library(moments)

Mean, Median, Modus

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

# 1. Mean, Median, Modus
mean_val <- mean(data_ecotrack)
median_val <- median(data_ecotrack)

# Fungsi mencari modus
get_mode <- function(v) {
  uniqv <- unique(v)
  tab <- tabulate(match(v, uniqv))
  uniqv[tab == max(tab)]
}
mode_val <- get_mode(data_ecotrack)

cat("Mean:", mean_val, "\nMedian:", median_val, "\nModus:", mode_val)
## Mean: 24.6 
## Median: 23 
## Modus: 22 24 23

Q1, Q2, IQR

# 2. Q1, Q3, dan IQR
q1 <- quantile(data_ecotrack, 0.25, type = 6) # type = 6 sesuai rumus baku statistik dasar
q3 <- quantile(data_ecotrack, 0.75, type = 6)
iqr_val <- q3 - q1

cat("Q1:", q1, "\nQ3:", q3, "\nIQR:", iqr_val)
## Q1: 21 
## Q3: 26 
## IQR: 5

Varians dan Standar Deviasi

varians_val <- var(data_ecotrack)
sd_val <- sd(data_ecotrack)

cat("Varians:", varians_val, "\nStandar Deviasi:", sd_val)
## Varians: 41.54286 
## Standar Deviasi: 6.445375

PENDAPAT: Data ini tergolong bervariasi tinggi, karena terlihat dari nilai Standar Deviasi = (6.45) yang relatif cukup besar terhadap rentang mayoritas data.

Koefisien Skewness Pearson dan Arah Kemencengannya

skew_val <- skewness(data_ecotrack)
cat("Skewness:", skew_val)
## Skewness: 2.212573