Analisis Multivariat pada Data Sleep Health and Lifestyle untuk Mengidentifikasi Faktor Penentu Kualitas Tidur
1. Persiapan Data
1.1 Load Library
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# LIBRARY
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# ===============================
# DATA UNDERSTANDING
# ===============================
data <- read.csv("Sleep_health_and_lifestyle_dataset.csv")
str(data)## 'data.frame': 374 obs. of 13 variables:
## $ Person.ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Age : int 27 28 28 28 28 28 29 29 29 29 ...
## $ Occupation : chr "Software Engineer" "Doctor" "Doctor" "Sales Representative" ...
## $ Sleep.Duration : num 6.1 6.2 6.2 5.9 5.9 5.9 6.3 7.8 7.8 7.8 ...
## $ Quality.of.Sleep : int 6 6 6 4 4 4 6 7 7 7 ...
## $ Physical.Activity.Level: int 42 60 60 30 30 30 40 75 75 75 ...
## $ Stress.Level : int 6 8 8 8 8 8 7 6 6 6 ...
## $ BMI.Category : chr "Overweight" "Normal" "Normal" "Obese" ...
## $ Blood.Pressure : chr "126/83" "125/80" "125/80" "140/90" ...
## $ Heart.Rate : int 77 75 75 85 85 85 82 70 70 70 ...
## $ Daily.Steps : int 4200 10000 10000 3000 3000 3000 3500 8000 8000 8000 ...
## $ Sleep.Disorder : chr "None" "None" "None" "Sleep Apnea" ...summary(data)## Person.ID Gender Age Occupation
## Min. : 1.00 Length:374 Min. :27.00 Length:374
## 1st Qu.: 94.25 Class :character 1st Qu.:35.25 Class :character
## Median :187.50 Mode :character Median :43.00 Mode :character
## Mean :187.50 Mean :42.18
## 3rd Qu.:280.75 3rd Qu.:50.00
## Max. :374.00 Max. :59.00
## Sleep.Duration Quality.of.Sleep Physical.Activity.Level Stress.Level
## Min. :5.800 Min. :4.000 Min. :30.00 Min. :3.000
## 1st Qu.:6.400 1st Qu.:6.000 1st Qu.:45.00 1st Qu.:4.000
## Median :7.200 Median :7.000 Median :60.00 Median :5.000
## Mean :7.132 Mean :7.313 Mean :59.17 Mean :5.385
## 3rd Qu.:7.800 3rd Qu.:8.000 3rd Qu.:75.00 3rd Qu.:7.000
## Max. :8.500 Max. :9.000 Max. :90.00 Max. :8.000
## BMI.Category Blood.Pressure Heart.Rate Daily.Steps
## Length:374 Length:374 Min. :65.00 Min. : 3000
## Class :character Class :character 1st Qu.:68.00 1st Qu.: 5600
## Mode :character Mode :character Median :70.00 Median : 7000
## Mean :70.17 Mean : 6817
## 3rd Qu.:72.00 3rd Qu.: 8000
## Max. :86.00 Max. :10000
## Sleep.Disorder
## Length:374
## Class :character
## Mode :character
##
##
## sum(is.na(data))## [1] 0Dataset yang digunakan adalah Sleep Health and Lifestyle Dataset yang berisi 374 data observasi dengan 13 variabel. Variabel dalam dataset terdiri dari: a) Variabel numerik (Age, Sleep Duration, Stress Level, dll) b) Variabel kategorik (Gender, BMI Category, Sleep Disorder) Data ini digunakan untuk menganalisis faktor yang mempengaruhi kualitas tidur.
