6. PENGUJIAN HIPOTESIS STATISTIK
# ==============================================
# ANALISIS HARAPAN HIDUP - KODE LENGKAP (FIXED)
# ==============================================
# 1. Setup dan Data
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("ggplot2")) install.packages("ggplot2")
library(tidyverse)
library(ggplot2)
set.seed(123)
create_life_data <- function(n = 1000) {
data.frame(
id = 1:n,
age = sample(0:100, n, replace = TRUE, prob = dnorm(0:100, mean = 50, sd = 20)),
gender = sample(c("Male", "Female"), n, replace = TRUE, prob = c(0.48, 0.52)),
smoker = sample(c("Yes", "No"), n, replace = TRUE, prob = c(0.3, 0.7)),
income_group = sample(c("Low", "Middle", "High"), n, replace = TRUE),
life_expectancy = rnorm(n, mean = 75, sd = 10)
)
}
life_data <- create_life_data(1000)
# 2. Complete Life Expectancy
calculate_complete_life_expectancy <- function(data) {
age_groups <- cut(data$age, breaks = seq(0, 100, by = 10),
labels = paste0(seq(0, 90, by = 10), "-", seq(9, 99, by = 10)))
results <- data %>%
mutate(age_group = age_groups) %>%
group_by(age_group) %>%
summarize(
n = n(),
complete_expectancy = mean(life_expectancy - age, na.rm = TRUE),
sd = sd(life_expectancy - age, na.rm = TRUE)
)
return(results)
}
# 3. Curtate Life Expectancy
calculate_curtate_life_expectancy <- function(data) {
results <- data %>%
mutate(curtate_years = floor(life_expectancy - age)) %>%
group_by(gender) %>%
summarize(
n = n(),
curtate_expectancy = mean(curtate_years, na.rm = TRUE),
complete_expectancy = mean(life_expectancy - age, na.rm = TRUE)
)
return(results)
}
# 4. Pengujian Hipotesis Sederhana
hypothesis_test_gender <- function(data) {
cat("\n=== UJI PERBEDAAN GENDER ===\n")
# Pisahkan data
male <- data %>% filter(gender == "Male") %>% pull(life_expectancy)
female <- data %>% filter(gender == "Female") %>% pull(life_expectancy)
# Uji t sederhana
test_result <- t.test(female, male)
cat("Rata-rata Female:", round(mean(female), 2), "\n")
cat("Rata-rata Male:", round(mean(male), 2), "\n")
cat("Perbedaan:", round(mean(female) - mean(male), 2), "\n")
cat("p-value:", round(test_result$p.value, 4), "\n")
if(test_result$p.value < 0.05) {
cat("KESIMPULAN: Ada perbedaan signifikan (p < 0.05)\n")
} else {
cat("KESIMPULAN: Tidak ada perbedaan signifikan\n")
}
return(test_result)
}
# 5. Visualisasi Sederhana
visualize_life_expectancy <- function(data) {
# Plot 1: Distribusi Harapan Hidup
p1 <- ggplot(data, aes(x = life_expectancy, fill = gender)) +
geom_density(alpha = 0.5) +
labs(title = "Distribusi Harapan Hidup per Gender",
x = "Harapan Hidup (tahun)", y = "Densitas") +
theme_minimal()
# Plot 2: Harapan Hidup vs Usia
p2 <- ggplot(data, aes(x = age, y = life_expectancy, color = smoker)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Hubungan Usia dan Harapan Hidup",
x = "Usia", y = "Harapan Hidup") +
theme_minimal()
# Plot 3: Complete vs Curtate
data_summary <- data %>%
mutate(
complete = life_expectancy - age,
curtate = floor(complete)
) %>%
group_by(gender) %>%
summarize(
complete_mean = mean(complete),
curtate_mean = mean(curtate)
)
p3 <- ggplot(data_summary %>% pivot_longer(cols = -gender),
aes(x = gender, y = value, fill = name)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Complete vs Curtate Life Expectancy",
x = "Gender", y = "Tahun", fill = "Tipe") +
theme_minimal()
# Tampilkan semua plot
print(p1)
print(p2)
print(p3)
}
# 6. Latihan Mandiri Sederhana
simple_exercise <- function(data) {
cat("\n=== LATIHAN MANDIRI ===\n")
# Soal 1: Hitung rata-rata per kelompok
cat("\n1. Rata-rata Harapan Hidup per Kelompok:\n")
summary1 <- data %>%
group_by(gender, smoker) %>%
summarize(
count = n(),
mean_life = mean(life_expectancy),
mean_remaining = mean(life_expectancy - age),
.groups = "drop"
)
print(summary1)
# Soal 2: Analisis sederhana
cat("\n2. Analisis Dasar:\n")
cat(" Total observasi:", nrow(data), "\n")
cat(" Rata-rata usia:", round(mean(data$age), 2), "\n")
cat(" Rata-rata harapan hidup:", round(mean(data$life_expectancy), 2), "\n")
cat(" Rata-rata sisa hidup:", round(mean(data$life_expectancy - data$age), 2), "\n")
# Soal 3: Perhitungan premi sederhana
cat("\n3. Estimasi Premi Asuransi Sederhana:\n")
premium_est <- data %>%
group_by(gender) %>%
summarize(
avg_age = mean(age),
avg_remaining = mean(life_expectancy - age),
# Premi sederhana: semakin muda, semakin murah
premium_index = 1000 / (100 - avg_age) * avg_remaining
)
print(premium_est)
}
# 7. FUNGSI UTAMA YANG PASTI BERJALAN
run_simple_analysis <- function() {
cat("\n", strrep("=", 50), "\n")
cat("ANALISIS HARAPAN HIDUP - VERSI SIMPLE\n")
cat(strrep("=", 50), "\n")
# 1. Buat data
cat("\n1. MEMUAT DATA...\n")
data <- create_life_data(500)
cat(" Sampel:", nrow(data), "observasi\n")
# 2. Statistik dasar
cat("\n2. STATISTIK DASAR:\n")
cat(" Usia rata-rata:", round(mean(data$age), 1), "tahun\n")
cat(" Harapan hidup rata-rata:", round(mean(data$life_expectancy), 1), "tahun\n")
cat(" Sisa hidup rata-rata:", round(mean(data$life_expectancy - data$age), 1), "tahun\n")
# 3. Complete vs Curtate
cat("\n3. PERBANDINGAN COMPLETE vs CURTATE:\n")
data <- data %>%
mutate(
complete = life_expectancy - age,
curtate = floor(complete)
)
comparison <- data %>%
summarize(
mean_complete = mean(complete),
mean_curtate = mean(curtate),
difference = mean_complete - mean_curtate
)
print(comparison)
# 4. Uji hipotesis
cat("\n4. PENGUJIAN HIPOTESIS:\n")
test_result <- hypothesis_test_gender(data)
# 5. Visualisasi
cat("\n5. MEMBUAT VISUALISASI...\n")
visualize_life_expectancy(data)
# 6. Latihan
cat("\n6. LATIHAN:\n")
simple_exercise(data)
cat("\n", strrep("=", 50), "\n")
cat("ANALISIS SELESAI\n")
cat(strrep("=", 50), "\n")
return(list(
data = data,
comparison = comparison,
test_result = test_result
))
}
# ==============================================
# JALANKAN ANALISIS
# ==============================================
# Versi sederhana yang pasti berjalan
results <- run_simple_analysis()
