Scatter Diagram | Types | Patterns | Interpretation | Case Study

What is a Scatter Diagram?

→ A Scatter Diagram is used to study and identify the possible relationship between two variables.

→ In simple words, we can say that this tool is used to find out the correlation between two variables.

→ It is also used to validate the relationship between cause and effect and is also known as the validation tool.

→ Scatter Chart is a graphical tool in which the values of two variables are plotted along two axes of the graph, the pattern of the resulting points will show the correlation.

→ We use this chart to find out the relation between cause and effect that we have found during a cause and effect diagram or fishbone diagram.

→ This tool is commonly used in the analyze phase of the Six Sigma Project.

→ A Scatter Diagram is a powerful tool that is majorly used in statistics and data analysis to visualize and show the correlation between two variables.

Table of Contents:

• What is a Scatter Diagram?
• Example of Scatter Diagram
• When to Use a Scatter Diagram?
• Why to Use a Scatter Diagram?
• How to Make a Scatter Diagram?
• Types of Correlation in Scatter Diagram
• Limitation of Scatter Diagram
• Benefits of Scatter Diagram
• Conclusion

Different Names of Scatter Diagram:

⏩The different names of the Scatter Diagram are:

• Scatter Plot
• Scatter Graph
• Scatter Chart
• Scattergram
• Correlation Chart

Example of Scatter Diagram:

⏩Examples of the relations of two variables are:

• Weight and Height of a Man
• Hardness and carbon content in the product
• Visual Inspection mistakes and Illumination levels
• Child’s height and Father’s height
• Curing Temperature and Curing Time
• Advertising and sales

When to Use a Scatter Diagram?

• To find the types of correlation between two variable
• Identify validation between cause and effect
• Provides confirmation for hypothesis testing between two variables
• Identify the possible patterns and trends
• Find out the outliers or abnormalities
• Quality control and process improvement
• This tool is used in different sectors and departments

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Why to Use a Scatter Diagram?

• This diagram provides the crear visual representation
• Find out the possible trends and patterns
• It is very easy to understand
• This tool is very easy to use and communicate
• It provides a graphical relationship between two variables
• This is a fundamental tool for the complex analysis

How to Make a Scatter Diagram?

→ Now we will take an example to understand this concept in detail.

→ We will take one example thermosetting press in the manufacturing process.

→ Now we need to find out the correlation between machine temperature vs curing time in the thermosetting process.

→ There are different steps for performing validation or analysis.

⏩Four Steps to Construct a Scatter Diagram are:

1. Data Collection
2. Choose Independent and Dependent variables.
3. Construct the Graph and add the titles & trend line.
4. Interpret the Graph

→ Now we will learn all the steps with detailed examples.

Step 1. Data Collection:

→ So in the very first steps, we need to collect the data to identify the correlation between two variables.

→ Now we are taking 50 readings of curing temperatures and curing times for a product manufactured on the thermosetting press.

→ So our target is to find out the relationship between curing time and curing temperature.

→ If we have more data samples then it will give a more precise result.

Step 2. Choose Independent and Dependent Variables:

→ The dependent variable is usually plotted along the vertical axis i.e. on the Y-axis.

→ It is also called a measured parameter.

→ The independent variable is usually plotted along the horizontal axis i.e. on the X-axis.

→ It is called a control parameter.

→ In this example, we are taking heating temperature as an independent variable on the x-axis.

→ And the curing time is dependent on the heating temp. so we mentioned it on the y-axis.

Step 3. Construct the Graph and add the titles & trend line:

→ So till now we have collected the data and identified the dependent and independent variables.

→ Now based on the data, we will construct a graph.

→ For this graph, we need to add a suitable title, horizontal axis name, vertical axis name, and make a trend line.

→ Refer to the below picture for a better understanding.

Step 4. Interpret the Graph:

→ Now till now we have successfully constructed the graph.

→ Also, we have added the trendline into the graph so we can analyze and interpret this graph further.

→ So we will interpret the chart based on the trend line.

→ So based on the trend line, there are three correlations available between the two variables.

⏩Three possible correlations are:

• Strong correlation
• Moderate correlation
• No correlation

Types of Correlation in Scatter Diagram:

→ There are many different types of correlation found between the Independent and Dependent variables.

⏩Types of Correlation in Scatter Diagram are:

• Strong Positive
• Moderate Positive
• Weak Positive
• Strong Negative
• Moderate Negative
• Weak Negative
• Random Pattern or No Correlation

→ We will take examples to understand the relation.

⏩Types of correlations between two variables are:

• Positive Correlation
• Negative Correlation
• No Correlation

→ Now we will learn all relations with the help of examples in detail.

(1) Positive Correlation:

→ A positive correlation means it is a clearly visible upward trend from left to right.

→ In a positive relation, as the value of x increases, the value of y will also increase.

→ We can say that the slope of the straight line drawn along the data points will go up and the pattern will resemble the straight line.

⏩A positive correlation is further classified into three categories:

• Strong Positive – It represents a perfectly straight line
• Moderate Positive – All points are nearby
• Weak Positive – All the points are scattered

⏩Examples of Positive Correlation are:

• Temperature increases, and ice cream sales will also increase
• Cold waves increase, and cold clothes sales will also increase
• Advertisement spending increase and sales increase

(2) Negative Correlation:

→ A negative correlation means there is a clearly visible downward trend from left to right.

→ In a negative correlation, as the value of x increases, the value of y will decrease and the slope of a straight line drawn along the data points will go down.

⏩The negative relation is further divided into three types:

• Strong Negative – It forms almost a straight line
• Moderate Negative – When points are near to one another
• Weak Negative – Data points are in scattered distribution

⏩Examples of Negative Correlation are:

• Temperature increases and the sales of winter clothes decrease
• Temperature increases and the curing time will decrease
• Speed increase and mileage decrease

(3) No Correlation:

→ No correlation means neither positive nor negative relation.

→ That indicates the independent variable does not affect the dependent variable.

→ No correlation is also known as random patterns.

⏩Examples of Random Patterns are:

• Age increase and height increase
• R&D spending increases profit increase
• Tuition time increases and exam marks increase

Limitation of Scatter Diagram:

• It shows only correlation not able to identify any causes
• Outliers can impact heavily on relation between variables
• It is limited to validation between two variables
• Only continuous data can be analyzed
• It can not predict any trend or patterns

Benefits of Scatter Diagram:

• Confirm a hypothesis (assumption) between two variables that are related or not
• Provide both visual and statistical outcomes for validation
• It is a very good validation tool
• Used for proving the relation between cause and effect
• Plotting the diagram is relatively simple
• This tool is very easy to understand and explain
• Provides visual representation
• It is very easy to create and interpret
• It provides a base for further detailed analysis

Conclusion:

→ Scatter Diagram is a very powerful tool for visualizing the relationship between two variables.

→ It provides a clear visual representation.

→ We can easily make this diagram and interpret the chart.

→ Since it provides visual representation we can easily interpret the graph.

→ The scatter diagram is an excellent tool for initial data investigation and visualization.