1.3 Preprocessing
# ===============================
# PREPROCESSING
# ===============================
# hapus ID
data <- data %>% dplyr::select(-`Person.ID`)
# ubah ke factor
data$Gender <- as.factor(data$Gender)
data$`BMI.Category` <- as.factor(data$`BMI.Category`)
data$Occupation <- as.factor(data$Occupation)
data$`Sleep.Disorder` <- as.factor(data$`Sleep.Disorder`)
# ubah variabel numerik
data$Quality.of.Sleep <- as.numeric(data$Quality.of.Sleep)
data$Stress.Level <- as.numeric(data$Stress.Level)
data$Sleep.Duration <- as.numeric(data$Sleep.Duration)2. Definsi Variabel & Statistik Deskriptif
2.1 Variabel dependen (Y)
Y <- data %>%
dplyr::select(`Quality.of.Sleep`, `Stress.Level`, `Sleep.Duration`)2.2 Variabel dependen (X)
X <- data %>%
dplyr::select(-`Quality.of.Sleep`, -`Stress.Level`, -`Sleep.Duration`)2.3 Statistik, Summary Variabel
summary(data)## Gender Age Occupation Sleep.Duration Quality.of.Sleep
## Female:185 Min. :27.00 Nurse :73 Min. :5.800 Min. :4.000
## Male :189 1st Qu.:35.25 Doctor :71 1st Qu.:6.400 1st Qu.:6.000
## Median :43.00 Engineer :63 Median :7.200 Median :7.000
## Mean :42.18 Lawyer :47 Mean :7.132 Mean :7.313
## 3rd Qu.:50.00 Teacher :40 3rd Qu.:7.800 3rd Qu.:8.000
## Max. :59.00 Accountant:37 Max. :8.500 Max. :9.000
## (Other) :43
## Physical.Activity.Level Stress.Level BMI.Category Blood.Pressure
## Min. :30.00 Min. :3.000 Normal :195 Length:374
## 1st Qu.:45.00 1st Qu.:4.000 Normal Weight: 21 Class :character
## Median :60.00 Median :5.000 Obese : 10 Mode :character
## Mean :59.17 Mean :5.385 Overweight :148
## 3rd Qu.:75.00 3rd Qu.:7.000
## Max. :90.00 Max. :8.000
##
## Heart.Rate Daily.Steps Sleep.Disorder
## Min. :65.00 Min. : 3000 Insomnia : 77
## 1st Qu.:68.00 1st Qu.: 5600 None :219
## Median :70.00 Median : 7000 Sleep Apnea: 78
## Mean :70.17 Mean : 6817
## 3rd Qu.:72.00 3rd Qu.: 8000
## Max. :86.00 Max. :10000
## summary(Y)## Quality.of.Sleep Stress.Level Sleep.Duration
## Min. :4.000 Min. :3.000 Min. :5.800
## 1st Qu.:6.000 1st Qu.:4.000 1st Qu.:6.400
## Median :7.000 Median :5.000 Median :7.200
## Mean :7.313 Mean :5.385 Mean :7.132
## 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:7.800
## Max. :9.000 Max. :8.000 Max. :8.500apply(Y, 2, sd)## Quality.of.Sleep Stress.Level Sleep.Duration
## 1.1969559 1.7745264 0.7956567apply(Y, 2, mean)## Quality.of.Sleep Stress.Level Sleep.Duration
## 7.312834 5.385027 7.132086apply(Y, 2, min)## Quality.of.Sleep Stress.Level Sleep.Duration
## 4.0 3.0 5.8apply(Y, 2, max)## Quality.of.Sleep Stress.Level Sleep.Duration
## 9.0 8.0 8.53. Visualisasi Data
3.1 Histogram
par(mfrow=c(1,3))
hist(data$Quality.of.Sleep,
main="Quality of Sleep",
col="skyblue")
hist(data$Stress.Level,
main="Stress Level",
col="maroon")
hist(data$Sleep.Duration,
main="Sleep Duration")
3.2 Boxplot
# boxplot
boxplot(Y,
main="Distribusi Variabel Dependen",
col=c("skyblue","maroon","pink"))
3.3 Heatmap
# korelasi(HEATMAP)
cor_matrix <- cor(Y)
cor_melted <- as.data.frame(as.table(cor_matrix))
ggplot(data = cor_melted, aes(x=Var1, y=Var2, fill=Freq)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 10, hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
coord_fixed() +
