library(readxl)
## Warning: package 'readxl' was built under R version 4.5.2
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(psych)
library(writexl)
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
library(e1071)
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
Life_Expectancy_Data <- read_excel("Life Expectancy Data.xlsx")
str(Life_Expectancy_Data)
## tibble [2,938 × 22] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:2938] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Year : num [1:2938] 2015 2014 2013 2012 2011 ...
## $ Status : chr [1:2938] "Developing" "Developing" "Developing" "Developing" ...
## $ Life expectancy : num [1:2938] 65 59.9 59.9 59.5 59.2 58.8 58.6 58.1 57.5 57.3 ...
## $ Adult Mortality : num [1:2938] 263 271 268 272 275 279 281 287 295 295 ...
## $ infant deaths : num [1:2938] 62 64 66 69 71 74 77 80 82 84 ...
## $ Alcohol : num [1:2938] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.03 0.02 0.03 ...
## $ percentage expenditure : num [1:2938] 71.3 73.5 73.2 78.2 7.1 ...
## $ Hepatitis B : num [1:2938] 65 62 64 67 68 66 63 64 63 64 ...
## $ Measles : num [1:2938] 1154 492 430 2787 3013 ...
## $ BMI : num [1:2938] 19.1 18.6 18.1 17.6 17.2 16.7 16.2 15.7 15.2 14.7 ...
## $ under-five deaths : num [1:2938] 83 86 89 93 97 102 106 110 113 116 ...
## $ Polio : num [1:2938] 6 58 62 67 68 66 63 64 63 58 ...
## $ Total expenditure : num [1:2938] 8.16 8.18 8.13 8.52 7.87 9.2 9.42 8.33 6.73 7.43 ...
## $ Diphtheria : num [1:2938] 65 62 64 67 68 66 63 64 63 58 ...
## $ HIV/AIDS : num [1:2938] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 ...
## $ GDP : num [1:2938] 584.3 612.7 631.7 670 63.5 ...
## $ Population : num [1:2938] 33736494 327582 31731688 3696958 2978599 ...
## $ thinness 1-19 years : num [1:2938] 17.2 17.5 17.7 17.9 18.2 18.4 18.6 18.8 19 19.2 ...
## $ thinness 5-9 years : num [1:2938] 17.3 17.5 17.7 18 18.2 18.4 18.7 18.9 19.1 19.3 ...
## $ Income composition of resources: num [1:2938] 0.479 0.476 0.47 0.463 0.454 0.448 0.434 0.433 0.415 0.405 ...
## $ Schooling : num [1:2938] 10.1 10 9.9 9.8 9.5 9.2 8.9 8.7 8.4 8.1 ...
View(Life_Expectancy_Data)
describe(Life_Expectancy_Data)
## vars n mean sd median
## Country* 1 2938 96.10 56.24 94.00
## Year 2 2938 2007.52 4.61 2008.00
## Status* 3 2938 1.83 0.38 2.00
## Life expectancy 4 2928 69.22 9.52 72.10
## Adult Mortality 5 2928 164.80 124.29 144.00
## infant deaths 6 2938 30.30 117.93 3.00
## Alcohol 7 2744 4.60 4.05 3.76
## percentage expenditure 8 2938 738.25 1987.91 64.91
## Hepatitis B 9 2385 80.94 25.07 92.00
## Measles 10 2938 2419.59 11467.27 17.00
## BMI 11 2904 38.32 20.04 43.50
## under-five deaths 12 2938 42.04 160.45 4.00
## Polio 13 2919 82.55 23.43 93.00
## Total expenditure 14 2712 5.94 2.50 5.76
## Diphtheria 15 2919 82.32 23.72 93.00
## HIV/AIDS 16 2938 1.74 5.08 0.10
## GDP 17 2490 7483.16 14270.17 1766.95
## Population 18 2286 12753375.12 61012096.51 1386542.00
## thinness 1-19 years 19 2904 4.84 4.42 3.30
