 A scatter diagram is one of the seven basic tools of quality that many professionals struggle with. that many professionals struggle with.

Other charts use lines or bars to show data; a scatter diagram uses dots. This may be confusing, but it is often easier to understand.

In this blog post, I will explain the scatter diagram.

## Scatter Diagram

A scatter diagram is also called a scatter plot, scatter graph, or correlation chart.

We draw a scatter diagram with two variables. The first variable is independent and the second variable depends on the first.

The scatter diagram is the simplest way to study the correlation between these variables. After determining how they are related, you can predict the behavior of the dependent variable based on the independent variable.

A scatter chart is useful when one variable is measurable, and the other is not.

According to the PMBOK Guide, 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.”

### Example of Scatter Diagram

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 it is complete, you notice that as the speed of vehicles increases, the number of accidents goes up. This shows the correlation between the two.

In most cases, the independent variable is plotted along the horizontal (x-axis), and the dependent variable is plotted on the vertical (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. There can also be two independent variables. In that case, you can use any axis for any variable.

I know that 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 does not show the relationship. The scatter diagram helps you analyze the correlation between the two variables.

However, the fishbone or Ishikawa diagram can help you draw a scatter diagram; for example, you can find the two variables (cause and effect) and then use the scatter diagram to analyze their relationship.

### 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. These are the most common 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

• Scatter diagrams cannot give you the exact extent of correlation.
• A scatter diagram does not show a 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

• It shows the relationship between two variables.
• It is the best method to show you a non-linear pattern.
• The range of data flow, like the maximum and minimum value, can be determined.
• Patterns are easy to observe.
• Plotting the diagram is simple.

### Summary

Scatter diagrams are useful in determining the relationship between two variables. This relationship can be between two causes, or a cause and an effect. It can be positive, negative, or have no correlation 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 one. A scatter diagram can have two independent variables.

A scatter diagram is an important concept from a PMP exam point of view. Please understand it well.