.890372 451.00 31
## 9983 3.688879 1892.00 7
## 9984 3.218876 1563.00 1
## 9985 3.555348 2504.00 8
## 9986 3.610918 337.00 1
## 9987 3.951244 347.00 31
## 9988 4.204693 1729.00 1
## 9989 4.174387 1390.00 31
## 9990 2.302585 983.00 1
## 9991 2.890372 678.00 1
## 9992 4.025352 1303.00 1
## 9993 4.174387 257.00 1
## 9994 3.367296 2565.00 14
## 9995 4.060443 226.00 12
## 9996 4.007333 989.00 14
## 9997 4.204693 250.00 1
## 9998 3.367296 957.00 1
## 9999 4.007333 3086.00 1
## 10000 3.433987 2342.00 1
## Arrival.Delay.in.Minutes_log
## 1 0.0000000
## 2 0.0000000
## 3 0.0000000
## 4 1.6094379
## 5 3.0445224
## 6 0.0000000
## 7 0.0000000
## 8 0.0000000
## 9 0.0000000
## 10 2.9444390
## 11 3.5115454
## 12 3.1780538
## 13 0.0000000
## 14 0.0000000
## 15 1.7917595
## 16 3.5115454
## 17 1.9459101
## 18 3.5115454
## 19 3.2188758
## 20 1.0986123
## 21 2.6390573
## 22 3.5115454
## 23 3.4011974
## 24 3.5115454
## 25 3.3672958
## 26 0.0000000
## 27 1.3862944
## 28 0.0000000
## 29 0.0000000
## 30 1.0986123
## 31 3.5115454
## 32 0.0000000
## 33 0.0000000
## 34 3.5115454
## 35 0.0000000
## 36 0.0000000
## 37 3.5115454
## 38 1.9459101
## 39 0.0000000
## 40 3.5115454
## 41 0.0000000
## 42 2.3025851
## 43 0.0000000
## 44 0.0000000
## 45 1.6094379
## 46 0.0000000
## 47 3.5115454
## 48 0.0000000
## 49 2.9957323
## 50 0.0000000
## 51 0.0000000
## 52 2.8903718
## 53 3.5115454
## 54 3.5115454
## 55 0.0000000
## 56 1.3862944
## 57 0.0000000
## 58 2.1972246
## 59 2.3978953
## 60 0.0000000
## 61 3.5115454
## 62 0.0000000
## 63 0.0000000
## 64 1.0986123
## 65 0.0000000
## 66 0.6931472
## 67 0.0000000
## 68 2.6390573
## 69 0.0000000
## 70 0.0000000
## 71 0.0000000
## 72 2.3978953
## 73 0.0000000
## 74 2.7725887
## 75 0.0000000
## 76 0.0000000
## 77 2.5649494
## 78 2.7080502
## 79 0.0000000
## 80 0.0000000
## 81 0.0000000
## 82 2.7080502
## 83 1.0986123
## 84 3.5115454
## 85 1.6094379
## 86 3.5115454
## 87 1.9459101
## 88 2.6390573
## 89 1.0986123
## 90 0.0000000
## 91 3.5115454
## 92 0.0000000
## 93 0.0000000
## 94 1.7917595
## 95 3.5115454
## 96 0.0000000
## 97 0.0000000
## 98 0.0000000
## 99 0.0000000
## 100 0.0000000
## 101 2.5649494
## 102 0.0000000
## 103 0.0000000
## 104 2.9957323
## 105 0.0000000
## 106 2.1972246
## 107 0.0000000
## 108 0.0000000
## 109 0.0000000
## 110 0.0000000
## 111 0.0000000
## 112 0.0000000
## 113 0.0000000
## 114 0.0000000
## 115 0.0000000
## 116 0.0000000
## 117 0.0000000
## 118 0.0000000
## 119 0.0000000
## 120 0.0000000
## 121 0.0000000
## 122 0.0000000
## 123 0.0000000
## 124 3.5115454
## 125 0.0000000
## 126 1.3862944
## 127 2.1972246
## 128 0.0000000
## 129 0.0000000
## 130 0.0000000
## 131 2.1972246
## 132 1.7917595
## 133 0.0000000
## 134 0.0000000
## 135 3.5115454
## 136 0.0000000
## 137 0.0000000
## 138 0.0000000
## 139 0.0000000
## 140 2.3025851
## 141 0.0000000
## 142 0.6931472
## 143 0.0000000
## 144 0.0000000
## 145 3.0910425
## 146 0.0000000
## 147 3.1780538
## 148 0.0000000
## 149 0.0000000
## 150 1.9459101
## 151 3.5115454
## 152 0.0000000
## 153 3.5115454
## 154 0.0000000
## 155 0.0000000
