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Scatter plots are very popular for correlation visualizations or when you want to show the distribution, or all possible values, of a large number of data points. Scatter plots are also useful for demonstrating clustering or identifying outliers in the data. Some use cases that illustrate visualizing the distribution of many data points with a scatter plot include:

Comparing countries’ life expectancies to their GDPs (Gross Domestic Product).

Comparing the daily sales of ice cream to the average outside temperature across multiple days.

Comparing the weight to the height of each person in a large group.

Some best practices for scatter plots include:

Label your axes.

Make sure the data set is large enough to provide visualization for clustering or outliers.

Start the value of the y-axis at zero to represent the data accurately. The value of the x-axis will depend on the data. For example, age ranges might be labeled on the x-axis.

Consider adding a trend line if a scatter plot shows a correlation between x- and y-axes.

Do not use more than two trend lines.

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