When is it appropriate to use a scatter plot?

A scatter plot is specifically designed to illustrate the relationship between two continuous variables, making it extremely useful in understanding how one variable may affect or correlate with another. By plotting individual data points on a two-dimensional grid, where each axis represents one of the two variables, a scatter plot allows for visual identification of trends, correlations, or patterns that may not be immediately apparent from the raw data alone.

For instance, if you wanted to see how temperature affects ice cream sales, you could plot temperature on one axis and ice cream sales on the other. This visual representation can highlight whether higher temperatures correspond with increased sales, revealing a potential positive correlation.

The other options refer to scenarios that require different types of data visualization. For example, comparing the composition of a dataset typically uses pie charts or bar charts rather than scatter plots, as these visualizations more effectively convey proportions or categories. Analyzing categorical data often calls for different types of visualizations, such as bar charts or histograms, which better represent frequency distributions. Illustrating changes over time is best done with line graphs, as these show trends across a temporal dimension more effectively than scatter plots.