##
## ==================================================
## ANALISIS HARAPAN HIDUP - VERSI SIMPLE
## ==================================================
##
## 1. MEMUAT DATA...
## Sampel: 500 observasi
##
## 2. STATISTIK DASAR:
## Usia rata-rata: 50.7 tahun
## Harapan hidup rata-rata: 74.7 tahun
## Sisa hidup rata-rata: 24 tahun
##
## 3. PERBANDINGAN COMPLETE vs CURTATE:
## mean_complete mean_curtate difference
## 1 23.97151 23.468 0.5035058
##
## 4. PENGUJIAN HIPOTESIS:
##
## === UJI PERBEDAAN GENDER ===
## Rata-rata Female: 74.44
## Rata-rata Male: 75
## Perbedaan: -0.56
## p-value: 0.5168
## KESIMPULAN: Tidak ada perbedaan signifikan
##
## 5. MEMBUAT VISUALISASI...
## `geom_smooth()` using formula = 'y ~ x'
##
## 6. LATIHAN:
##
## === LATIHAN MANDIRI ===
##
## 1. Rata-rata Harapan Hidup per Kelompok:
## # A tibble: 4 × 5
## gender smoker count mean_life mean_remaining
## <chr> <chr> <int> <dbl> <dbl>
## 1 Female No 192 74.3 23.1
## 2 Female Yes 80 74.8 24.5
## 3 Male No 169 75.0 24.1
## 4 Male Yes 59 74.9 25.7
##
## 2. Analisis Dasar:
## Total observasi: 500
## Rata-rata usia: 50.72
## Rata-rata harapan hidup: 74.69
## Rata-rata sisa hidup: 23.97
##
## 3. Estimasi Premi Asuransi Sederhana:
## # A tibble: 2 × 4
## gender avg_age avg_remaining premium_index
## <chr> <dbl> <dbl> <dbl>
## 1 Female 51.0 23.5 479.
## 2 Male 50.4 24.6 496.
##
## ==================================================
## ANALISIS SELESAI
## ==================================================
# Tampilkan hasil
print(results$comparison)
## mean_complete mean_curtate difference
## 1 23.97151 23.468 0.5035058
print(results$test_result)
##
## Welch Two Sample t-test
##
## data: female and male
## t = -0.64883, df = 486.98, p-value = 0.5168
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.254894 1.135369
## sample estimates:
## mean of x mean of y
## 74.43825 74.99802
exercise_life_expectancy <- function() {
cat("\n=== LATIHAN MANDIRI ===\n")
# Buat dataset baru untuk latihan
exercise_data <- create_life_data(500)
# SOAL 1: Hitung harapan hidup lengkap per kelompok pendapatan
cat("\nSOAL 1: Hitung Complete Life Expectancy per Income Group\n")
soal1 <- exercise_data %>%
group_by(income_group) %>%
summarize(
n = n(),
complete_expectancy = mean(life_expectancy - age, na.rm = TRUE),
sd = sd(life_expectancy - age, na.rm = TRUE)
)
print(soal1)
# SOAL 2: Uji apakah kelompok pendapatan tinggi memiliki harapan hidup lebih panjang
cat("\nSOAL 2: Uji perbedaan harapan hidup antar kelompok pendapatan\n")
high_income <- exercise_data %>% filter(income_group == "High")
low_income <- exercise_data %>% filter(income_group == "Low")
t_test_income <- t.test(high_income$life_expectancy,
low_income$life_expectancy,
alternative = "greater")
cat("H0: μ_high ≤ μ_low\n")
cat("H1: μ_high > μ_low\n")
cat("p-value:", t_test_income$p.value, "\n")
cat("Kesimpulan:", ifelse(t_test_income$p.value < 0.05,
"Pendapatan tinggi berhubungan dengan harapan hidup lebih panjang",
"Tidak cukup bukti perbedaan signifikan"), "\n")
# SOAL 3: Hitung hubungan antara Complete dan Curtate
cat("\nSOAL 3: Analisis hubungan e_x dan e_x°\n")
exercise_data <- exercise_data %>%
mutate(
curtate = floor(life_expectancy - age),
complete = life_expectancy - age,
difference = complete - curtate
)
relationship_summary <- exercise_data %>%
summarize(
mean_curtate = mean(curtate),
mean_complete = mean(complete),
mean_difference = mean(difference),
theoretical_difference = 0.5, # Asumsi distribusi uniform
actual_vs_theoretical = mean_difference - theoretical_difference
)
print(relationship_summary)
# SOAL 4: Analisis pengaruh interaksi gender dan smoking
cat("\nSOAL 4: Analisis interaksi Gender × Smoking\n")
interaction_analysis <- exercise_data %>%
group_by(gender, smoker) %>%
summarize(
n = n(),
mean_life = mean(life_expectancy),
mean_remaining = mean(life_expectancy - age),
sd_life = sd(life_expectancy)
)
print(interaction_analysis)
# Visualisasi interaksi
p <- ggplot(interaction_analysis,
aes(x = interaction(gender, smoker),
y = mean_life,
fill = gender)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean_life - sd_life,
ymax = mean_life + sd_life),
width = 0.2, position = position_dodge(0.9)) +
labs(title = "Interaksi Gender dan Smoking terhadap Harapan Hidup",
x = "Kombinasi Gender-Smoking",
y = "Rata-rata Harapan Hidup (tahun)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
print(p)
return(list(
soal1 = soal1,
t_test_income = t_test_income,
relationship = relationship_summary,
interaction = interaction_analysis
))
}
# Jalankan latihan mandiri
exercise_results <- exercise_life_expectancy()
##
## === LATIHAN MANDIRI ===
##
## SOAL 1: Hitung Complete Life Expectancy per Income Group
## # A tibble: 3 × 4
## income_group n complete_expectancy sd
## <chr> <int> <dbl> <dbl>
## 1 High 168 24.8 20.5
## 2 Low 168 25.0 20.4
## 3 Middle 164 28.6 23.6
##
## SOAL 2: Uji perbedaan harapan hidup antar kelompok pendapatan
## H0: μ_high ≤ μ_low
## H1: μ_high > μ_low
## p-value: 0.1320049
## Kesimpulan: Tidak cukup bukti perbedaan signifikan
##
## SOAL 3: Analisis hubungan e_x dan e_x°
## mean_curtate mean_complete mean_difference theoretical_difference
## 1 25.622 26.09804 0.4760433 0.5
## actual_vs_theoretical
## 1 -0.02395667
##
## SOAL 4: Analisis interaksi Gender × Smoking
## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.
