Learn

In this lesson we discussed several ways of examining an association between two quantitative variables. More specifically, we:

- Used scatter plots to examine relationships between quantitative variables
- Used covariance and correlation to quantify the strength of a linear relationship between two quantitative variables

Note that the dataset used in this lesson was downloaded from kaggle.

### Instructions

As a final exercise, a new dataset named `penguins`

has been uploaded for you in **script.py**. This dataset contains various measurements for a sample of penguins. To practice the skills learned in this lesson, here are some things to try:

- Inspect the first few rows of data.
- Create a scatter plot of flipper length (
`flipper_length_mm`

) and body mass (`body_mass_g`

). - Inspect your plot. What is the relationship between these variables?
- Calculate the covariance for these two variables.
- Calculate the correlation for these two variables. Does this number make sense given the plot you created?

Solution code is available to you in **solution.py** if you want to compare your work.

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