Data Analysis of Automobile Carbon Footprint
J Faleiro
May 5 2014
The Data
- This small application demonstrates ad hoc data investigation in features related to the carbon footprint of mid-70's automobiles
- This data was extracted from the 1974 issue of Motor Trend, an US automobile magazine.
- The data comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).
Summary
data(mtcars)
knitr::kable(head(mtcars,5))
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
Data Analysis
- Select features by moving the
Predictorand theOutcomedrop bown boxes - The dashboard will show a linear interpolation between the features selected.
- The shaded area represents the 95% confidence interval of the linear prediction.
- You can additionally select a
Clusteredfeatured in the third drop-down box to add colors to points as a way to help visualize clusters.
Sample Visualization & Data Insights
- The outcome
mpgis negativelly correlated to all regressors - All regressors are positivelly correlated amongst themselves
- This application is available for use here