## # A tibble: 4 × 6
## # Groups: gender [2]
## gender smoker n mean_life mean_remaining sd_life
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 Female No 181 75.5 26.4 10.2
## 2 Female Yes 73 77.0 27.0 11.0
## 3 Male No 163 73.9 25.5 11.3
## 4 Male Yes 83 74.0 25.8 9.41
exercise_results
## $soal1
## # A tibble: 3 × 4
## income_group n complete_expectancy sd
## <chr> <int> <dbl> <dbl>
## 1 High 168 24.8 20.5
## 2 Low 168 25.0 20.4
## 3 Middle 164 28.6 23.6
##
## $t_test_income
##
## Welch Two Sample t-test
##
## data: high_income$life_expectancy and low_income$life_expectancy
## t = 1.1189, df = 328.33, p-value = 0.132
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -0.6108233 Inf
## sample estimates:
## mean of x mean of y
## 75.12684 73.83886
##
##
## $relationship
## mean_curtate mean_complete mean_difference theoretical_difference
## 1 25.622 26.09804 0.4760433 0.5
## actual_vs_theoretical
## 1 -0.02395667
##
## $interaction
## # A tibble: 4 × 6
## # Groups: gender [2]
## gender smoker n mean_life mean_remaining sd_life
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 Female No 181 75.5 26.4 10.2
## 2 Female Yes 73 77.0 27.0 11.0
## 3 Male No 163 73.9 25.5 11.3
## 4 Male Yes 83 74.0 25.8 9.41
# 1. Setup dan Data
#if (!require("tidyverse")) install.packages("tidyverse")
#if (!require("ggplot2")) install.packages("ggplot2")
library(tidyverse)
library(ggplot2)
set.seed(123)
# Fungsi buat data sederhana
create_simple_life_data <- function(n = 500) {
data.frame(
id = 1:n,
age = sample(20:80, n, replace = TRUE),
gender = sample(c("Male", "Female"), n, replace = TRUE, prob = c(0.48, 0.52)),
smoker = sample(c("Yes", "No"), n, replace = TRUE, prob = c(0.3, 0.7)),
life_expectancy = rnorm(n, mean = 75, sd = 10)
)
}
# 2. Fungsi utama yang sederhana
simple_life_analysis <- function() {
cat("==================================================\n")
cat("ANALISIS HARAPAN HIDUP - VERSI SIMPLE\n")
cat("==================================================\n\n")
# Buat data
cat("1. MEMBUAT DATA...\n")
data <- create_simple_life_data(300)
cat(" Jumlah data:", nrow(data), "\n")
cat(" Variabel: age, gender, smoker, life_expectancy\n\n")
# Hitung complete dan curtate
cat("2. PERHITUNGAN HARAPAN HIDUP\n")
data$complete <- data$life_expectancy - data$age
data$curtate <- floor(data$complete)
# Statistik dasar
cat(" Rata-rata usia:", round(mean(data$age), 1), "\n")
cat(" Rata-rata harapan hidup:", round(mean(data$life_expectancy), 1), "\n")
cat(" Complete Life Expectancy (e_x°):", round(mean(data$complete), 2), "\n")
cat(" Curtate Life Expectancy (e_x):", round(mean(data$curtate), 2), "\n")
cat(" Perbedaan (e_x° - e_x):", round(mean(data$complete) - mean(data$curtate), 2), "\n")
cat(" (Teori: e_x° ≈ e_x + 0.5)\n\n")
# 3. Pengujian hipotesis
cat("3. PENGUJIAN HIPOTESIS\n")
# Hipotesis 1: Perbedaan gender
cat(" Hipotesis 1: Apakah perempuan hidup lebih lama?\n")
male_le <- data$life_expectancy[data$gender == "Male"]
female_le <- data$life_expectancy[data$gender == "Female"]
t_test_gender <- t.test(female_le, male_le)
cat(" Rata-rata Female:", round(mean(female_le), 2), "\n")
cat(" Rata-rata Male:", round(mean(male_le), 2), "\n")
cat(" p-value:", round(t_test_gender$p.value, 4), "\n")
if (t_test_gender$p.value < 0.05) {
cat(" KESIMPULAN: Signifikan (p < 0.05) - Perempuan hidup lebih lama\n")
} else {
cat(" KESIMPULAN: Tidak signifikan - Tidak cukup bukti\n")
}
cat("\n")
# Hipotesis 2: Pengaruh merokok
cat(" Hipotesis 2: Apakah merokok mengurangi harapan hidup?\n")
smoker_le <- data$life_expectancy[data$smoker == "Yes"]
nonsmoker_le <- data$life_expectancy[data$smoker == "No"]
t_test_smoke <- t.test(nonsmoker_le, smoker_le, alternative = "greater")
cat(" Rata-rata Non-Smoker:", round(mean(nonsmoker_le), 2), "\n")
cat(" Rata-rata Smoker:", round(mean(smoker_le), 2), "\n")
cat(" p-value:", round(t_test_smoke$p.value, 4), "\n")
if (t_test_smoke$p.value < 0.05) {
cat(" KESIMPULAN: Signifikan - Merokok mengurangi harapan hidup\n")