## 156 2.3025851
## 157 0.0000000
## 158 3.4011974
## 159 2.1972246
## 160 2.3025851
## 161 0.0000000
## 162 2.4849066
## 163 0.0000000
## 164 0.0000000
## 165 3.4965076
## 166 3.5115454
## 167 0.0000000
## 168 3.5115454
## 169 0.6931472
## 170 0.0000000
## 171 0.0000000
## 172 0.0000000
## 173 3.5115454
## 174 1.6094379
## 175 3.5115454
## 176 0.0000000
## 177 0.0000000
## 178 3.5115454
## 179 3.5115454
## 180 0.0000000
## 181 0.0000000
## 182 3.4011974
## 183 3.4965076
## 184 0.0000000
## 185 0.0000000
## 186 0.0000000
## 187 2.7725887
## 188 0.0000000
## 189 1.3862944
## 190 0.0000000
## 191 0.0000000
## 192 1.0986123
## 193 0.0000000
## 194 1.3862944
## 195 0.6931472
## 196 0.0000000
## 197 3.5115454
## 198 0.0000000
## 199 2.5649494
## 200 3.5115454
## 201 0.0000000
## 202 0.0000000
## 203 2.5649494
## 204 0.0000000
## 205 3.5115454
## 206 0.0000000
## 207 0.0000000
## 208 2.1972246
## 209 0.0000000
## 210 2.3978953
## 211 0.0000000
## 212 0.6931472
## 213 0.0000000
## 214 1.6094379
## 215 2.7725887
## 216 3.5115454
## 217 0.0000000
## 218 0.0000000
## 219 0.0000000
## 220 0.0000000
## 221 0.0000000
## 222 2.0794415
## 223 0.0000000
## 224 2.1972246
## 225 1.3862944
## 226 0.0000000
## 227 0.0000000
## 228 0.0000000
## 229 3.5115454
## 230 0.0000000
## 231 2.6390573
## 232 0.0000000
## 233 0.0000000
## 234 0.0000000
## 235 0.0000000
## 236 0.0000000
## 237 0.0000000
## 238 0.0000000
## 239 3.5115454
## 240 0.0000000
## 241 3.5115454
## 242 0.0000000
## 243 0.0000000
## 244 0.0000000
## 245 3.5115454
## 246 0.0000000
## 247 0.0000000
## 248 3.3672958
## 249 0.0000000
## 250 3.5115454
## 251 1.3862944
## 252 3.2188758
## 253 0.0000000
## 254 0.0000000
## 255 0.0000000
## 256 0.0000000
## 257 0.0000000
## 258 2.3025851
## 259 1.7917595
## 260 3.0445224
## 261 3.5115454
## 262 2.9444390
## 263 0.0000000
## 264 0.0000000
## 265 0.0000000
## 266 3.4657359
## 267 0.0000000
## 268 3.5115454
## 269 0.0000000
## 270 0.0000000
## 271 3.5115454
## 272 0.0000000
## 273 2.4849066
## 274 1.0986123
## 275 1.6094379
## 276 2.3025851
## 277 1.7917595
## 278 0.0000000
## 279 3.3672958
## 280 0.0000000
## 281 3.5115454
## 282 0.0000000
## 283 0.0000000
## 284 0.0000000
## 285 2.3978953
## 286 0.0000000
## 287 1.3862944
## 288 2.6390573
## 289 0.0000000
## 290 2.1972246
## 291 0.0000000
## 292 0.0000000
## 293 0.0000000
## 294 0.0000000
## 295 0.0000000
## 296 0.0000000
## 297 3.1354942
## 298 1.6094379
## 299 2.5649494
## 300 0.0000000
## 301 0.0000000
## 302 0.0000000
## 303 2.3978953
## 304 1.6094379
## 305 0.0000000
## 306 3.5115454
## 307 0.0000000
## 308 0.0000000
## 309 2.6390573
## 310 1.7917595
## 311 3.0910425
## 312 0.0000000
## 313 0.0000000
## 314 1.7917595
## 315 0.0000000
## 316 0.0000000
## 317 0.0000000
## 318 3.4011974
## 319 0.0000000
## 320 0.0000000
## 321 0.0000000
## 322 2.1972246
## 323 0.0000000
## 324 0.6931472
## 325 3.5115454
## 326 0.0000000
## 327 0.0000000
## 328 0.0000000
## 329 3.5115454
## 330 0.0000000
## 331 2.8903718
## 332 3.5115454
## 333 3.5115454
## 334 3.5115454
## 335 0.0000000
