Ecotrack

Data EcoTrack

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

data

##  [1] 22 25 19 30 24 21 45 23 20 26 24 22 18 27 23

1. Mean, Median, dan Modus

mean(data)

## [1] 24.6

median(data)

## [1] 23

modus <- function(x) {
  nilai <- unique(x)
  frekuensi <- tabulate(match(x, nilai))
  nilai[frekuensi == max(frekuensi)]
}

modus(data)

## [1] 22 24 23

2. Q1, Q3, dan IQR

Q1 <- quantile(data, 0.25)
Q3 <- quantile(data, 0.75)
IQR_data <- IQR(data)

Q1

##  25% 
## 21.5

Q3

##  75% 
## 25.5

IQR_data

## [1] 4

3. Varians dan Standar Deviasi

var(data)

## [1] 41.54286

sd(data)

## [1] 6.445375

4. Skewness Pearson

mean_data <- mean(data)
median_data <- median(data)
sd_data <- sd(data)

skewness_pearson <- 3 * (mean_data - median_data) / sd_data

5. Kesimpulan

cat("Mean =", mean(data), "\n")

## Mean = 24.6

cat("Median =", median(data), "\n")

## Median = 23

cat("Modus =", modus(data), "\n")

## Modus = 22 24 23

cat("Q1 =", Q1, "\n")

## Q1 = 21.5

cat("Q3 =", Q3, "\n")

## Q3 = 25.5

cat("IQR =", IQR_data, "\n")

## IQR = 4

cat("Varians =", var(data), "\n")

## Varians = 41.54286

cat("Standar Deviasi =", sd(data), "\n")

## Standar Deviasi = 6.445375

cat("Skewness Pearson =", skewness_pearson, "\n")

## Skewness Pearson = 0.7447201

sd(data)

## [1] 6.445375