## thinness 5-9 years 20 2904 4.87 4.51 3.30
## Income composition of resources 21 2771 0.63 0.21 0.68
## Schooling 22 2775 11.99 3.36 12.30
## trimmed mad min max
## Country* 95.90 72.65 1.00 1.930000e+02
## Year 2007.52 5.93 2000.00 2.015000e+03
## Status* 1.91 0.00 1.00 2.000000e+00
## Life expectancy 69.91 8.60 36.30 8.900000e+01
## Adult Mortality 150.51 112.68 1.00 7.230000e+02
## infant deaths 10.20 4.45 0.00 1.800000e+03
## Alcohol 4.23 4.81 0.01 1.787000e+01
## percentage expenditure 230.74 96.24 0.00 1.947991e+04
## Hepatitis B 86.89 8.90 1.00 9.900000e+01
## Measles 286.08 25.20 0.00 2.121830e+05
## BMI 39.05 24.17 1.00 8.730000e+01
## under-five deaths 14.15 5.93 0.00 2.500000e+03
## Polio 88.05 8.90 3.00 9.900000e+01
## Total expenditure 5.85 2.36 0.37 1.760000e+01
## Diphtheria 87.99 8.90 2.00 9.900000e+01
## HIV/AIDS 0.54 0.00 0.10 5.060000e+01
## GDP 3751.73 2360.98 1.68 1.191727e+05
## Population 3953693.58 2012347.06 34.00 1.293859e+09
## thinness 1-19 years 4.14 3.41 0.10 2.770000e+01
## thinness 5-9 years 4.15 3.41 0.10 2.860000e+01
## Income composition of resources 0.65 0.19 0.00 9.500000e-01
## Schooling 12.17 3.11 0.00 2.070000e+01
## range skew kurtosis se
## Country* 1.920000e+02 0.03 -1.22 1.04
## Year 1.500000e+01 -0.01 -1.21 0.09
## Status* 1.000000e+00 -1.72 0.95 0.01
## Life expectancy 5.270000e+01 -0.64 -0.24 0.18
## Adult Mortality 7.220000e+02 1.17 1.74 2.30
## infant deaths 1.800000e+03 9.78 115.76 2.18
## Alcohol 1.786000e+01 0.59 -0.81 0.08
## percentage expenditure 1.947991e+04 4.65 26.51 36.68
## Hepatitis B 9.800000e+01 -1.93 2.76 0.51
## Measles 2.121830e+05 9.43 114.58 211.56
## BMI 8.630000e+01 -0.22 -1.29 0.37
## under-five deaths 2.500000e+03 9.49 109.49 2.96
## Polio 9.600000e+01 -2.10 3.76 0.43
## Total expenditure 1.723000e+01 0.62 1.15 0.05
## Diphtheria 9.700000e+01 -2.07 3.55 0.44
## HIV/AIDS 5.050000e+01 5.39 34.80 0.09
## GDP 1.191711e+05 3.20 12.29 285.98
## Population 1.293859e+09 15.90 297.09 1276079.80
## thinness 1-19 years 2.760000e+01 1.71 3.96 0.08
## thinness 5-9 years 2.850000e+01 1.78 4.34 0.08
## Income composition of resources 9.500000e-01 -1.14 1.38 0.00
## Schooling 2.070000e+01 -0.60 0.88 0.06
missing_table <- data.frame(
Variable = names(Life_Expectancy_Data),
Missing = colSums(is.na(Life_Expectancy_Data))
)
print(missing_table)
## Variable Missing
## Country Country 0
## Year Year 0
## Status Status 0
## Life expectancy Life expectancy 10
## Adult Mortality Adult Mortality 10
## infant deaths infant deaths 0
## Alcohol Alcohol 194
## percentage expenditure percentage expenditure 0
## Hepatitis B Hepatitis B 553
## Measles Measles 0
## BMI BMI 34
## under-five deaths under-five deaths 0
## Polio Polio 19
## Total expenditure Total expenditure 226
## Diphtheria Diphtheria 19
## HIV/AIDS HIV/AIDS 0
## GDP GDP 448
## Population Population 652
## thinness 1-19 years thinness 1-19 years 34
## thinness 5-9 years thinness 5-9 years 34
## Income composition of resources Income composition of resources 167
## Schooling Schooling 163
num_vars <- names(Life_Expectancy_Data)[sapply(Life_Expectancy_Data, is.numeric)]
