B. SOAL PRAKTIK 1.a

# Load dataset
data("airquality")

# Hitung statistik deskriptif untuk variabel Ozone
mean_ozone <- mean(airquality$Ozone, na.rm = TRUE)
median_ozone <- median(airquality$Ozone, na.rm = TRUE)
sd_ozone <- sd(airquality$Ozone, na.rm = TRUE)

# Tampilkan hasil
cat("Mean Ozone:", mean_ozone, "\n")
## Mean Ozone: 42.12931
cat("Median Ozone:", median_ozone, "\n")
## Median Ozone: 31.5
cat("Standar Deviasi Ozone:", sd_ozone, "\n")
## Standar Deviasi Ozone: 32.98788

1.b

# Scatter plot Wind vs Temp
plot(airquality$Wind, airquality$Temp,
     main = "Scatter Plot antara Wind dan Temp",
     xlab = "Wind",
     ylab = "Temp",
     col = "maroon",
     pch = 19)

# Load dataset
data("mtcars")

# Hitung frekuensi setiap kategori pada variabel cyl
cyl_count <- table(mtcars$cyl)

# Buat bar chart
barplot(cyl_count,
        main = "Bar Chart untuk Variabel cyl",
        xlab = "Jumlah Silinder (cyl)",
        ylab = "Frekuensi",
        col = "navy")

# Tambahkan label pada grafik
text(x = barplot(cyl_count), y = cyl_count, label = cyl_count, pos = 3)

3.a

# Load dataset
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 = "brown")

3.b

# Hitung korelasi
correlation <- cor(iris$Sepal.Length, iris$Petal.Length)

# Tampilkan hasil
cat("Korelasi antara Sepal.Length dan Petal.Length:", correlation, "\n")
## Korelasi antara Sepal.Length dan Petal.Length: 0.8717538

Nilai korelasi yang positif tinggi menunjukkan adanya hubungan linear positif yang kuat antara variabel Sepal.Length dan Petal.Length. Artinya, ketika panjang sepal (Sepal.Length) meningkat, panjang petal (Petal.Length) juga cenderung ikut meningkat. Korelasi mendekati 1 menunjukkan bahwa hubungan tersebut hampir linear sempurna dan jika nilai korelasi lebih rendah, hubungan tersebut masih ada tetapi lebih lemah.

3.c

# Scatter plot dengan warna berdasarkan Species
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
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") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

4.

# Buat tabel kontingensi
chi_table <- table(mtcars$vs, mtcars$am)

# Uji Chi-Square
chi_result <- chisq.test(chi_table)

# Tampilkan hasil
print(chi_result)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  chi_table
## X-squared = 0.34754, df = 1, p-value = 0.5555

5.a

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

# Ringkasan model
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

5.b

# Scatter plot Temp vs Solar.R dengan garis regresi
plot(airquality$Solar.R, airquality$Temp,
     main = "Scatter Plot Temp vs Solar.R dengan Garis Regresi",
     xlab = "Solar.R",
     ylab = "Temp",
     col = "purple",
     pch = 19)

# Tambahkan garis regresi
abline(model, col = "black", lwd = 2)

5.c Interpretasi hasil model:

Koefisien pada regresi menunjukkan perubahan yang diharapkan pada variabel Temp untuk setiap unit perubahan pada Solar.R. Nilai R kuadrat (dari summary(model)) menunjukkan sejauh mana variabel Solar.R dapat menjelaskan variasi dalam Temp.