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jawaban soal praktik 1A

data(mtcars)
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

# Menghitung statistik deskriptif
mean_mpg <- mean(mtcars$mpg)
median_mpg <- median(mtcars$mpg)
sd_mpg <- sd(mtcars$mpg)

# Menampilkan hasil
cat("Mean MPG:", mean_mpg, "\n")

## Mean MPG: 20.09062

cat("Median MPG:", median_mpg, "\n")

## Median MPG: 19.2

cat("Standar Deviasi MPG:", sd_mpg, "\n")

## Standar Deviasi MPG: 6.026948

jawaban soal praktik 1B

# Membuat boxplot
boxplot(mpg ~ cyl, data = mtcars,
        main = "Boxplot MPG Berdasarkan Cylinders",
        xlab = "Jumlah Cylinders",
        ylab = "Miles per Gallon (MPG)",
        col = c("lightblue", "lightgreen", "pink"))

jawaban soal praktik 2

# Membuat histogram
hist(mtcars$hp, probability = TRUE, col = "lightblue",
     main = "Histogram HP dengan Garis Densitas",
     xlab = "Horsepower (HP)")
# Menambahkan garis densitas
lines(density(mtcars$hp), col = "red", lwd = 2)

jawaban soal praktik 3

# Memuat dataset iris
data(iris)

# Mengecek 5 baris pertama
head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

# Uji ANOVA
anova_result <- aov(Sepal.Length ~ Species, data = iris)

# Ringkasan hasil
summary(anova_result)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## Species       2  63.21  31.606   119.3 <2e-16 ***
## Residuals   147  38.96   0.265                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

jawaban soal praktik 4

# Membandingkan panjang petal antara setosa dan versicolor
setosa <- subset(iris, Species == "setosa")$Petal.Length
versicolor <- subset(iris, Species == "versicolor")$Petal.Length

# Melakukan uji t-test
t_test_result <- t.test(setosa, versicolor, var.equal = TRUE)

# Menampilkan hasil
t_test_result

## 
##  Two Sample t-test
## 
## data:  setosa and versicolor
## t = -39.493, df = 98, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.938597 -2.657403
## sample estimates:
## mean of x mean of y 
##     1.462     4.260

jawaban soal praktik 5A

# Memuat dataset mtcars
data(mtcars)

# Membuat model regresi
lm_model <- lm(mpg ~ wt, data = mtcars)

# Menampilkan ringkasan model
summary(lm_model)

## 
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5432 -2.3647 -0.1252  1.4096  6.8727 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
## wt           -5.3445     0.5591  -9.559 1.29e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared:  0.7528, Adjusted R-squared:  0.7446 
## F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10

jawaban soal praktik 5B

# Scatter plot dengan garis regresi
plot(mtcars$wt, mtcars$mpg,
     main = "Scatter Plot MPG vs Weight",
     xlab = "Weight (wt)",
     ylab = "Miles per Gallon (MPG)",
     pch = 19, col = "blue")
abline(lm_model, col = "red", lwd = 2)

jawaban soal praktik 5C Koefisien Regresi

Intercept (Konstanta): 37.2851

Ini berarti ketika berat kendaraan adalah 0, perkiraan MPG adalah 37.2851 Namun, interpretasi ini bersifat hipotetikal karena tidak ada kendaraan dengan berat 0

Koefisien Weight (wt): -5.3445

Menunjukkan hubungan negatif antara berat kendaraan dan MPG Setiap peningkatan 1 unit berat kendaraan, akan menurunkan MPG sebesar 5.3445 Artinya, semakin berat kendaraan, semakin rendah jarak tempuh per galon bahan bakarnya