cat_vars <- names(Life_Expectancy_Data)[sapply(Life_Expectancy_Data, is.character)]
impute_numeric <- function(x) {
if (all(is.na(x))) return(x)
if (abs(skewness(x, na.rm = TRUE)) > 1) {
x[is.na(x)] <- median(x, na.rm = TRUE)
} else {
x[is.na(x)] <- mean(x, na.rm = TRUE)
}
return(x)
}
impute_categorical <- function(x) {
mode_value <- names(sort(table(x), decreasing = TRUE))[1]
x[is.na(x)] <- mode_value
return(x)
}
Life_Expectancy_Imputed <- Life_Expectancy_Data
# Imputasi numerik
for (col in num_vars) {
Life_Expectancy_Imputed[[col]] <- impute_numeric(Life_Expectancy_Imputed[[col]])
}
# Imputasi kategorikal
for (col in cat_vars) {
Life_Expectancy_Imputed[[col]] <- impute_categorical(Life_Expectancy_Imputed[[col]])
}
cat("Missing value sebelum imputasi:\n")
## Missing value sebelum imputasi:
print(colSums(is.na(Life_Expectancy_Data)))
## Country Year
## 0 0
## Status Life expectancy
## 0 10
## Adult Mortality infant deaths
## 10 0
## Alcohol percentage expenditure
## 194 0
## Hepatitis B Measles
## 553 0
## BMI under-five deaths
## 34 0
## Polio Total expenditure
## 19 226
## Diphtheria HIV/AIDS
## 19 0
## GDP Population
## 448 652
## thinness 1-19 years thinness 5-9 years
## 34 34
## Income composition of resources Schooling
## 167 163
cat("\nMissing value sesudah imputasi:\n")
##
## Missing value sesudah imputasi:
print(colSums(is.na(Life_Expectancy_Imputed)))
## Country Year
## 0 0
## Status Life expectancy
## 0 0
## Adult Mortality infant deaths
## 0 0
## Alcohol percentage expenditure
## 0 0
## Hepatitis B Measles
## 0 0
## BMI under-five deaths
## 0 0
## Polio Total expenditure
## 0 0
## Diphtheria HIV/AIDS
## 0 0
## GDP Population
## 0 0
## thinness 1-19 years thinness 5-9 years
## 0 0
## Income composition of resources Schooling
## 0 0
cek_outlier <- 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
sum(x < lower | x > upper, na.rm = TRUE)
}
num_vars <- names(Life_Expectancy_Imputed)[sapply(Life_Expectancy_Imputed, is.numeric)]
outlier_before <- data.frame(
Variable = num_vars,
Outlier_Before = sapply(Life_Expectancy_Imputed[num_vars], cek_outlier)
)
cat("=== OUTLIER SEBELUM PEMBERSIHAN ===\n")
## === OUTLIER SEBELUM PEMBERSIHAN ===
print(outlier_before)
## Variable Outlier_Before
## Year Year 0
## Life expectancy Life expectancy 17
## Adult Mortality Adult Mortality 86
## infant deaths infant deaths 315
## Alcohol Alcohol 3
## percentage expenditure percentage expenditure 389
## Hepatitis B Hepatitis B 322
## Measles Measles 542
## BMI BMI 0
## under-five deaths under-five deaths 394
## Polio Polio 279
## Total expenditure Total expenditure 51
## Diphtheria Diphtheria 298
## HIV/AIDS HIV/AIDS 542
## GDP GDP 445
## Population Population 452
## thinness 1-19 years thinness 1-19 years 100
## thinness 5-9 years thinness 5-9 years 99
## Income composition of resources Income composition of resources 130
## Schooling Schooling 77
df_before_long <- Life_Expectancy_Imputed %>%