} else {
cat(" KESIMPULAN: Tidak signifikan\n")
}
cat("\n")
# 4. Visualisasi
cat("4. VISUALISASI DATA\n")
# Plot 1: Distribusi harapan hidup
p1 <- ggplot(data, aes(x = life_expectancy, fill = gender)) +
geom_histogram(alpha = 0.7, bins = 20, position = "identity") +
labs(title = "Distribusi Harapan Hidup per Gender",
x = "Harapan Hidup (tahun)",
y = "Frekuensi") +
theme_minimal()
# Plot 2: Complete vs Curtate
summary_data <- data.frame(
Type = c("Complete (e_x°)", "Curtate (e_x)"),
Value = c(mean(data$complete), mean(data$curtate))
)
p2 <- ggplot(summary_data, aes(x = Type, y = Value, fill = Type)) +
geom_bar(stat = "identity") +
labs(title = "Perbandingan Complete vs Curtate Life Expectancy",
x = "Tipe Harapan Hidup",
y = "Rata-rata (tahun)") +
theme_minimal()
# Plot 3: Hubungan usia dan sisa hidup
p3 <- ggplot(data, aes(x = age, y = complete)) +
geom_point(aes(color = smoker), alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(title = "Hubungan Usia dan Sisa Harapan Hidup",
x = "Usia",
y = "Sisa Harapan Hidup (tahun)") +
theme_minimal()
print(p1)
print(p2)
print(p3)
# 5. Latihan sederhana
cat("5. LATIHAN MANDIRI\n")
cat(" a. Hitung harapan hidup per kelompok gender dan smoker:\n")
exercise_table <- data %>%
group_by(gender, smoker) %>%
summarize(
n = n(),
mean_age = mean(age),
mean_life = mean(life_expectancy),
mean_remaining = mean(complete),
.groups = "drop"
)
print(exercise_table)
cat("\n b. Hitung premi asuransi sederhana (asumsi):\n")
cat(" Premi = 1,000,000 / (100 - usia) * sisa_hidup\n")
data$premium <- 1000000 / (100 - data$age) * data$complete
premium_summary <- data %>%
group_by(gender, smoker) %>%
summarize(
avg_premium = mean(premium),
.groups = "drop"
)
print(premium_summary)
# 6. Ringkasan
cat("\n==================================================\n")
cat("RINGKASAN\n")
cat("==================================================\n")
cat("1. Complete Life Expectancy:", round(mean(data$complete), 2), "tahun\n")
cat("2. Curtate Life Expectancy:", round(mean(data$curtate), 2), "tahun\n")
cat("3. Selisih:", round(mean(data$complete) - mean(data$curtate), 2), "tahun\n")
cat("4. Harapan hidup tertinggi:",
ifelse(mean(female_le) > mean(male_le), "Perempuan", "Laki-laki"), "\n")
cat("5. Premi tertinggi:",
premium_summary$gender[which.max(premium_summary$avg_premium)],
"yang", premium_summary$smoker[which.max(premium_summary$avg_premium)], "\n")
cat("==================================================\n")
# Return hasil
return(list(
data = data,
t_test_gender = t_test_gender,
t_test_smoke = t_test_smoke,
summary = exercise_table,
premium = premium_summary
))
}
# ==============================================
# JALANKAN ANALISIS
# ==============================================
# Jalankan fungsi utama
hasil <- simple_life_analysis()
## ==================================================
## ANALISIS HARAPAN HIDUP - VERSI SIMPLE
## ==================================================
##
## 1. MEMBUAT DATA...
## Jumlah data: 300
## Variabel: age, gender, smoker, life_expectancy
##
## 2. PERHITUNGAN HARAPAN HIDUP
## Rata-rata usia: 50.3
## Rata-rata harapan hidup: 74.4
## Complete Life Expectancy (e_x°): 24.1
## Curtate Life Expectancy (e_x): 23.58
## Perbedaan (e_x° - e_x): 0.51
## (Teori: e_x° ≈ e_x + 0.5)
##
## 3. PENGUJIAN HIPOTESIS
## Hipotesis 1: Apakah perempuan hidup lebih lama?
## Rata-rata Female: 73.56
## Rata-rata Male: 75.39
## p-value: 0.1067
## KESIMPULAN: Tidak signifikan - Tidak cukup bukti
##
## Hipotesis 2: Apakah merokok mengurangi harapan hidup?
## Rata-rata Non-Smoker: 74.67
## Rata-rata Smoker: 73.84
## p-value: 0.2471
## KESIMPULAN: Tidak signifikan
##
## 4. VISUALISASI DATA
## `geom_smooth()` using formula = 'y ~ x'
## 5. LATIHAN MANDIRI
## a. Hitung harapan hidup per kelompok gender dan smoker:
## # A tibble: 4 × 6
## gender smoker n mean_age mean_life mean_remaining
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 Female No 119 51.0 73.5 22.5
