• This presentation explores the relationship between car weight and fuel efficiency.
• Understanding this relationship is crucial in the field of automotive design and environmental science.
• Our objective is to analyze the mtcars dataset to identify trends and insights.

• The mtcars dataset is a collection of data on 32 car models from a 1974 Motor Trend magazine issue.
• Fuel efficiency and car weight are critical factors that influence a car’s environmental impact and performance.
• Statistical analysis helps in understanding how these variables interact.

• We will employ linear regression to explore the relationship between car weight (wt) and miles per gallon (mpg).
• Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables.
• The formula for a simple linear regression is \(y = \beta_0 + \beta_1x + \epsilon\), where \(y\) is the dependent variable, \(x\) is the independent variable, and \(\beta\) are the coefficients.

• The mtcars dataset includes measurements like miles per gallon (mpg), weight (wt), and horsepower (hp).
• This dataset is widely used for illustrative examples in statistical analysis.
• For our analysis, the key variables are wt (car weight) and mpg (fuel efficiency).

• The scatter plot demonstrates that heavier cars generally exhibit lower fuel efficiency.
• The blue line represents the linear trend, highlighting a negative correlation between weight and miles per gallon.

• This plot differentiates the cars based on the number of cylinders (cyl), adding another dimension to our analysis.

• This interactive Plotly plot allows users to hover over data points for more detailed information.

library(plotly)
p <- ggplot(mtcars, aes(x = wt, y = mpg, text = rownames(mtcars))) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Interactive Plot: Fuel Efficiency vs. Car Weight",
       x = "Weight (1000 lbs)",
       y = "Miles per Gallon")
ggplotly(p)

• “Motor Trend Car Road Tests” data was extracted from the 1974 Motor Trend US magazine.

NOTE: mtcars data is provided by the default R package.

Visit: rdocumentation.org/packages/datasets for more info.