select(all_of(num_vars)) %>%
tidyr::pivot_longer(cols = everything(), names_to = "Variable", values_to = "Value")
ggplot(df_before_long, aes(x = "", y = Value)) +
geom_boxplot(
fill = "lightgreen",
color = "darkgreen",
outlier.color = "red",
outlier.size = 1
) +
facet_wrap(~ Variable, scales = "free", ncol = 4) +
theme_minimal(base_size = 11) +
theme(
strip.text = element_text(face = "bold"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
) +
labs(title = "Boxplot Outlier Sebelum Pembersihan", x = NULL, y = "Nilai")
handle_outlier <- 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
return(x)
}
Life_Expectancy_Clean <- Life_Expectancy_Imputed
Life_Expectancy_Clean[num_vars] <-
lapply(Life_Expectancy_Imputed[num_vars], handle_outlier)
outlier_after <- data.frame(
Variable = num_vars,
Outlier_After = sapply(Life_Expectancy_Clean[num_vars], cek_outlier)
)
print(outlier_after)
## Variable Outlier_After
## Year Year 0
## Life expectancy Life expectancy 0
## Adult Mortality Adult Mortality 0
## infant deaths infant deaths 0
## Alcohol Alcohol 0
## percentage expenditure percentage expenditure 0
## Hepatitis B Hepatitis B 0
## Measles Measles 0
## BMI BMI 0
## under-five deaths under-five deaths 0
## Polio Polio 0
## Total expenditure Total expenditure 0
## Diphtheria Diphtheria 0
## HIV/AIDS HIV/AIDS 0
## GDP GDP 0
## Population Population 0
## thinness 1-19 years thinness 1-19 years 0
## thinness 5-9 years thinness 5-9 years 0
## Income composition of resources Income composition of resources 0
## Schooling Schooling 0
df_after_long <- Life_Expectancy_Clean %>%
select(all_of(num_vars)) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Value")
ggplot(df_after_long, aes(x = "", y = Value)) +
geom_boxplot(
fill = "lightgreen",
color = "darkgreen",
outlier.color = "red",
outlier.size = 1
) +
facet_wrap(~ Variable, scales = "free", ncol = 4) +
theme_minimal(base_size = 11) +
theme(
strip.text = element_text(face = "bold"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
) +
labs(title = "Boxplot Setelah Penanganan Outlier", x = NULL, y = "Nilai")
cat_cols <- sapply(Life_Expectancy_Clean, is.character)
cat_cols <- names(Life_Expectancy_Clean)[cat_cols]
cat("Kolom kategorikal yang ditemukan:\n")
## Kolom kategorikal yang ditemukan:
print(cat_cols)
## [1] "Country" "Status"
for (col in cat_cols) {
unique_vals <- unique(Life_Expectancy_Clean[[col]])
if (length(unique_vals) == 2) {
Life_Expectancy_Clean[[col]] <- as.numeric(as.factor(Life_Expectancy_Clean[[col]])) - 1
} else {
cat(paste("Kolom", col, "memiliki lebih dari 2 kategori, perlu one-hot encoding.\n"))
}
}
## Kolom Country memiliki lebih dari 2 kategori, perlu one-hot encoding.
str(Life_Expectancy_Clean)
## tibble [2,938 × 22] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:2938] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Year : num [1:2938] 2015 2014 2013 2012 2011 ...
