Analyzing Fuel Efficiency: Relationships in the mtcars Dataset

 

What patterns or relationships did you observe?

The mtcars dataset revealed clear patterns about fuel efficiency. Weight had the strongest negative correlation with mpg at -0.87, meaning heavier cars consistently get worse gas mileage. Cylinders (-0.85), displacement (-0.85), and horsepower (-0.78) showed similar negative relationships. The regression analysis confirmed these patterns. A simple model using just weight explained 75% of the MPG variation, while adding horsepower and displacement increased this to 83%. Interestingly, displacement became statistically insignificant in the multiple regression model, suggesting these variables overlap in measuring vehicle "bigness."

How did your use of grid layout enhance interpretation?

The 2x2 grid layout made comparing relationships much easier than viewing plots separately. I could immediately see that all four variables showed downward-sloping patterns, confirming negative relationships with mpg. The grid also revealed differences in correlation strength. Weight showed tighter clustering around the regression line compared to cylinders, matching its stronger correlation coefficient. The side-by-side arrangement helped me notice varying slope steepness, with weight having a steeper decline than horsepower, indicating weight changes have bigger impacts on fuel efficiency.

How do Few's recommendations help or hinder your design choices?

Few's emphasis on minimizing clutter improved my visualization. I used simple blue tones, removed unnecessary elements, and kept axis labels clear. The minimal theme let the data stand out rather than decorative elements. Few's principles guided me toward clarity and simplicity. The grid layout itself reflects his philosophy by allowing easy comparison without overwhelming viewers. Each plot is clean with visible regression lines, making the overall message immediately clear: bigger vehicles have worse fuel economy.

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