Korelasi

# Membuat data contoh 
# 1. MEMUAT PACKAGE DAN DATA
library(ggplot2)
library(lmtest)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(car)

## Loading required package: carData

library(csv)

df <- read.csv("C:/Users/LENOVO/Downloads/economic_dataset.csv")
data <- df[c("Inflation_Rate","Economic_Growth")]
head(data)

##   Inflation_Rate Economic_Growth
## 1           3.37            2.25
## 2           6.50           -1.61
## 3           5.42           -0.36
## 4           2.74            2.80
## 5           4.13            1.19
## 6           7.48            0.77

tail(data)

##     Inflation_Rate Economic_Growth
## 145           5.83            2.17
## 146           4.68            0.89
## 147           5.68           -1.89
## 148           7.32           -1.04
## 149           4.64            2.91
## 150           5.21           -0.27

Statistik deskriptif sederhana

# Statistik deskriptif sederhana 
summary(data) 

##  Inflation_Rate  Economic_Growth  
##  Min.   :0.800   Min.   :-3.5800  
##  1st Qu.:3.877   1st Qu.:-0.3000  
##  Median :5.060   Median : 0.8800  
##  Mean   :5.086   Mean   : 0.6533  
##  3rd Qu.:6.310   3rd Qu.: 1.6625  
##  Max.   :8.900   Max.   : 4.1500

Standar deviasi

sd(df$Inflation_Rate)

## [1] 1.64148

sd(df$Economic_Growth)

## [1] 1.479998

Uji korelasi Pearson

hasil_korelasi <- cor.test(df$Inflation_Rate, df$Economic_Growth, method = "pearson")

# Menampilkan hasil 
print(hasil_korelasi) 

## 
##  Pearson's product-moment correlation
## 
## data:  df$Inflation_Rate and df$Economic_Growth
## t = -8.8887, df = 148, p-value = 1.96e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6854107 -0.4745589
## sample estimates:
##        cor 
## -0.5899524

interpretasi dari uji korelasinya

berdasarkan uji korelasi person di peroleh p-value sebesar 1.96e-15 dimana < 0.05, sehingga H0 ditolak, maka terdapat hubungan yang signifikan antara Inflation_Rate dan Economic_Growth

Membuat scatter plot

plot(df$Inflation_Rate, df$Economic_Growth, 
     main = "Scatter Plot Inflation_Rate vs Economic_Growth", 
     xlab = "Inflation_Rate", 
     ylab = "Economic_Growth", 
     pch = 19, 
     col = "blue")

## Menambahkan garis regresi 
abline(lm(df$Economic_Growth ~ df$Inflation_Rate), col = "red", lwd = 2)

# Hubungan Korelasi

ggplot(data, aes(x =Inflation_Rate, y = Economic_Growth)) + 
geom_point(size = 3) + 
geom_smooth(method = "lm", se = TRUE) + 
labs(title = "Hubungan Marketing Spend dan Profit", 
x = "Inflation_Rate", 
y = "Economic_Growth") + 
theme_minimal()

## `geom_smooth()` using formula = 'y ~ x'

# Interpretasinya