## $ Status : num [1:2938] 1 1 1 1 1 1 1 1 1 1 ...
## $ Life expectancy : num [1:2938] 65 59.9 59.9 59.5 59.2 58.8 58.6 58.1 57.5 57.3 ...
## $ Adult Mortality : num [1:2938] 263 271 268 272 275 279 281 287 295 295 ...
## $ infant deaths : num [1:2938] 55 55 55 55 55 55 55 55 55 55 ...
## $ Alcohol : num [1:2938] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.03 0.02 0.03 ...
## $ percentage expenditure : num [1:2938] 71.3 73.5 73.2 78.2 7.1 ...
## $ Hepatitis B : num [1:2938] 65 62 64 67 68 66 63 64 63 64 ...
## $ Measles : num [1:2938] 901 492 430 901 901 ...
## $ BMI : num [1:2938] 19.1 18.6 18.1 17.6 17.2 16.7 16.2 15.7 15.2 14.7 ...
## $ under-five deaths : num [1:2938] 70 70 70 70 70 70 70 70 70 70 ...
## $ Polio : num [1:2938] 49.5 58 62 67 68 66 63 64 63 58 ...
## $ Total expenditure : num [1:2938] 8.16 8.18 8.13 8.52 7.87 9.2 9.42 8.33 6.73 7.43 ...
## $ Diphtheria : num [1:2938] 65 62 64 67 68 66 63 64 63 58 ...
## $ HIV/AIDS : num [1:2938] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 ...
## $ GDP : num [1:2938] 584.3 612.7 631.7 670 63.5 ...
## $ Population : num [1:2938] 10832552 327582 10832552 3696958 2978599 ...
## $ thinness 1-19 years : num [1:2938] 15.3 15.3 15.3 15.3 15.3 ...
## $ thinness 5-9 years : num [1:2938] 15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6 ...
## $ Income composition of resources: num [1:2938] 0.479 0.476 0.47 0.463 0.454 0.448 0.434 0.433 0.415 0.405 ...
## $ Schooling : num [1:2938] 10.1 10 9.9 9.8 9.5 9.2 8.9 8.7 8.4 8.1 ...
View(Life_Expectancy_Clean)
set.seed(123)
index_train <- sample(1:nrow(Life_Expectancy_Clean),
size = 0.8 * nrow(Life_Expectancy_Clean))
data_train <- Life_Expectancy_Clean[index_train, ]
data_test <- Life_Expectancy_Clean[-index_train, ]
cat("Jumlah data training:", nrow(data_train), "\n")
## Jumlah data training: 2350
cat("Jumlah data testing :", nrow(data_test), "\n")
## Jumlah data testing : 588
str(data_train)
## tibble [2,350 × 22] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:2350] "Sudan" "Sweden" "Sao Tome and Principe" "Central African Republic" ...