## 2 Female Yes 42 50.4 73.9 23.5
## 3 Male No 88 52.1 76.3 24.2
## 4 Male Yes 51 45.6 73.8 28.2
##
## b. Hitung premi asuransi sederhana (asumsi):
## Premi = 1,000,000 / (100 - usia) * sisa_hidup
## # A tibble: 4 × 3
## gender smoker avg_premium
## <chr> <chr> <dbl>
## 1 Female No 385756.
## 2 Female Yes 378156.
## 3 Male No 408068.
## 4 Male Yes 443939.
##
## ==================================================
## RINGKASAN
## ==================================================
## 1. Complete Life Expectancy: 24.1 tahun
## 2. Curtate Life Expectancy: 23.58 tahun
## 3. Selisih: 0.51 tahun
## 4. Harapan hidup tertinggi: Laki-laki
## 5. Premi tertinggi: Male yang Yes
## ==================================================
hasil
## $data
## id age gender smoker life_expectancy complete curtate premium
## 1 1 50 Female No 60.34790 10.34790216 10 206958.043
## 2 2 34 Female No 58.76100 24.76100388 24 375166.725
## 3 3 70 Female No 76.92297 6.92296879 6 230765.626
## 4 4 33 Male No 92.44520 59.44520141 59 887241.812
## 5 5 22 Female Yes 75.78813 53.78813039 53 689591.415
## 6 6 61 Female No 70.31052 9.31051523 9 238731.160
## 7 7 69 Male Yes 57.71167 -11.28832991 -12 -364139.674
## 8 8 73 Male Yes 85.65154 12.65154397 12 468575.703
## 9 9 62 Female Yes 79.80282 17.80281766 17 468495.202
## 10 10 56 Female Yes 76.37499 20.37499039 20 463067.963
## 11 11 71 Male No 82.88957 11.88957450 11 409985.328
## 12 12 33 Male Yes 66.72920 33.72920084 33 503420.908
## 13 13 73 Female Yes 77.43944 4.43944151 4 164423.760
## 14 14 44 Female Yes 65.00137 21.00136975 21 375024.460
## 15 15 45 Male No 86.41415 41.41415006 41 752984.546
## 16 16 46 Female No 55.17088 9.17088250 9 169831.157
## 17 17 24 Female No 74.75493 50.75493252 50 667828.059
## 18 18 70 Male No 72.91848 2.91847532 2 97282.511
## 19 19 46 Female Yes 65.12951 19.12950593 19 354250.110
## 20 20 76 Female No 70.92484 -5.07516207 -6 -211465.086
## 21 21 47 Male No 69.87220 22.87219594 22 431550.867
## 22 22 76 Male Yes 64.88440 -11.11560354 -12 -463150.147
## 23 23 28 Female Yes 57.93598 29.93597943 29 415777.492
## 24 24 48 Male No 92.81877 44.81876899 44 861899.404
## 25 25 54 Male Yes 59.55739 5.55739433 5 120812.920
## 26 26 27 Female No 73.13191 46.13190846 46 631943.951
## 27 27 45 Female No 73.73328 28.73327766 28 522423.230
## 28 28 26 Male Yes 64.19582 38.19581503 38 516159.663
## 29 29 61 Male Yes 76.74957 15.74956963 15 403835.119
## 30 30 28 Male Yes 63.09004 35.09004255 35 487361.702
## 31 31 38 Male No 74.45754 36.45753660 36 588024.784
## 32 32 55 Female No 58.15334 3.15334352 3 70074.300
## 33 33 33 Female Yes 68.85397 35.85396933 35 535133.871
## 34 34 36 Male Yes 78.67083 42.67083316 42 666731.768
## 35 35 62 Female No 50.42657 -11.57342826 -12 -304563.902
## 36 36 58 Female No 72.95665 14.95664659 14 356110.633
## 37 37 72 Female No 80.58336 8.58336203 8 306548.644
## 38 38 31 Male Yes 78.24211 47.24210607 47 684668.204
## 39 39 34 Female No 81.97737 47.97737006 47 726929.849
## 40 40 51 Female No 64.77427 13.77426937 13 281107.538
## 41 41 61 Male No 73.31729 12.31729241 12 315828.011
## 42 42 64 Female Yes 67.40511 3.40510856 3 94586.349
## 43 43 26 Male Yes 72.47091 46.47090763 46 627985.238
## 44 44 28 Female No 84.52680 56.52679991 56 785094.443
## 45 45 60 Female No 72.24585 12.24585102 12 306146.275
## 46 46 29 Male Yes 62.67194 33.67193961 33 474252.671
## 47 47 42 Male Yes 77.37504 35.37503584 35 609914.411
## 48 48 46 Male Yes 85.51259 39.51259289 39 731714.683
## 49 49 79 Female No 74.44542 -4.55457945 -5 -216884.736
## 50 50 72 Female No 69.62323 -2.37676867 -3 -84884.596
## 51 51 26 Male No 73.50382 47.50382315 47 641943.556
## 52 52 72 Female Yes 70.90086 -1.09914409 -2 -39255.146
## 53 53 46 Female Yes 75.17542 29.17541600 29 540285.481
## 54 54 51 Male No 82.51041 31.51041104 31 643069.613
## 55 55 57 Female Yes 78.62371 21.62371124 21 502877.006
## 56 56 44 Female No 69.14851 25.14851130 25 449080.559
## 57 57 53 Male No 83.15959 30.15959483 30 641693.507
## 58 58 48 Female Yes 72.33063 24.33063406 24 467896.809
## 59 59 24 Female No 70.28279 46.28279337 46 608984.123
## 60 60 27 Female No 73.88195 46.88194869 46 642218.475
## 61 61 31 Female No 76.38753 45.38752680 45 657790.244
## 62 62 32 Female No 76.08582 44.08581793 44 648320.852
## 63 63 37 Female No 63.74970 26.74969883 26 424598.394
## 64 64 52 Male No 76.20595 24.20595490 24 504290.727
## 65 65 76 Female No 76.49818 0.49817935 0 20757.473
## 66 66 46 Male No 73.68602 27.68601659 27 512704.011
## 67 67 44 Male No 80.81616 36.81615900 36 657431.411
## 68 68 57 Male Yes 75.53327 18.53326870 18 431006.249
## 69 69 40 Female No 60.66523 20.66523332 20 344420.555
## 70 70 34 Female No 52.77009 18.77009350 18 284395.356
## 71 71 60 Male No 99.38003 39.38003047 39 984500.762
## 72 72 66 Male Yes 84.41265 18.41265109 18 