## 336 3.5115454
## 337 0.0000000
## 338 2.4849066
## 339 3.0445224
## 340 0.0000000
## 341 0.0000000
## 342 0.0000000
## 343 0.0000000
## 344 3.5115454
## 345 0.0000000
## 346 2.6390573
## 347 3.5115454
## 348 2.4849066
## 349 0.0000000
## 350 0.0000000
## 351 1.9459101
## 352 1.6094379
## 353 3.5115454
## 354 0.6931472
## 355 0.0000000
## 356 0.0000000
## 357 0.0000000
## 358 1.7917595
## 359 0.0000000
## 360 0.0000000
## 361 0.0000000
## 362 3.5115454
## 363 3.3322045
## 364 3.5115454
## 365 0.0000000
## 366 3.1354942
## 367 0.0000000
## 368 3.5115454
## 369 0.0000000
## 370 2.0794415
## 371 0.0000000
## 372 1.6094379
## 373 0.0000000
## 374 2.6390573
## 375 0.0000000
## 376 0.0000000
## 377 2.5649494
## 378 3.5115454
## 379 1.0986123
## 380 0.0000000
## 381 2.7080502
## 382 3.4657359
## 383 0.0000000
## 384 0.0000000
## 385 2.3978953
## 386 0.0000000
## 387 0.0000000
## 388 3.5115454
## 389 0.6931472
## 390 0.0000000
## 391 0.0000000
## 392 0.0000000
## 393 3.0910425
## 394 0.0000000
## 395 3.0910425
## 396 0.0000000
## 397 0.0000000
## 398 0.0000000
## 399 2.8903718
## 400 0.0000000
## 401 2.5649494
## 402 0.0000000
## 403 0.6931472
## 404 1.9459101
## 405 3.5115454
## 406 0.0000000
## 407 0.6931472
## 408 0.0000000
## 409 0.0000000
## 410 2.4849066
## 411 2.7080502
## 412 0.0000000
## 413 0.0000000
## 414 0.0000000
## 415 0.0000000
## 416 3.5115454
## 417 0.0000000
## 418 0.0000000
## 419 1.0986123
## 420 0.0000000
## 421 2.3978953
## 422 0.0000000
## 423 3.4657359
## 424 2.9957323
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##
## $subset
## NULL
##
## $outlierMethod
## [1] "none"
##
## attr(,"class")
## [1] "mvn"set.seed(123)
sample_age <- sample(df_da$Age_log, 1000)
shapiro.test(sample_age)##
## Shapiro-Wilk normality test
##
## data: sample_age
## W = 0.94383, p-value < 2.2e-16sample_Flight.Distance <- sample(df_da$Flight.Distance, 1000)
shapiro.test(sample_Flight.Distance)##
## Shapiro-Wilk normality test
##
## data: sample_Flight.Distance
## W = 0.86966, p-value < 2.2e-16sample_Departure.Delay.in.Minutes <- sample(df_da$Departure.Delay.in.Minutes, 1000)
shapiro.test(sample_Departure.Delay.in.Minutes)##
## Shapiro-Wilk normality test
##
## data: sample_Departure.Delay.in.Minutes
## W = 0.64264, p-value < 2.2e-16sample_Arrival.Delay.in.Minutes_log <- sample(df_da$Arrival.Delay.in.Minutes_log, 1000)
shapiro.test(sample_Arrival.Delay.in.Minutes_log)##
## Shapiro-Wilk normality test
##
## data: sample_Arrival.Delay.in.Minutes_log
## W = 0.73791, p-value < 2.2e-16Uji Homogenitas
library(biotools)## Warning: package 'biotools' was built under R version 4.5.3## ---
## biotools version 4.3set.seed(123)
sample_index <- sample(1:nrow(df_da), 1000)
boxM(
num_var[sample_index, ],
grouping = df_da$Seat_Group[sample_index]
)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: num_var[sample_index, ]
## Chi-Sq (approx.) = 45.603, df = 20, p-value = 0.0009136Model Analisis Diskriminan
library(MASS)
model_da <- lda(
Seat_Group ~ Age_log +
Flight.Distance +
Departure.Delay.in.Minutes +
