• Objective: This project analyzes the already built in mtcars dataset in R to explore how the vehicle features relate to fuel efficiency.

• The Data: Sourced from base R, mtcars contains data on 32 cars from the 1974 Motor Trend US magazine.

• Key Variables of Interest:

  • mpg: Miles per gallon
  • wt: Weight (1000 lbs)
  • hp: Gross horsepower
  • cyl: # cylinders
  • am: Transmission type (Automatic or Manual)

Here’s how I prepared and cleaned my data:

# converting variables to factors for cleaner plotting
data("mtcars")
mtcars <- mtcars %>%
  mutate(
    cyl = as.factor(cyl),
    am = factor(am, levels = c(0, 1), labels = c("Automatic", "Manual"))
  )

# quick preview of the dataset
head(mtcars[, c("mpg", "cyl", "wt", "hp", "am")], 3)

##                mpg cyl    wt  hp     am
## Mazda RX4     21.0   6 2.620 110 Manual
## Mazda RX4 Wag 21.0   6 2.875 110 Manual
## Datsun 710    22.8   4 2.320  93 Manual

The dataset is mostly composed of 8 cylinder vehicles, then 4 cylinder and 6 cylinder options.

Manual transmissions in the dataset generally show a higher median fuel efficiency compared to automatics.

clear negative trend in this graph: as vehicle weight increases, miles per gallon (mpg) tends to decrease.

The 3D graph shows that cars with both high weight and high horsepower tend to have the lowest fuel efficiency.

## 
## Call:
## lm(formula = mpg ~ wt + hp, data = mtcars)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.941 -1.600 -0.182  1.050  5.854 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 37.22727    1.59879  23.285  < 2e-16 ***
## wt          -3.87783    0.63273  -6.129 1.12e-06 ***
## hp          -0.03177    0.00903  -3.519  0.00145 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.593 on 29 degrees of freedom
## Multiple R-squared:  0.8268, Adjusted R-squared:  0.8148 
## F-statistic: 69.21 on 2 and 29 DF,  p-value: 9.109e-12

The regression output suggests that vehicle weight has a strong negative relationship with fuel efficiency. Horsepower also has a negative association, although weight appears to be the stronger predictor in this model.

# Descriptive statistics for the main numerical variables
summary(mtcars[, c("mpg", "wt", "hp")])

##       mpg              wt              hp       
##  Min.   :10.40   Min.   :1.513   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:2.581   1st Qu.: 96.5  
##  Median :19.20   Median :3.325   Median :123.0  
##  Mean   :20.09   Mean   :3.217   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:3.610   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :5.424   Max.   :335.0

These summary statistics show the spread of the main variables in the dataset. For example, MPG ranges from 10.4 to 33.9, showing a wide difference in fuel efficiency across the cars.

• The visualizations show a clear negative relationship between vehicle weight and miles per gallon
• Manual cars generally have higher fuel efficiency than automatic cars in this dataset I’m using
• The 3D plot suggests that cars with both higher weight and higher horsepower tend to have the lowest mpg
• The linear regression results support this pattern, with both wt and hp showing negative coefficients
• Weight appears to be the stronger predictor of fuel efficiency in the regression model
• The model explains a large portion of the variation in MPG, with an R squared of approx 0.83

• Heavier cars tend to have lower fuel efficiency.
• Manual cars generally show better MPG than automatics in this dataset.
• Higher horsepower often appears alongside lower MPG, especially in heavier vehicles.
• Overall, weight appears to be one of the strongest predictors of miles per gallon in the mtcars dataset.