541548.562
## 73 73 45 Male Yes 75.98949 30.98949299 30 563445.327
## 74 74 79 Female Yes 69.06550 -9.93450389 -10 -473071.614
## 75 75 50 Male Yes 68.04705 18.04705064 18 360941.013
## 76 76 35 Male Yes 80.70353 45.70353337 45 703131.283
## 77 77 71 Male No 65.57792 -5.42208165 -6 -186968.333
## 78 78 49 Female No 71.67901 22.67901060 22 444686.482
## 79 79 25 Female No 80.62611 55.62611271 55 741681.503
## 80 80 62 Female No 64.00915 2.00914549 2 52872.250
## 81 81 27 Female No 76.55948 49.55948423 49 678897.044
## 82 82 41 Male No 85.31787 44.31787176 44 751150.369
## 83 83 41 Female No 83.43324 42.43324032 42 719207.463
## 84 84 58 Male No 78.29015 20.29014833 20 483098.770
## 85 85 50 Male No 56.18321 6.18321289 6 123664.258
## 86 86 67 Female No 71.29311 4.29311220 4 130094.309
## 87 87 36 Female No 65.53461 29.53460861 29 461478.260
## 88 88 73 Female No 72.81591 -0.18409405 -1 -6818.298
## 89 89 69 Female No 68.92894 -0.07106249 -1 -2292.339
## 90 90 68 Female No 56.09366 -11.90633913 -12 -372073.098
## 91 91 53 Male No 74.50164 21.50163749 21 457481.649
## 92 92 23 Male Yes 60.88209 37.88208506 37 491975.131
## 93 93 32 Female No 63.70845 31.70845313 31 466300.781
## 94 94 24 Male No 81.97000 57.96999800 57 762763.132
## 95 95 73 Male Yes 78.17461 5.17460726 5 191652.121
## 96 96 70 Female No 66.58064 -3.41936393 -4 -113978.798
## 97 97 44 Female No 81.06060 37.06060494 37 661796.517
## 98 98 71 Male No 81.56468 10.56468109 10 364299.348
## 99 99 41 Female No 75.73776 34.73776168 34 588775.622
## 100 100 44 Female No 90.35197 46.35197483 46 827713.836
## 101 101 51 Male No 79.13916 28.13916298 28 574268.632
## 102 102 65 Female No 84.39767 19.39767493 19 554219.284
## 103 103 44 Male No 72.23882 28.23882168 28 504264.673
## 104 104 42 Female No 68.07489 26.07489035 26 449567.075
## 105 105 54 Female Yes 90.36321 36.36321087 36 790504.584
## 106 106 59 Female No 76.72731 17.72731485 17 432373.533
## 107 107 67 Female No 66.32396 -0.67603807 -1 -20486.002
## 108 108 49 Male No 70.14097 21.14096778 21 414528.780
## 109 109 31 Female No 59.28478 28.28477806 28 409924.320
## 110 110 50 Female Yes 76.72144 26.72144429 26 534428.886
## 111 111 76 Female No 76.63136 0.63136413 0 26306.839
## 112 112 65 Male No 84.14156 19.14155854 19 546901.673
## 113 113 49 Male Yes 76.70645 27.70644565 27 543263.640
## 114 114 54 Male Yes 81.82201 27.82201448 27 604826.402
## 115 115 33 Female Yes 69.40793 36.40793154 36 543401.963
## 116 116 48 Male No 68.55563 20.55563070 20 395300.590
## 117 117 51 Male Yes 72.60513 21.60512723 21 440920.964
## 118 118 26 Female Yes 91.82444 65.82444093 65 889519.472
## 119 119 22 Female No 72.61187 50.61186926 50 648870.119
## 120 120 42 Female Yes 78.27631 36.27631386 36 625453.687
## 121 121 73 Male No 64.14768 -8.85231827 -9 -327863.639
## 122 122 77 Male No 77.51060 0.51059551 0 22199.805
## 123 123 34 Male Yes 88.98075 54.98075147 54 833041.689
## 124 124 40 Female No 71.73383 31.73382877 31 528897.146
## 125 125 56 Male Yes 85.55361 29.55361234 29 671673.008
## 126 126 27 Female No 66.93572 39.93572018 39 547064.660
## 127 127 70 Male No 66.84404 -3.15596411 -4 -105198.804
## 128 128 29 Female Yes 67.98522 38.98521580 38 549087.546
## 129 129 69 Female Yes 60.42355 -8.57645431 -9 -276659.816
## 130 130 61 Female No 69.37705 8.37705273 8 214796.224
## 131 131 63 Female No 94.17803 31.17803049 31 842649.473
## 132 132 53 Female Yes 72.51345 19.51345063 19 415179.801
## 133 133 29 Male No 88.80644 59.80643747 59 842344.190
## 134 134 41 Male No 76.20607 35.20607089 35 596713.066
## 135 135 31 Female No 72.39518 41.39518012 41 599930.147
## 136 136 39 Male No 78.44850 39.44849936 39 646696.711
## 137 137 65 Male No 66.95393 1.95392956 1 55826.559
## 138 138 36 Male Yes 86.08556 50.08556092 50 782586.889
## 139 139 65 Female No 63.96921 -1.03079161 -2 -29451.189
## 140 140 73 Female No 83.01130 10.01130423 10 370789.046
## 141 141 54 Male Yes 77.38551 23.38550624 23 508380.571
## 142 142 59 Male No 76.38502 17.38501860 17 424024.844
## 143 143 65 Female No 82.26280 17.26280300 17 493222.943
## 144 144 70 Female No 66.41004 -3.58995725 -4 -119665.242
## 145 145 49 Female No 62.72416 13.72416064 13 269101.189
## 146 146 34 Female No 72.08145 38.08144599 38 576991.606
## 147 147 43 Male No 87.12737 44.12736728 44 774164.338
## 148 148 68 Male No 80.77182 12.77182206 12 399119.439
## 149 149 42 Male Yes 97.80428 55.80428421 55 962142.831
## 150 150 62 Female No 64.95890 2.95889915 2 77865.767
## 151 151 26 Female No 76.05948 50.05948239 50 676479.492
## 152 152 48 Male No 105.57125 57.57125296 57 1107139.480
## 153 153 34 Female No 75.19743 41.19742651 41 624203.432
## 154 154 42 Female No 83.91496 41.91495753 41 722671.682
## 155 155 45 Female No 83.49443 38.49443442 38 699898.808
## 156 156 57 Male No 91.10905 34.10904824 34 793233.680
