The Scatter Diagram

The Scatter Diagram is a tool for determining the potential correlation between two different sets of variables, i.e., how one variable changes with the other variable.  This diagram simply plots pairs of corresponding data from two variables, which are usually two variables in a process being studied.  The scatter diagram does not determine the exact relationship between the two variables, but it does indicate whether they are correlated or not.  It, by itself, also does not predict cause and effect relationships between these variables.

The scatter diagram is used to: 1) quickly confirm a hypothesis that two variables are correlated; 2)  provide a graphical representation of the strength of the relationship between two variables; and 3) serve as a follow-up step to a cause-effect analysis to establish whether a change in an identified cause can indeed produce a change in its identified effect.

To make a scatter diagram for two variables requiring confirmation of correlation, the following simple steps are usually followed: 

1) collect 50-100 pairs of data for the two variables and tabulate them;

2) draw the x- and y-axes of the diagram, along with the scales that increase to the right for the x-axis and upward for the y-axis;

3) assign the data for one variable to the x-axis (the independent variable) and the data for the other variable to the y-axis (the independent variable); 

4) plot the data pairs on the scatter diagram, encircling (as many times as necessary) all data points that are repeated.

Interpretation of the resulting scatter diagram is as simple as looking at the pattern formed by the points. If the data points plotted on the scatter diagram are all over the place with no discernible pattern whatsoever, then there is no correlation at all between the two variables of the scatter diagram.  An example of a scatter diagram that shows no correlation is shown in Figure 1.

Figure 1.  A Scatter Diagram showing no correlation

There is positive correlation between two sets of data if an increase in the x-value results in an increase in the y-value. Figure 2a shows a scatter diagram that exhibits positive correlation.  Note that in such a correlation, the data points constitute a perceivable diagonal line that goes from the lower left to the upper right corner.

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See Also:  Matrix Diagram;  Ishikawa Diagram