## 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 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:**

- Data Collection
- Choose Independent and Dependent variables.
- Construct the Graph and add the titles & trend line.
- 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.

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