m5

# Bersihkan environment
rm(list = ls())

# Panggil package
library(dplyr)

## Warning: package 'dplyr' was built under R version 4.5.2

## 
## 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(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.5.2

# Import data CSV
data <- read.csv("C:/Users/MyBook Hype AMD/Downloads/bodyfat.csv")
kolom <- data[, 2]
data_BodyFat<- data[, c("Abdomen", "BodyFat")]
head(data_BodyFat)

##   Abdomen BodyFat
## 1    85.2    12.3
## 2    83.0     6.1
## 3    87.9    25.3
## 4    86.4    10.4
## 5   100.0    28.7
## 6    94.4    20.9

tail(data_BodyFat)

##     Abdomen BodyFat
## 247   107.6    30.2
## 248    83.6    11.0
## 249   105.0    33.6
## 250   111.5    29.3
## 251   101.3    26.0
## 252   108.5    31.9

#statistik deskriftif
summary(data_BodyFat)

##     Abdomen          BodyFat     
##  Min.   : 69.40   Min.   : 0.00  
##  1st Qu.: 84.58   1st Qu.:12.47  
##  Median : 90.95   Median :19.20  
##  Mean   : 92.56   Mean   :19.15  
##  3rd Qu.: 99.33   3rd Qu.:25.30  
##  Max.   :148.10   Max.   :47.50

# Standar deviasi 
sd(data_BodyFat$Abdomen)

## [1] 10.78308

sd(data_BodyFat$BodyFat)

## [1] 8.36874

# Uji korelasi Pearson 
hasil_korelasi <- cor.test(data_BodyFat$Abdomen,data_BodyFat$BodyFat, method = "pearson")
# Menampilkan hasil 
print(hasil_korelasi)

## 
##  Pearson's product-moment correlation
## 
## data:  data_BodyFat$Abdomen and data_BodyFat$BodyFat
## t = 22.112, df = 250, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7669520 0.8514218
## sample estimates:
##       cor 
## 0.8134323

# Membuat scatter plot 
plot(data_BodyFat$Abdomen, data_BodyFat$BodyFat, main = "Scatter Plot Abdomen vs BodyFat", xlab = "Abdomen", ylab = "BodyFat", pch = 19, col = "blue") 
# Menambahkan garis regresi 
abline(lm(data_BodyFat$BodyFat ~ data_BodyFat$Abdomen), col = "red", lwd = 2)