A scatter diagram is one of the seven basic tools of quality, but many professionals find it to be a difficult concept.
Other charts use lines or bars to show data, while a scatter diagram uses dots. This may be confusing, but it is often easier to understand than lines and bars.
In this blog post, I will explain the scatter diagram.
A scatter plot, scatter graph, and correlation chart are other names for a scatter diagram.
We draw this graph with two variables. The first variable is independent and the second variable depends on the first.
This diagram is used to find the correlation between these two variables, how they are related. After determining the correlation, you can then predict the behavior of the dependent variable based on the measure of the independent variable.
A scatter chart is useful when one variable is measurable and the other is not.
According to the PMBOK Guide 6th edition, a scatter diagram is, “a graph that shows the relationship between two variables. Scatter diagrams can show a relationship between any element of a process, environment, or activity on one axis and a quality defect on the other axis.”
You are analyzing accident patterns on a highway. You select the two variables, motor speed and the number of accidents, and draw the diagram.
Once the diagram is complete, you notice that as the speed of vehicles increases, the number of accidents goes up. This shows the relationship between the two.
Since this diagram shows you the correlation between the variables, many experts call it a correlation chart.
In most cases, the independent variable is plotted along the horizontal axis (x-axis) and the dependent variable is plotted on the vertical axis (y-axis). The independent variable is the control parameter because it influences the behavior of the dependent variable.
It is not necessary to have a controlling parameter to draw a scatter diagram. It can have two independent variables. In that case, you can use any axis for any variable.
I have seen many professionals think that a scatter diagram is like a fishbone diagram because the fatter has two parameters: cause and effect.
Please note that these two diagrams are different. The fishbone diagram shows you the effect of a cause, but it does not show the relationship between these two. The scatter diagram helps you analyze the relationship between the two variables.
However, the Ishikawa diagram can help you draw the scatter diagram; for example, you can find the two variables (cause and effect), and then draw the scatter diagram to analyze the relationship between them.
Types of Scatter Diagram
You can classify scatter diagrams in many ways; I will discuss the two most popular based on correlation and slope of the trend. They cover almost all types of scatter diagrams used in project management.
According to the correlation, you can divide scatter diagrams into the following categories:
- Scatter Diagram with No Correlation
- Scatter Diagram with Moderate Correlation
- Scatter Diagram with Strong Correlation
Scatter Diagram with No Correlation
This diagram is also known as “Scatter Diagram with Zero Degree of Correlation”.
Here, the data point spread is so random that you cannot draw a line through them.
Therefore, you can say that these variables have no correlation.
Scatter Diagram with Moderate Correlation
This diagram is also known as “Scatter Diagram with a Low Degree of Correlation”.
Here, the data points are a little closer and you can see that some kind of relationship exists between these variables.
Scatter Diagram with Strong Correlation
This diagram is also known as “Scatter Diagram with a High Degree of Correlation”.
In this diagram, data points are close to each other and you can draw a line by following their pattern.
In this case, you say that these variables are closely related.
As discussed earlier, you can categorize the scatter diagram according to the slope, or trend, of the data points:
- Scatter Diagram with Strong Positive Correlation
- Scatter Diagram with Weak Positive Correlation
- Scatter Diagram with Strong Negative Correlation
- Scatter Diagram with Weak Negative Correlation
- Scatter Diagram with Weakest (or no) Correlation
A strong positive correlation means a visible upward trend from left to right; a strong negative correlation means a visible downward trend from left to right. A weak correlation means the trend is less clear. A flat line, from left to right, is the weakest correlation, as it is neither positive nor negative. A scatter diagram with no correlation shows that the independent variable does not affect the dependent variable.
Scatter Diagram with Strong Positive Correlation
This diagram is also known as a Scatter Diagram with Positive Slant.
In a positive slant, the correlation is positive, i.e. as the value of X increases, the value of Y will increase. You can say that the slope of a straight line drawn along the data points will go up. The pattern resembles a straight line.
For example, if the weather gets hotter, cold drink sales will go up.
Scatter Diagram with Weak Positive Correlation
As the value of X increases, the value of Y also increases, but the pattern does not resemble a straight line.
Scatter Diagram with Strong Negative Correlation
This diagram is also known as a Scatter Diagram with a Negative Slant.
In the negative slant, the correlation is negative, i.e. as the value of X increases, the value of Y will decrease. The slope of a straight line drawn along the data points will go down.
For example, if the temperature goes up, sales of winter coats go down.
Scatter Diagram with Weak Negative Correlation
As the value of X increases, the value of Y will decrease, but the pattern is not clear.
Scatter Diagram with No Correlation
There isn’t any relationship between the two variables to be seen. It might just be a series of points with no visible trend, or it might be a straight, flat row of points. In either case, the independent variable has no effect on the second variable; it is not dependent.
Limitations of a Scatter Diagram
The following are a few limitations of a scatter diagram:
- Scatter diagrams cannot give you the exact extent of correlation.
- A scatter diagram does not show you the quantitative measurement of the relationship between the variables. It only shows the quantitative expression of quantitative change.
- This chart does not show you the relationship for more than two variables.
Benefits of a Scatter Diagram
The following are a few advantages of a scatter diagram:
- It shows the relationship between two variables.
- It is the best method to show you a non-linear pattern.
- The range of data flow, i.e. maximum and minimum value, can be determined.
- Observation and reading are straightforward.
- Plotting the diagram is easy.
Scatter diagrams are useful to determine the relationship between two variables. This relationship can be between two causes, or a cause and an effect, etc. It can be positive, negative or no relationship at all. The first variable is independent, and the second variable depends on the first. To analyze the pattern of the relationship, you change the independent variable and monitor the changes in the dependent variable. A scatter diagram can have two independent variables.
Do you use scatter diagrams in your projects? If yes, please share your experiences in the comments section.
Further reading: What is a Scatter Diagram?