Having analyzed variables individually in the previous chapter, we now shift focus to understanding how pairs of variables relate to each other. This process, known as bivariate analysis, helps identify potential connections, dependencies, or patterns between two distinct characteristics within your dataset.

In this chapter, you will learn methods to:

• Visualize the relationship between two numerical variables using scatter plots.
• Quantify the strength and direction of linear association between numerical variables using correlation coefficients, such as Pearson's $r$.
• Compare the distribution of a numerical variable across different categories of a categorical variable using comparative plots like grouped box plots or violin plots.
• Analyze the association between two categorical variables using cross-tabulations (contingency tables).
• Visualize the relationship between two categorical variables using stacked or grouped bar charts.

We will use Python libraries like Pandas for calculations and Matplotlib/Seaborn for creating informative visualizations to investigate these pairwise relationships effectively. Understanding these interactions is fundamental for building intuition about the data and guiding subsequent modeling steps.