Scatter plots are similar to line graphs in that they use horizontal and vertical axes to plot data points. However, they have a very specific purpose. Scatter plots show how much one variable is affected by another. The relationship between two variables is called their correlation.
Select a variable to be plotted along the x-axis.
Select another variable to be plotted along the y-axis.
Correlation is said to be linear if the ratio of change is constant. When the amount of output in a factory is doubled by doubling the number of workers, this is an example of linear correlation.
In other words, when all the points on the scatter diagram tend to lie near a line which looks like a straight line, the correlation is said to be linear.
The closer the data points come when plotted to making a straight line, the higher the correlation between the two variables, or the stronger the relationship
If the data points make a straight line going from the origin out to high x- and y-values, then the variables are said to have a positive correlation.
If the line goes from a high-value on the y-axis down to a high-value on the x-axis, the variables have a negative correlation.
Non Linear (Curvilinear) Correlation
Correlation is said to be non linear if the ratio of change is not constant. In other words, when all the points on the scatter diagram tend to lie near a smooth curve, the correlation is said to be non linear (curvilinear). This is shown in the figure on the right below.