geom_text(aes(Var1, Var2, label = round(Freq, 2)), color = "black", size = 4) +
labs(title = "Heatmap Korelasi Variabel Dependen")
4. Peng-Ujian
4.1 Uji Outlier
# OUTLIER (MAHALANOBIS)
mahal <- mahalanobis(Y, colMeans(Y), cov(Y))
cutoff <- qchisq(0.975, df = 3)
outlier <- mahal > cutoff
which(outlier)## [1] 4 5 6 17 19 31 32 81 82 104 106 148sum(outlier)## [1] 124.2 Uji Normalitas
hasil_normalitas <- MVN::mvn(Y)
hasil_normalitas## $multivariate_normality
## Test Statistic p.value Method MVN
## 1 Henze-Zirkler 21.194 <0.001 asymptotic ✗ Not normal
##
## $univariate_normality
## Test Variable Statistic p.value Normality
## 1 Anderson-Darling Quality.of.Sleep 15.554 <0.001 ✗ Not normal
## 2 Anderson-Darling Stress.Level 12.641 <0.001 ✗ Not normal
## 3 Anderson-Darling Sleep.Duration 7.304 <0.001 ✗ Not normal
##
## $descriptives
## Variable n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## 1 Quality.of.Sleep 374 7.313 1.197 7.0 4.0 9.0 6.0 8.0 -0.207 2.246
## 2 Stress.Level 374 5.385 1.775 5.0 3.0 8.0 4.0 7.0 0.154 1.674
## 3 Sleep.Duration 374 7.132 0.796 7.2 5.8 8.5 6.4 7.8 0.037 1.715
##
## $data
## Quality.of.Sleep Stress.Level Sleep.Duration
## 1 6 6 6.1
## 2 6 8 6.2
## 3 6 8 6.2
## 4 4 8 5.9
## 5 4 8 5.9
## 6 4 8 5.9
## 7 6 7 6.3
## 8 7 6 7.8
## 9 7 6 7.8
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## 13 6 8 6.1
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## 17 5 7 6.5
## 18 6 8 6.0
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## 20 7 6 7.6
## 21 7 6 7.7
## 22 7 6 7.7
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## 25 7 6 7.8
## 26 7 6 7.9
## 27 7 6 7.8
## 28 7 6 7.9
## 29 7 6 7.9
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## 31 5 7 6.4
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## 33 8 4 7.9
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## 35 7 6 7.7
## 36 6 8 6.1
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## 38 7 6 7.6
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## 41 7 6 7.7
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## 89 8 4 7.3
## 90 8 4 7.3
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## 94 7 5 7.4
## 95 8 4 7.2
## 96 8 4 7.1
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## 99 8 4 7.1
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## 101 8 4 7.2
## 102 8 4 7.2
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## 104 5 7 6.6
## 105 8 4 7.2
## 106 5 7 6.6
## 107 6 6 6.1
## 108 8 4 7.8
## 109 8 4 7.8
## 110 8 5 7.4
## 111 8 4 7.2
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## 146 7 5 7.4
## 147 8 5 7.2
## 148 5 7 6.5
## 149 7 6 6.9
## 150 9 3 8.0
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## 152 8 5 7.2
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## 154 8 5 7.2
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## 159 8 5 7.2
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## 161 8 5 7.2
## 162 8 6 7.2
## 163 8 6 7.2
## 164 8 5 7.9
## 165 8 5 7.9
## 166 8 5 7.6
## 167 8 6 7.3
## 168 7 6 7.1
## 169 7 6 7.1
## 170 8 5 7.7
## 171 8 5 7.7
## 172 8 5 7.7
## 173 8 5 7.7
## 174 8 5 7.7
## 175 8 5 7.6
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## 180 8 5 7.8
