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##1. Dataset airquality
data(airquality)
##A. Menghitung statistik deskriptif
mean_ozone <- mean(airquality$Ozone, na.rm=TRUE)
median_ozone <- median(airquality$Ozone, na.rm=TRUE)
sd_ozone <- sd(airquality$Ozone, na.rm=TRUE)

# Menampilkan hasil
cat("Mean:", mean_ozone, "\nMedian:", median_ozone, "\nStandar Deviasi:", sd_ozone)
## Mean: 42.12931 
## Median: 31.5 
## Standar Deviasi: 32.98788
##B. Scatter plot antara Wind dan Temp
plot(airquality$Wind, airquality$Temp,
     xlab = "Wind", ylab = "Temperature", 
     main = "Scatter Plot antara Wind dan Temp")

## 2. Grafik Batang untuk cyl di Dataset mtcars

data(mtcars)

# Membuat grafik batang dengan label jumlah setiap kategori
barplot(table(mtcars$cyl), 
        main = "Distribusi Variabel cyl", 
        xlab = "Jumlah Silinder", 
        ylab = "Frekuensi",
        col = "skyblue")

## 3. Dataset iris
# a. Membuat Boxplot untuk Petal.Width Berdasarkan Species
data(iris)

# Boxplot Petal.Width berdasarkan Species
boxplot(Petal.Width ~ Species, data=iris, 
        main="Boxplot Petal.Width Berdasarkan Species", 
        xlab="Species", ylab="Petal Width", col="lightgreen")

## b. Menghitung Korelasi antara Sepal.Length dan Petal.Length

# Menghitung korelasi
cor_value <- cor(iris$Sepal.Length, iris$Petal.Length)
cat("Korelasi antara Sepal.Length dan Petal.Length:", cor_value)
## Korelasi antara Sepal.Length dan Petal.Length: 0.8717538
## c. Scatter Plot Sepal.Length dan Sepal.Width Berdasarkan Warna Spesies

library(ggplot2)

# Membuat scatter plot dengan warna berbeda
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
  geom_point() +
  geom_smooth(method="lm", se=FALSE) +
  labs(title="Scatter Plot Sepal.Length vs Sepal.Width", 
       x="Sepal Length", y="Sepal Width")
## `geom_smooth()` using formula = 'y ~ x'

## 4. Uji Chi-Square pada Dataset mtcars
data(mtcars)
# Mengubah vs dan am menjadi faktor
mtcars$vs <- as.factor(mtcars$vs)
mtcars$am <- as.factor(mtcars$am)
# Membuat tabel kontingensi
tab <- table(mtcars$vs, mtcars$am)
# Uji Chi-Square
chi_result <- chisq.test(tab)

# Menampilkan hasil
print(chi_result)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  tab
## X-squared = 0.34754, df = 1, p-value = 0.5555
## 5. Regresi Linear Sederhana dengan Dataset airquality

#a. Ringkasan Model Regresi Linear
# Model regresi linear: Temp ~ Solar.R
model <- lm(Temp ~ Solar.R, data=airquality)
summary(model)
## 
## Call:
## lm(formula = Temp ~ Solar.R, data = airquality)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.3787  -4.9572   0.8932   5.9111  18.4013 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 72.863012   1.693951  43.014  < 2e-16 ***
## Solar.R      0.028255   0.008205   3.444 0.000752 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.898 on 144 degrees of freedom
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.07609,    Adjusted R-squared:  0.06967 
## F-statistic: 11.86 on 1 and 144 DF,  p-value: 0.0007518
## b. Scatter Plot dengan Garis Regresi
# Pastikan dataset 'airquality' telah tersedia
data(airquality)

# Hapus nilai NA dalam dataset agar tidak error
airquality_clean <- na.omit(airquality)

# Buat model regresi linear
model<- lm(Temp ~ Solar.R, data = airquality_clean)

# Buat scatter plot antara Solar.R dan Temp
plot(airquality_clean$Solar.R, airquality_clean$Temp,
     xlab = "Solar Radiation", 
     ylab = "Temperature",
     main = "Regresi Linear: Temp ~ Solar.R")

##c. Interpretasi Hasil

#Koefisien regresi dan nilai R² dapat dilihat dari output summary(model). #Koefisien menunjukkan pengaruh Solar.R terhadap Temp. #Nilai R² menunjukkan seberapa baik model menjelaskan variasi data.