A scatter plot, scatter plot, bubble chart or ball chart is a type of mathematical diagram that uses Cartesian coordinates to show the values ​​of two variables for a set of data.[1]​.

It is used when one or several variables are under the control of the experimenter. If there is a parameter that is systematically increased or decreased by the experimenter, it is called control parameter or independent variable and is usually represented along the horizontal axis (x-axis). The measured or dependent variable is usually represented along the vertical axis (ordinate axis). If there is no dependent variable, any variable can be plotted on each axis and the scatterplot will show the degree of correlation (not causality "Causation (statistical)")) between the variables.

A scatterplot can suggest various types of correlations between variables with a given confidence interval. The correlation can be positive (increase), negative (decrease), or null (the variables are not correlated). A fit line (also called a "trend line") can be drawn in order to study the correlation between variables. An equation for the correlation between variables can be determined by fitting procedures. For a linear correlation, the fitting procedure is known as linear regression and guarantees a correct solution in a finite time.

One of the most powerful aspects of a scatter plot, however, is its ability to show nonlinear relationships between variables. Furthermore, if the data are represented by a mixture model of simple relationships, these relationships are visually evident as overlapping patterns.

Scatter diagram is one of the basic quality control tools, which also include histogram, Pareto chart, check sheet, control charts, Ishikawa diagram and flow chart.