## 181 8 5 7.8
## 182 8 5 7.8
## 183 8 5 7.8
## 184 8 5 7.8
## 185 6 7 6.8
## 186 6 7 6.8
## 187 7 4 6.7
## 188 6 7 6.3
## 189 7 4 6.7
## 190 6 7 6.5
## 191 7 4 6.7
## 192 6 7 6.4
## 193 6 7 6.5
## 194 6 7 6.5
## 195 6 7 6.5
## 196 6 7 6.5
## 197 6 7 6.5
## 198 6 7 6.5
## 199 6 7 6.5
## 200 6 7 6.5
## 201 6 7 6.5
## 202 8 5 7.8
## 203 8 5 7.8
## 204 6 7 6.9
## 205 8 4 7.6
## 206 8 5 7.7
## 207 8 5 7.7
## 208 8 5 7.7
## 209 8 5 7.7
## 210 8 5 7.8
## 211 8 5 7.7
## 212 8 5 7.8
## 213 8 5 7.8
## 214 8 5 7.8
## 215 8 5 7.8
## 216 8 5 7.8
## 217 8 5 7.8
## 218 8 5 7.8
## 219 8 5 7.8
## 220 6 7 6.5
## 221 7 4 6.6
## 222 6 7 6.4
## 223 6 7 6.3
## 224 6 7 6.4
## 225 7 4 6.6
## 226 6 7 6.3
## 227 7 4 6.6
## 228 6 7 6.3
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## 230 6 7 6.3
## 231 7 4 6.6
## 232 6 7 6.3
## 233 7 4 6.6
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## 282 6 8 6.1
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## 284 6 8 6.0
## 285 6 8 6.0
## 286 6 8 6.0
## 287 6 8 6.0
## 288 6 8 6.0
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## 290 6 8 6.1
## 291 6 8 6.0
## 292 6 8 6.1
## 293 6 8 6.1
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## 295 6 8 6.1
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## 300 9 3 8.5
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## 302 9 3 8.5
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## 304 6 8 6.0
## 305 6 8 6.1
## 306 6 8 6.1
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## 308 7 7 6.5
## 309 7 7 6.6
## 310 7 7 6.6
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## 313 9 3 8.4
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## 316 9 3 8.3
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## 319 9 3 8.4
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## 344 9 3 8.1
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## 351 9 3 8.1
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## 356 9 3 8.0
## 357 9 3 8.0
## 358 9 3 8.0
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## 374 9 3 8.1
##
## $subset
## NULL
##
## $outlierMethod
## [1] "none"
##
## attr(,"class")
## [1] "mvn"4.3 Uji Dependensi
# UJI DEPENDENSI (BARTLETT - FIXED)
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:car':
##
## logit## The following object is masked from 'package:MVN':
##
## mardia## The following objects are masked from 'package:ggplot2':
##
## %+%, alphacortest.bartlett(cor(Y), n = nrow(data))## $chisq
## [1] 1176.769
##
## $p.value
## [1] 8.045963e-255
##
## $df
## [1] 34.4 Uji Homogenitas
# ===============================
# UJI HOMOGENITAS (BOX'S M)
# ===============================
boxM(Y, data$`Sleep.Disorder`)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: Y by data$Sleep.Disorder
## Chi-Sq (approx.) = 242.4278, df = 12, p-value = < 2.2e-16boxM(Y, data$`BMI.Category`)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: Y by data$BMI.Category
## Chi-Sq (approx.) = 296.0651, df = 18, p-value = < 2.2e-163. Analisis Manova
# TWO-WAY MANOVA
manova_model <- manova(
cbind(Quality.of.Sleep, Stress.Level, Sleep.Duration) ~
Sleep.Disorder * BMI.Category,
data = data
)
# Pillai (robust)
summary(manova_model, test="Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## Sleep.Disorder 2 0.34856 25.4685 6 724 < 2.2e-16 ***