Arrival.Delay.in.Minutes_log +
Class +
Type.of.Travel +
Gender +
Customer.Type +
Food.and.drink +
Inflight.wifi.service,
data = df_model
)
model_da## Call:
## lda(Seat_Group ~ Age_log + Flight.Distance + Departure.Delay.in.Minutes +
## Arrival.Delay.in.Minutes_log + Class + Type.of.Travel + Gender +
## Customer.Type + Food.and.drink + Inflight.wifi.service, data = df_model)
##
## Prior probabilities of groups:
## Netral Nyaman Tidak_Nyaman
## 0.1815726 0.5566226 0.2618047
##
## Group means:
## Age_log Flight.Distance Departure.Delay.in.Minutes
## Netral 3.548959 1066.7985 9.030854
## Nyaman 3.652444 1291.8188 7.861251
## Tidak_Nyaman 3.441409 968.5965 8.878487
## Arrival.Delay.in.Minutes_log ClassEco ClassEco Plus
## Netral 1.261431 0.5355372 0.07823691
## Nyaman 1.093407 0.3727534 0.05535586
## Tidak_Nyaman 1.227419 0.5934276 0.08674054
## Type.of.TravelPersonal Travel GenderMale
## Netral 0.3460055 0.4931129
## Nyaman 0.2600647 0.4820273
## Tidak_Nyaman 0.3813527 0.5158579
## Customer.TypeLoyal Customer Food.and.drink1 Food.and.drink2
## Netral 0.8055096 0.08595041 0.09641873
## Nyaman 0.8635873 0.03360891 0.14270309
## Tidak_Nyaman 0.7126481 0.33549866 0.44554834
## Food.and.drink3 Food.and.drink4 Food.and.drink5
## Netral 0.62699725 0.10468320 0.08539945
## Nyaman 0.15240834 0.35603882 0.31416247
## Tidak_Nyaman 0.08406572 0.07107375 0.06228506
## Inflight.wifi.service1 Inflight.wifi.service2
## Netral 0.1526171 0.2319559
## Nyaman 0.1619339 0.2172897
## Tidak_Nyaman 0.1994650 0.3282384
## Inflight.wifi.service3 Inflight.wifi.service4
## Netral 0.3845730 0.1553719
## Nyaman 0.2207045 0.2271747
## Tidak_Nyaman 0.2311807 0.1452044
## Inflight.wifi.service5
## Netral 0.05399449
## Nyaman 0.14306254
## Tidak_Nyaman 0.07069163
##
## Coefficients of linear discriminants:
## LD1 LD2
## Age_log -5.632421e-01 -1.081672e-01
## Flight.Distance -3.474016e-05 -7.896922e-05
## Departure.Delay.in.Minutes 4.944427e-03 1.206882e-03
## Arrival.Delay.in.Minutes_log 2.492776e-03 2.517682e-02
## ClassEco 4.491611e-01 3.260559e-01
## ClassEco Plus 5.909134e-01 3.149784e-01
## Type.of.TravelPersonal Travel 1.109357e-01 -2.074187e-01
## GenderMale 1.650470e-01 5.257266e-02
## Customer.TypeLoyal Customer -5.251022e-01 1.210932e-01
## Food.and.drink1 1.879265e+00 5.948761e-02
## Food.and.drink2 1.105579e+00 -1.548807e-01
## Food.and.drink3 -2.963093e-01 2.526030e+00
## Food.and.drink4 -1.095256e+00 -6.751243e-02
## Food.and.drink5 -1.095281e+00 -7.502440e-02
## Inflight.wifi.service1 1.445943e-01 1.980829e-01
## Inflight.wifi.service2 3.088957e-01 2.225901e-01
## Inflight.wifi.service3 1.589964e-01 5.267485e-01
## Inflight.wifi.service4 -3.180365e-02 1.243490e-01
## Inflight.wifi.service5 -1.181741e-02 -1.141565e-01
##
## Proportion of trace:
## LD1 LD2
## 0.6854 0.3146pred_da <- predict(model_da)
table(Prediksi = pred_da$class,
Aktual = df_model$Seat_Group)## Aktual
## Prediksi Netral Nyaman Tidak_Nyaman
## Netral 1133 763 218
## Nyaman 389 4229 400
## Tidak_Nyaman 293 572 1999mean(pred_da$class == df_model$Seat_Group)## [1] 0.7363946