## 157 157 65 Female No 75.55300 10.55299946 10 301514.270
## 158 158 51 Female No 87.05396 36.05396448 36 735795.193
## 159 159 26 Male No 57.53716 31.53715509 31 426177.771
## 160 160 46 Female No 67.48127 21.48126956 21 397801.288
## 161 161 61 Male No 75.77622 14.77622246 14 378877.499
## 162 162 24 Female Yes 69.09895 45.09894856 45 593407.218
## 163 163 25 Male Yes 77.24218 52.24218285 52 696562.438
## 164 164 35 Female No 57.84396 22.84396057 22 351445.547
## 165 165 43 Male Yes 80.35228 37.35227760 37 655303.116
## 166 166 51 Male No 70.07461 19.07460532 19 389277.660
## 167 167 40 Female No 70.37173 30.37172723 30 506195.454
## 168 168 74 Male No 73.49644 -0.50356069 -1 -19367.719
## 169 169 30 Female No 69.15030 39.15029581 39 559289.940
## 170 170 55 Female No 57.73492 2.73491803 2 60775.956
## 171 171 63 Male Yes 81.04025 18.04024959 18 487574.313
## 172 172 65 Female No 75.26877 10.26876990 10 293393.426
## 173 173 79 Male Yes 44.53033 -34.46966569 -35 -1641412.652
## 174 174 80 Female No 86.07528 6.07527710 6 303763.855
## 175 175 38 Male No 68.08325 30.08324879 30 485213.690
## 176 176 44 Male Yes 76.95710 32.95709640 32 588519.579
## 177 177 58 Female No 77.92254 19.92254428 19 474346.292
## 178 178 73 Female No 64.48104 -8.51896381 -9 -315517.178
## 179 179 45 Male No 59.90306 14.90305601 14 270964.655
## 180 180 28 Female No 90.32718 62.32718319 62 865655.322
## 181 181 26 Male Yes 94.23694 68.23693661 68 922120.765
## 182 182 53 Male Yes 80.98443 27.98443438 27 595413.497
## 183 183 67 Female No 76.66663 9.66663054 9 292928.198
## 184 184 32 Female No 76.37199 44.37198837 44 652529.241
## 185 185 38 Male No 78.13710 40.13709510 40 647372.502
## 186 186 75 Female Yes 72.26401 -2.73598781 -3 -109439.512
## 187 187 71 Male No 85.48433 14.48432867 14 499459.609
## 188 188 66 Female No 81.93806 15.93805852 15 468766.427
## 189 189 58 Female No 69.04932 11.04932014 11 263079.051
## 190 190 23 Male No 76.23842 53.23841943 53 691408.045
## 191 191 20 Male No 75.37522 55.37522092 55 692190.261
## 192 192 59 Female No 75.65903 16.65903233 16 406317.862
## 193 193 49 Female No 85.28351 36.28351215 36 711441.415
## 194 194 49 Female No 69.85139 20.85139277 20 408850.839
## 195 195 71 Female No 81.23276 10.23276425 10 352853.940
## 196 196 44 Female No 76.73953 32.73953094 32 584634.481
## 197 197 35 Female Yes 74.65219 39.65219115 39 610033.710
## 198 198 43 Male No 84.80824 41.80823565 41 733477.818
## 199 199 73 Female No 76.38592 3.38592210 3 125404.522
## 200 200 30 Male Yes 64.03830 34.03830494 34 486261.499
## 201 201 67 Female No 62.16291 -4.83708808 -5 -146578.427
## 202 202 39 Female No 71.57481 32.57480909 32 534013.264
## 203 203 59 Female No 67.83601 8.83601292 8 215512.510
## 204 204 22 Male No 62.60655 40.60654733 40 520596.761
## 205 205 48 Female Yes 68.41491 20.41491398 20 392594.500
## 206 206 55 Male Yes 56.73686 1.73686495 1 38596.999
## 207 207 71 Female No 70.81396 -0.18603923 -1 -6415.146
## 208 208 63 Female No 71.33796 8.33795989 8 225350.267
## 209 209 41 Male No 72.05999 31.05998887 31 526440.489
## 210 210 68 Female No 74.76877 6.76877367 6 211524.177
## 211 211 61 Male No 78.62399 17.62399384 17 451897.278
## 212 212 78 Female No 70.33286 -7.66714383 -8 -348506.538
## 213 213 39 Female No 77.98514 38.98513610 38 639100.592
## 214 214 30 Female No 65.47292 35.47291853 35 506755.979
## 215 215 76 Female Yes 81.67541 5.67540579 5 236475.241
## 216 216 74 Female Yes 75.87239 1.87238539 1 72014.823
## 217 217 27 Male No 73.90791 46.90790552 46 642574.048
## 218 218 65 Male No 68.22508 3.22507803 3 92145.087
## 219 219 40 Female No 91.98823 51.98822656 51 866470.443
## 220 220 64 Male No 78.85831 14.85831002 14 412730.834
## 221 221 21 Female Yes 77.46757 56.46757029 56 714779.371
## 222 222 62 Male No 49.13068 -12.86931756 -13 -338666.252
## 223 223 32 Male Yes 55.36897 23.36896980 23 343661.321
## 224 224 65 Female No 74.38695 9.38695172 9 268198.620
## 225 225 25 Male Yes 83.95253 58.95252538 58 786033.672
## 226 226 76 Female No 74.71941 -1.28058771 -2 -53357.821
## 227 227 27 Male No 65.00253 38.00252983 38 520582.600
## 228 228 63 Female Yes 73.40396 10.40396039 10 281188.119
## 229 229 51 Male No 69.72306 18.72306352 18 382103.337
## 230 230 77 Male No 77.16156 0.16156456 0 7024.546
## 231 231 55 Female No 65.81045 10.81045020 10 240232.227
## 232 232 64 Male Yes 65.99405 1.99405345 1 55390.374
## 233 233 80 Female Yes 77.88609 -2.11391209 -3 -105695.605
## 234 234 33 Male No 84.28885 51.28884794 51 765505.193
## 235 235 72 Female No 91.73047 19.73047212 19 704659.719
## 236 236 35 Male No 77.85514 42.85514424 42 659309.911
## 237 237 42 Male No 71.27418 29.27417559 29 504727.165
## 238 238 80 Female Yes 60.66117 -19.33882756 -20 -966941.378
## 239 239 52 Male No 66.15532 14.15532080 14 294902.517
## 240 240 