## BMI.Category 3 0.19698 8.5032 9 1089 2.278e-12 ***
## Sleep.Disorder:BMI.Category 5 0.20907 5.4384 15 1089 1.006e-10 ***
## Residuals 363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14. Analisis Anova (Univariat)
# ===============================
# ANOVA (UNIVARIAT)
# ===============================
summary.aov(manova_model)## Response Quality.of.Sleep :
## Df Sum Sq Mean Sq F value Pr(>F)
## Sleep.Disorder 2 69.21 34.607 29.6904 1.142e-12 ***
## BMI.Category 3 11.00 3.666 3.1455 0.02522 *
## Sleep.Disorder:BMI.Category 5 31.07 6.214 5.3308 9.643e-05 ***
## Residuals 363 423.12 1.166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Stress.Level :
## Df Sum Sq Mean Sq F value Pr(>F)
## Sleep.Disorder 2 40.38 20.1877 6.5540 0.001599 **
## BMI.Category 3 1.31 0.4382 0.1423 0.934579
## Sleep.Disorder:BMI.Category 5 14.76 2.9513 0.9581 0.443442
## Residuals 363 1118.11 3.0802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Sleep.Duration :
## Df Sum Sq Mean Sq F value Pr(>F)
## Sleep.Disorder 2 34.662 17.3308 33.3827 4.915e-14 ***
## BMI.Category 3 7.955 2.6518 5.1079 0.001792 **
## Sleep.Disorder:BMI.Category 5 5.065 1.0131 1.9514 0.085220 .
## Residuals 363 188.453 0.5192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15. UJI Asumsi Ancova
# ===============================
# contoh uji interaksi (Gender dengan covariate)
uji_homogen <- lm(Quality.of.Sleep ~ Gender*Age, data=data)
anova(uji_homogen)uji_homogen2 <- lm(Stress.Level ~ Gender*Age, data=data)
anova(uji_homogen2)uji_homogen3 <- lm(Sleep.Duration ~ Gender*Age, data=data)
anova(uji_homogen3)6. Analisis Mancova
# ===============================
model_mancova <- manova(
cbind(Quality.of.Sleep, Stress.Level, Sleep.Duration) ~
Gender + BMI.Category + Sleep.Disorder +
Age + Physical.Activity.Level + Heart.Rate + Daily.Steps,
data = data
)
summary(model_mancova, test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## Gender 1 0.44887 147.75 3 361.00 < 2.2e-16 ***
## BMI.Category 3 0.32261 57.78 9 878.73 < 2.2e-16 ***
## Sleep.Disorder 2 0.56363 39.95 6 722.00 < 2.2e-16 ***
## Age 1 0.24323 374.39 3 361.00 < 2.2e-16 ***
## Physical.Activity.Level 1 0.83885 23.12 3 361.00 1.039e-13 ***
## Heart.Rate 1 0.34619 227.26 3 361.00 < 2.2e-16 ***
## Daily.Steps 1 0.63118 70.32 3 361.00 < 2.2e-16 ***
## Residuals 363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17. Analisis Ancova
7.1 Quality of Sleep
# ===============================
ancova1 <- aov(Quality.of.Sleep ~ Gender + BMI.Category + Sleep.Disorder +
Age + Physical.Activity.Level + Heart.Rate + Daily.Steps, data=data)
summary(ancova1)## Df Sum Sq Mean Sq F value Pr(>F)
## Gender 1 45.37 45.37 235.45 < 2e-16 ***
## BMI.Category 3 106.40 35.47 184.07 < 2e-16 ***
## Sleep.Disorder 2 8.94 4.47 23.19 3.33e-10 ***
## Age 1 196.28 196.28 1018.68 < 2e-16 ***
## Physical.Activity.Level 1 9.23 9.23 47.90 2.04e-11 ***
## Heart.Rate 1 80.61 80.61 418.34 < 2e-16 ***
## Daily.Steps 1 17.63 17.63 91.50 < 2e-16 ***
## Residuals 363 69.94 0.19
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.2 Stress Level
ancova2 <- aov(Stress.Level ~ Gender + BMI.Category + Sleep.Disorder +
