Module 9. Visual Multi Variances Analysis

 


For this visualization, I used the built-in mtcars dataset in R, which contains data on various car models including fuel efficiency, horsepower, weight, and engine characteristics. I chose this dataset because it’s small, clean, and ideal for exploring relationships among several continuous and categorical variables. I have also worked with it before. The scatter plot shows how miles per gallon (MPG) decreases as weight (wt) increases. The points are colored by the number of cylinders, sized by horsepower, and faceted by transmission type (automatic or manual). This multivariate approach reveals that cars with more cylinders and higher horsepower generally weigh more and have lower fuel efficiency, while manual cars tend to achieve slightly higher MPG overall.The multivariate visualization was effective because it allowed multiple comparisons in one clean view. I applied three design principles from this module:

  • Alignment: The axes, labels, and panels are neatly aligned, helping the eye move naturally across both transmission types.

  • Contrast: Color and size are used to clearly distinguish cylinders and horsepower without overwhelming the viewer.

  • Balance: The faceted layout gives equal visual weight to both transmission types, maintaining symmetry and visual harmony.

    Together, these design choices create a balanced and informative chart that makes complex relationships easy to interpret!

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