What is a Scatter Diagram in 7 QC Tools?

Scatter Plot Examples

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.

What is a scatter diagram used for?

It is used for validation of two different variable

It is also used for checking the trend with respect to time

The Scatter_diagram is used to confirm a hypothesis testing between two variables.

How to draw a Scatter Diagram?

→ Refer below four steps for Making a Scatter_Graph.

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

Step 1. Data Collection:

Step 2. Choose Independent and Dependent Variables:

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

Step 4. Interpret the Graph

Types of Correlation in Scatter Diagram in 7 QC Tools:

What are the 3 types of scatter plots?

→ There are main three types are mentioned below.

Positive Negative Neutral

Benefits of Scatter Diagram:

Limitation of Scatter Plot:

👉 Also Read:

Cause & Effect Diagram (Fishbone or Ishikawa) 2.(Fishbone or Ishikawa)

→ Scatter Diagram is used to study and identify the possible relationship between two variables.→ It is also used to validate the relationship between cause and effects and it is also known as the validation tool.→ Scatter Chart inis a graph in which the values of two variables are plotted along two axes of the graph, the pattern of the resulting points will say the correlation.→ We use this chart to find out the relation between cause and its effect by using cause and effect diagram.→ This tool is commonly used in thein the→ Refer below the relations of two variables that we can found in our real life.→ Now we are taking one example to understand how to make a chart?→ In this example, we are taking 50 readings of different curing temperatures on different curing times for a product manufactured on the thermosetting press. we want to find out the relation between curing time and curing temperature.→ We have to find is any correlation is present or not between curing temperature and curing time.→ If we have more data sample then it will give a more precise result.→ The dependent variable is usually plotted along the vertical axis i.e. in Y-axis and it is called a measured parameter.→ The independent variable is usually plotted along the horizontal axis i.e. in X-axis and it is called a control parameter.→ In this case, we are taking heating temperature as an independent variable on the x-axis, and curing time is dependent on heating temp. so we mentioned it on the y-axis.→ Now based on recorded data construct a graph and add a suitable title, horizontal axis name, vertical axis name, and make trend line.→ We will interpret the chart based on the trend line.→ There are many different types of correlation found between the Independent and Dependent variables which are mentioned below with pictorial representation.→ Mainly three relations available between two variables we can say that Strong, Moderate, and No Relation.→ A strong positive correlation means it is a clearly visible upward trend from left to right, a strong negative correlation means it is a clearly visible downward trend from left to right.→ eg. In 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.→ For example, in the summer season the temperature increase, icecream sales will also increase.→ 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.→ For example, in the summer season the temperature increase, the sales of winter coats decrease.→ A weak correlation means it is less clear that the relationship is either positive or negative?→ No correlation means neither positive nor negative relation and indicates the independent variable does not affect the dependent variable.→ The subtypes of the scatter_plot are mentioned below and you can refer the pictures for your better understanding.⇢ (1) Strong Positive⇢ (2) Moderate Positive⇢ (3) Weak Positive⇢ (4). Strong Negative⇢ (5). Moderate Negative⇢ (6). Weak Negative⇢ (7). Random Pattern→ It is beneficial to confirm a hypothesis (assumption) between two variables that are related or not.→ Provide both visual and statistical means to test the strength of a potential relationship.→ It is a very good validation tool.→ Used for proving the relation between→ Plotting the diagram is relatively simple.→ It does not show you the quantitative measure of the relationship between the variable.→ This chart does not show you the relationship for more than two variables at a time.