Age + Physical.Activity.Level + Heart.Rate + Daily.Steps, data=data)
summary(ancova2)## Df Sum Sq Mean Sq F value Pr(>F)
## Gender 1 184.2 184.2 368.99 < 2e-16 ***
## BMI.Category 3 130.0 43.3 86.83 < 2e-16 ***
## Sleep.Disorder 2 27.8 13.9 27.84 5.63e-12 ***
## Age 1 215.2 215.2 431.16 < 2e-16 ***
## Physical.Activity.Level 1 5.2 5.2 10.35 0.00141 **
## Heart.Rate 1 332.9 332.9 666.75 < 2e-16 ***
## Daily.Steps 1 98.0 98.0 196.36 < 2e-16 ***
## Residuals 363 181.2 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.3 Sleep Duration
ancova3 <- aov(Sleep.Duration ~ Gender + BMI.Category + Sleep.Disorder +
Age + Physical.Activity.Level + Heart.Rate + Daily.Steps, data=data)
summary(ancova3)## Df Sum Sq Mean Sq F value Pr(>F)
## Gender 1 3.49 3.49 22.72 2.71e-06 ***
## BMI.Category 3 47.82 15.94 103.77 < 2e-16 ***
## Sleep.Disorder 2 4.22 2.11 13.75 1.75e-06 ***
## Age 1 78.35 78.35 510.11 < 2e-16 ***
## Physical.Activity.Level 1 4.61 4.61 30.03 7.98e-08 ***
## Heart.Rate 1 23.29 23.29 151.63 < 2e-16 ***
## Daily.Steps 1 18.59 18.59 121.06 < 2e-16 ***
## Residuals 363 55.76 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18. Uji Lanjut (POST-HOC)
TukeyHSD(ancova1, "BMI.Category")## Warning in replications(paste("~", xx), data = mf): non-factors ignored: Age## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Physical.Activity.Level## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Heart.Rate## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Daily.Steps## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Quality.of.Sleep ~ Gender + BMI.Category + Sleep.Disorder + Age + Physical.Activity.Level + Heart.Rate + Daily.Steps, data = data)
##
## $BMI.Category
## diff lwr upr p adj
## Normal Weight-Normal -0.46874303 -0.7289413 -0.2085448 0.0000276
## Obese-Normal -1.10256828 -1.4699048 -0.7352318 0.0000000
## Overweight-Normal -1.03318256 -1.1566929 -0.9096722 0.0000000
## Obese-Normal Weight -0.63382525 -1.0691122 -0.1985383 0.0011386
## Overweight-Normal Weight -0.56443954 -0.8286240 -0.3002551 0.0000004
## Overweight-Obese 0.06938572 -0.3007851 0.4395565 0.9626665TukeyHSD(ancova1, "Sleep.Disorder")## Warning in replications(paste("~", xx), data = mf): non-factors ignored: Age## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Physical.Activity.Level## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Heart.Rate## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Daily.Steps## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Quality.of.Sleep ~ Gender + BMI.Category + Sleep.Disorder + Age + Physical.Activity.Level + Heart.Rate + Daily.Steps, data = data)
##
## $Sleep.Disorder
## diff lwr upr p adj
## None-Insomnia 0.28972133 0.1528572 0.4265854 0.0000029
## Sleep Apnea-Insomnia 0.27221667 0.1062640 0.4381694 0.0003933
## Sleep Apnea-None -0.01750466 -0.1537181 0.1187088 0.95083998. Visualisasi
8.1 Boxplot Quality of Sleep berdasarkan BMI Category
boxplot(Quality.of.Sleep ~ BMI.Category, data=data,
col="skyblue",
main="Quality of Sleep berdasarkan BMI Category")
8.2 Scatterplot Hubungan Age dengan Quality of Sleep
plot(data$Age, data$Quality.of.Sleep,
main="Hubungan Age dan Quality of Sleep")