When should we use Scatter Plots?
Scatter plots are graphical representations used to show the relationship between two variables. They are especially valuable in identifying correlations, trends, and outliers within datasets, making them a critical tool in the data visualization toolkit.
Definition:
In a scatter plot, each dot represents an observation. The position of the dot on the horizontal and vertical axes indicates the values of its corresponding variables.
Applicability:
Scatter plots are particularly useful when you want to investigate the potential relationship or correlation between two variables. They are applicable when both variables are continuous and you suspect they might influence each other.
- → Example 1: An online retailer analyzing the relationship between the time spent on the site and the total purchase amount. Here, a scatter plot can help identify if longer engagement on the site correlates with higher spending.
- → Example 2: A SaaS company looking to understand the relationship between feature usage and customer satisfaction. By plotting feature usage against satisfaction scores, a scatter plot can reveal trends or patterns that might not be evident from the raw data.
Limitations:
While scatter plots are incredibly versatile, they are less effective when dealing with categorical data or when trying to compare more than two variables at once.
They can also become cluttered and less informative with very large datasets or when data points overlap significantly.
Takeaway:
By understanding and utilizing scatter plots correctly, we can gain deeper insights into the complex relationships between various business metrics.