59 Male No 86.24183 27.24182941 27 664434.864
## 241 241 59 Female No 67.77194 8.77194203 8 213949.806
## 242 242 29 Female No 79.62665 50.62664957 50 713051.402
## 243 243 44 Female Yes 71.87446 27.87446470 27 497758.298
## 244 244 27 Male Yes 83.48254 56.48254497 56 773733.493
## 245 245 37 Male Yes 74.14680 37.14680423 37 589631.813
## 246 246 80 Male No 67.84655 -12.15345095 -13 -607672.548
## 247 247 72 Male No 67.63304 -4.36696068 -5 -155962.881
## 248 248 28 Male Yes 74.92777 46.92777361 46 651774.633
## 249 249 26 Male No 82.00478 56.00477870 56 756821.334
## 250 250 26 Female No 91.26392 65.26392345 65 881944.911
## 251 251 77 Female Yes 91.24661 14.24660897 14 619417.781
## 252 252 80 Female No 89.18685 9.18685299 9 459342.649
## 253 253 79 Male No 65.09507 -13.90493419 -14 -662139.724
## 254 254 29 Male No 81.13368 52.13368245 52 734277.218
## 255 255 43 Female No 70.11174 27.11173769 27 475644.521
## 256 256 73 Male No 70.77413 -2.22587281 -3 -82439.734
## 257 257 42 Male No 77.03246 35.03245676 35 604007.875
## 258 258 45 Female No 89.95230 44.95229991 44 817314.544
## 259 259 62 Male No 62.14715 0.14714847 0 3872.328
## 260 260 52 Male No 96.46510 44.46510413 44 926356.336
## 261 261 76 Male No 73.37354 -2.62645541 -3 -109435.642
## 262 262 80 Male No 69.76618 -10.23382144 -11 -511691.072
## 263 263 48 Male No 83.43447 35.43446866 35 681432.090
## 264 264 70 Male Yes 71.98692 1.98691666 1 66230.555
## 265 265 29 Female No 85.87842 56.87841508 56 801104.438
## 266 266 72 Male Yes 75.40935 3.40934820 3 121762.436
## 267 267 73 Male No 77.86535 4.86535252 4 180198.241
## 268 268 32 Female No 74.95887 42.95886593 42 631748.028
## 269 269 62 Female No 81.86813 19.86813204 19 522845.580
## 270 270 30 Male Yes 73.55320 43.55320420 43 622188.631
## 271 271 44 Female No 68.87922 24.87922065 24 444271.797
## 272 272 71 Male No 81.30565 10.30565378 10 355367.372
## 273 273 45 Male Yes 63.07297 18.07297427 18 328599.532
## 274 274 26 Male No 83.34967 57.34967448 57 774995.601
## 275 275 44 Female No 80.97328 36.97328115 36 660237.163
## 276 276 42 Male Yes 63.19187 21.19187309 21 365377.122
## 277 277 45 Female Yes 81.67472 36.67471782 36 666813.051
## 278 278 51 Female No 66.21800 15.21800461 15 310571.523
## 279 279 39 Male No 85.97471 46.97471316 46 770077.265
## 280 280 80 Male No 64.08962 -15.91038436 -16 -795519.218
## 281 281 43 Male No 74.07024 31.07024469 31 545092.012
## 282 282 76 Male No 59.90417 -16.09583205 -17 -670659.669
## 283 283 28 Female Yes 72.36108 44.36108052 44 616126.118
## 284 284 60 Male No 80.98658 20.98657541 20 524664.385
## 285 285 56 Female No 108.04330 52.04329844 52 1182802.237
## 286 286 42 Female Yes 84.47105 42.47104778 42 732259.444
## 287 287 33 Male Yes 62.84598 29.84598475 29 445462.459
## 288 288 65 Female No 83.92175 18.92175371 18 540621.535
## 289 289 25 Female No 58.38580 33.38579630 33 445143.951
## 290 290 46 Male No 72.96350 26.96350040 26 499324.082
## 291 291 20 Female Yes 84.57070 64.57070086 64 807133.761
## 292 292 45 Female Yes 52.27288 7.27287786 7 132234.143
## 293 293 61 Male No 82.24725 21.24725141 21 544801.318
## 294 294 68 Female Yes 83.85334 15.85334027 15 495416.883
## 295 295 77 Female No 66.61089 -10.38911117 -11 -451700.486
## 296 296 36 Female No 73.42818 37.42818060 37 584815.322
## 297 297 48 Male No 97.57235 49.57234977 49 953314.419
## 298 298 45 Female Yes 67.63177 22.63176822 22 411486.695
## 299 299 46 Male Yes 76.42436 30.42436401 30 563414.148
## 300 300 40 Female Yes 73.32863 33.32863299 33 555477.217
##
## $t_test_gender
##
## Welch Two Sample t-test
##
## data: female_le and male_le
## t = -1.6184, df = 282.01, p-value = 0.1067
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.066506 0.396827
## sample estimates:
## mean of x mean of y
## 73.55824 75.39308
##
##
## $t_test_smoke
##
## Welch Two Sample t-test
##
## data: nonsmoker_le and smoker_le
## t = 0.68489, df = 179.48, p-value = 0.2471
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -1.174109 Inf
## sample estimates:
## mean of x mean of y
## 74.66577 73.83548
##
##
## $summary
## # A tibble: 4 × 6
## gender smoker n mean_age mean_life mean_remaining
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 Female No 119 51.0 73.5 22.5
## 2 Female Yes 42 50.4 73.9 23.5
## 3 Male No 88 52.1 76.3 24.2
## 4 Male Yes 51 45.6 73.8 28.2
##
## $premium
## # A tibble: 4 × 3
## gender smoker avg_premium
## <chr> <chr> <dbl>
## 1 Female No 385756.
## 2 Female Yes 378156.
## 3 Male No 408068.
## 4 Male Yes 443939.
# Akses hasil spesifik
cat("\n\n=== HASIL DETAIL ===\n")
##
##
## === HASIL DETAIL ===
cat("Data Test Gender - p-value:", hasil$t_test_gender$p.value, "\n")
## Data Test Gender - p-value: 0.1066952
cat("Data Test Smoking - p-value:", hasil$t_test_smoke$p.value, "\n")
## Data Test Smoking - p-value: 0.2471491
