data(mtcars) mean(mtcars\(mpg) median(mtcars\)mpg) sd(mtcars$mpg)

#membuat boxplot
boxplot(mpg ~ cyl, data = mtcars, main = "boxplot MPG Berdasarkan cyl",
        xlab = "Cylinders", ylab = "Miles per gallon")

#histogram untuk variabel HP dan tambahan garis densitas
hist(mtcars$hp, breaks = 10, probability = TRUE,
     main = "hitogram horsepower dengan densitas",
     xlab = "horsepower")
lines(density(mtcars$hp), col = "blue" , lwd = 2)

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

#uji t-test
t_test_result <- t.test(Petal.Length ~ Species,
                        data = subset(iris, Species %in% c("setosa","versicolor")))
t_test_result

## 
##  Welch Two Sample t-test
## 
## data:  Petal.Length by Species
## t = -39.493, df = 62.14, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group setosa and group versicolor is not equal to 0
## 95 percent confidence interval:
##  -2.939618 -2.656382
## sample estimates:
##     mean in group setosa mean in group versicolor 
##                    1.462                    4.260

#model regresi linear sederhana
#a tampilkan ringkasan model menggunakan summary()
model <- lm(mpg ~ wt, data = mtcars)
summary(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

#b buat scatter plot dengan garis regresi
plot(mtcars$wt, mtcars$mpg,
     main = "Scatter Plot MPG vs WT dengan Garis Regresi",
     xlab = "Berat Mobil (wt",
     ylab = "MIles per Gallon (mpg",
     pch = 16)
abline(model, col + "red", lwd = 2)

#c interpretasi hasil
# koefisien regresi (slope) menunjukan perubahan rata-rata mpg untuk setiap peningkatan 1 unit wt
#Nilai R aksen menunjukan seberapa baik model menjelaskan variasi data. jika R aksen mendekati 1, model sangat baik