Scatter Diagrams (or Plots) are used to visually represent data for further analysis in correlation or regression. The diagram shows a pair of numerical data, one for each axis (horizontal and vertical with are x-y respectively).
They explore associations between two variables and the "x" variable (input) is varied systematically and the "y" (output) is measured once the input changes. The intention is to visually examine trends or patterns of the input (x) onto the response in the experiment.
The chart below would indicate a weak negative linear correlation. From here the Coefficient of Correlation can be determined as well as best fit line to describe the behavior of the data.
There are no assumptions of normality or sequence to use a Scatter Plot. Sequential plotting is required for a Time-Series plot (and for SPC). In other words, the data for Scatter Plots does not have to be in sequence or chronoligical order but this is required for SPC and Time-Series charts.
It is most useful when there is a lot of data in table and very difficult to tell if there is any type, and to what degree, of correlation.
As association of variables does not always imply cause. There could be other lurking variables effecting the measured "y" response.
A scatter plot of the blue dots shown below makes it very obvious that there is strong positive linear correlation but it does not provide any value other than a visual indication.
A Scatter diagram itself is limited in its use but is rather a starting point for further analysis of correlation and regression.
Line charts are similar to Scatter Plots but contain lines connecting the data.
The chart above is an example of the value of the plot. Simply looking at a set of x-y data on a sheet would be a challenge to see this relationship. There appears to be a strong non-linear relationship with a few outliers within the range of 0-200 units.
Six Sigma Certification
Six Sigma Modules
Green Belt Program (1,000+ Slides)
Cause & Effect Matrix
Central Limit Theorem
1-Way Anova Test
Correlation and Regression