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.

Do you use scatter diagrams in your projects? If yes, please share your experiences in the comments section.

Our professor ask as to use scatter diagram to see the relation for example the total development cost and the annual produce but the data will plot is only one from each so how can we see the relation. I hope you can help us.

Thanks Fahad

You are welcome Ali.

A scatter diagram can have two independent variables (as per the summary mentioned above) – How that is possible? It should have one independent variable & one dependent variable know?

In some cases, a scatter diagram can have two independent variables.

This helps me a lot

I am a Geography student and those examples and that limitations and benefits helps me a lot, thanks…

You are welcome Rahul.

Good morning.. Plz say in which book you taken this for reference. I need urgent to claim answer for competitive exams

PMP reference books and internet search.

To much helpful artical and in a very esay language tha would b understandable.

Thanks Imran for your visit.

Well done. Clear and concise with everything needed for a beginner to understand the Scatter Diagram.

Thank you Stephen for your comment.

Thanks for this information; very clear and precise.

Thanks Brian for your comment and visit.

Awesome. Thank you Farhad.

You are welcome Khalilullah.

thanks a lot

You are welcome Junaid.

Easy to understand

Thanks Rahul.

Excellent.

A bit off the topic… I’ve been wondering if there are many opportunities as a project manager , for someone who has been in steel fabrication and welding as their base career? Sorry. I hope this makes sense. Thanks

Thank u so much helped a lot

You are welcome Senumi.

NICE SIR IT IS GREAT ….PLZ SHOW ONE EXAMPLE WITH DATA

Hello Randiv, when I will update the post, I will try to add some chart with data.

what is correlation chart

It is another name of scatter diagram.

Nice Artical

Thanks Pramod.

Greetings Farhad,

Nice and helped topic for PM aspirants and I would also like to add up few more lines.

A scattered diagram is a correlation and they may be positive or negative and are represented by a regression line and are generally used when QC finds variable and that might not be in control and systematic and changing in one another variable.

Independent variable is plotted along the horizontal line axis whereas dependent variable is plotted along the vertical axis.

Thanks & Best Regards

https://tiemchart.com/

Thanks Almesh for sharing your thoughts.

could u pls ……post some solved question related to scatter diagram?

Hello Humna, this time I am too busy with other activities, so cannot fulfil your request. I suggest you refer any good PMP exam reference book to find questions on this article.

very informative ….thank u so much

You are welcome Humna.

good work

Thanks Joran.

Why 2 points lie one on top of other?

A scatter diagram consists of hundreds of diagram, some of them may be at the top of others. You have to see the pattern.

The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve. The better the correlation, the tighter the points will hug the line.

Thank you it was usefull for me .

You are welcome Hermenegildo.

Thank you’re the best ?

Thanks Arshley for your comment.

Thanks really good explanation.

May you please explain “Decision tree”?

Thanks again

Sure, soon I will write a blog post on it.

Risk Assessment: Here you assess the current risks if they are still valid or can be closed if outdated.

Risk Audit: It deals with effectiveness of risk responses as well as the effectiveness of the risk management process.

Hi

please could you explain what are diffrents between risk audit and risk assessment in control risk .

Thank you

thank you very much. so usefull

You are welcome Mike.

Thanks Fahad. I have been going through all your posts and they are indeed very helpful.

Please keep helping PMP Aspirants like me with your blogs.

Thanks once again!!!

You are welcome Ritika.

I’ve really enjoyed the explanation you made above. Thank you

You are welcome Bilshan.

I didnt understand the exact diff b/w two types of scatter diagrams: type of correlation ans slope of trend coz both the types are showing the same thing. Pls throw some light on my confusion.

Notice the spread of dots. It shows how closely they are related.

Very nice article sir

You are welcome Pradeep.

Very good article and very clear explanation. Really appreciate the effort.

Thanks Raj.

Asalaam-o-alaikum !

The way you explain every topic is marvelous. Thank you.

Could you please explain Resource optimization techniques and Influence diagram.

Okay.

I have noted it, and soon you will see a post on this topic.

Thanks for the good article.

pls note that there’s one typo, it should be “dependent” variable is plotted on the vertical axis (y-axis).

“Usually independent variable is plotted along the horizontal axis (x-axis) and independent variable is plotted on the vertical axis (y-axis) “

Thanks for your comment Vijay.

Error is corrected.

Good article. Thanks a lot.

You are welcome Bijoy.

thank you.. Really i am understood very well .Mnay thanks for you.

You are welcome Muhsin.

The first variable is independent and the second is dependent in the first variable

Correct.

thank you so much.Super

You are welcome Sundarishanmugam.