## $ Year : num [1:2350] 2010 2010 2006 2002 2013 ...
## $ Status : num [1:2350] 1 0 1 1 1 0 1 0 1 1 ...
## $ Life expectancy : num [1:2350] 62.5 81.5 64.7 45.6 71 78.7 74.1 81 82.2 79.2 ...
## $ Adult Mortality : num [1:2350] 243 58 22 58 135 8 151 67 6 81 ...
## $ infant deaths : num [1:2350] 55 0 0 17 55 1 4 0 1 0 ...
## $ Alcohol : num [1:2350] 1.77 7.2 5.46 1.47 0.01 ...
## $ percentage expenditure : num [1:2350] 172 778.2 47.1 31.6 52.8 ...
## $ Hepatitis B : num [1:2350] 75 92 75 92 96 92 97 95 97 92 ...
## $ Measles : num [1:2350] 680 6 0 901 237 ...
## $ BMI : num [1:2350] 38.3 57.3 24 17.2 17 ...
## $ under-five deaths : num [1:2350] 70 0 0 25 70 1 4 0 1 1 ...
## $ Polio : num [1:2350] 49.5 98 97 49.5 96 98 97 96 95 95 ...
## $ Total expenditure : num [1:2350] 7.97 9.47 7.84 4.16 2.88 8.46 9.78 8.1 7.81 8.3 ...
## $ Diphtheria : num [1:2350] 49.5 98 97 49.5 96 98 97 96 95 95 ...
## $ HIV/AIDS : num [1:2350] 0.3 0.1 1.2 1.85 0.1 0.1 0.4 0.1 0.1 0.1 ...
## $ GDP : num [1:2350] 1476 5276 844 254 952 ...
## $ Population : num [1:2350] 10832552 9378126 159328 397612 10832552 ...
## $ thinness 1-19 years : num [1:2350] 3.3 1.3 6.9 1.2 15.3 ...
## $ thinness 5-9 years : num [1:2350] 3.3 1.3 6.8 1.2 15.6 1 2.1 0.2 1.1 0.8 ...
## $ Income composition of resources: num [1:2350] 0.461 0.895 0.521 0.315 0.565 0.881 0.614 0.902 0.895 0.83 ...
## $ Schooling : num [1:2350] 7 15.8 10.2 5.4 10 16.5 11.6 18.6 16 15.3 ...
model_vif <- lm(
`Life expectancy` ~
`Adult Mortality` +
`infant deaths` +
Alcohol +
`percentage expenditure` +
`Hepatitis B` +
Measles +
BMI +
`under-five deaths` +
Polio +
`Total expenditure` +
Diphtheria +
`HIV/AIDS` +
GDP +
Population +
`thinness 1-19 years` +
`thinness 5-9 years` +
`Income composition of resources` +
Schooling,
data = Life_Expectancy_Clean
)
vif_values <- vif(model_vif)
vif_df <- data.frame(
Variabel = names(vif_values),
VIF = as.numeric(vif_values)
)
cat("=== HASIL VIF (Vertikal) ===\n")
## === HASIL VIF (Vertikal) ===
print(vif_df)
## Variabel VIF
## 1 `Adult Mortality` 1.716368
## 2 `infant deaths` 106.746383
## 3 Alcohol 1.604219
## 4 `percentage expenditure` 3.819646
## 5 `Hepatitis B` 1.487448
## 6 Measles 1.605689
## 7 BMI 1.859041
## 8 `under-five deaths` 112.466522
## 9 Polio 3.767173
## 10 `Total expenditure` 1.196539
## 11 Diphtheria 3.926215
## 12 `HIV/AIDS` 2.285519
## 13 GDP 4.199764
## 14 Population 1.201641
## 15 `thinness 1-19 years` 9.230862
## 16 `thinness 5-9 years` 9.317550
## 17 `Income composition of resources` 3.452700
## 18 Schooling 4.168052
cat("\n=== INTERPRETASI OTOMATIS ===\n")
##
## === INTERPRETASI OTOMATIS ===
for (i in 1:length(vif_values)) {
if (vif_values[i] < 5) {
cat(names(vif_values)[i], ": Tidak ada indikasi multikolinearitas (VIF < 5)\n")
} else if (vif_values[i] >= 5 & vif_values[i] < 10) {
cat(names(vif_values)[i], ": Ada indikasi multikolinearitas sedang (5 ≤ VIF < 10)\n")
} else {
cat(names(vif_values)[i], ": Multikolinearitas tinggi (VIF ≥ 10)\n")
}
}
## `Adult Mortality` : Tidak ada indikasi multikolinearitas (VIF < 5)
## `infant deaths` : Multikolinearitas tinggi (VIF ≥ 10)
## Alcohol : Tidak ada indikasi multikolinearitas (VIF < 5)
## `percentage expenditure` : Tidak ada indikasi multikolinearitas (VIF < 5)
## `Hepatitis B` : Tidak ada indikasi multikolinearitas (VIF < 5)
## Measles : Tidak ada indikasi multikolinearitas (VIF < 5)
## BMI : Tidak ada indikasi multikolinearitas (VIF < 5)
## `under-five deaths` : Multikolinearitas tinggi (VIF ≥ 10)
## Polio : Tidak ada indikasi multikolinearitas (VIF < 5)
## `Total expenditure` : Tidak ada indikasi multikolinearitas (VIF < 5)
## Diphtheria : Tidak ada indikasi multikolinearitas (VIF < 5)
## `HIV/AIDS` : Tidak ada indikasi multikolinearitas (VIF < 5)
## GDP : Tidak ada indikasi multikolinearitas (VIF < 5)
## Population : Tidak ada indikasi multikolinearitas (VIF < 5)
## `thinness 1-19 years` : Ada indikasi multikolinearitas sedang (5 ≤ VIF < 10)
## `thinness 5-9 years` : Ada indikasi multikolinearitas sedang (5 ≤ VIF < 10)
## `Income composition of resources` : Tidak ada indikasi multikolinearitas (VIF < 5)
## Schooling : Tidak ada indikasi multikolinearitas (VIF < 5)