tugas 4

No.1

# Input data
data <- c(22, 25, 19, 30, 24, 21, 45, 23, 20, 26, 24, 22, 18, 27, 23)

# Mean
mean_val <- mean(data)

# Median
median_val <- median(data)

# Modus
get_mode <- function(v) {
   uniqv <- unique(v)
   freq <- tabulate(match(v, uniqv))
   uniqv[freq == max(freq)]
}
mode_val <- get_mode(data)

cat("Mean:", mean_val, "\nMedian:", median_val, "\nModus:", mode_val)

## Mean: 24.6 
## Median: 23 
## Modus: 22 24 23

No.2

q1 <- quantile(data, 0.25, type = 6)
q3 <- quantile(data, 0.75, type = 6)
iqr_val <- q3 - q1

cat("Q1:", q1, "\nQ3:", q3, "\nIQR:", iqr_val)

## Q1: 21 
## Q3: 26 
## IQR: 5

No.3

var_val <- var(data)
sd_val <- sd(data)

cat("Varians:", var_val, "\nStandar Deviasi:", sd_val)

## Varians: 41.54286 
## Standar Deviasi: 6.445375

No.4

library(e1071)

skew_val <- skewness(data)
cat("Skewness:", skew_val)

## Skewness: 1.995046