Scatter Diagram in 7 QC Tools

Scatter Diagram in 7 QC Tools

→ Scatter Diagram is used to study and identify the possible relationship between two variables.
→ It is also used to validate the relation between cause and effects and it is also known as the validation tool.
→ Scatter Chart in 7 QC Tools is 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 the analyze phase in the Six Sigma Methodology.

Examples of the relation between two variables:

→ 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.



Steps for Making a Scatter Graph:

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



Step 1. Data Collection:

→ 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 time 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.

Step 1 Data Collection

Step 2. Choose Independent and Dependent Variables:

→ 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.

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

→ Now based on recorded data construct a graph and add a suitable title, horizontal axis name, vertical axis name, and make trend line.

Construction of Scatter Diagram

Step 4. Interpret the Graph

→ We will interpret the chart based on the trend line.

Types of Correlation in Scatter Diagram in 7 QC Tools:

→ 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 relation is either positive or negative?
→ No correlation means neither positive nor negative relation and indicates the independent variable does not affect the dependent variable.


→ Examples of correlation:

     ⇢ (1) Strong Positive
     ⇢ (2) Moderate Positive
     ⇢ (3) Weak Positive

Positive Relation

     ⇢ (4). Strong Negative
     ⇢ (5). Moderate Negative
     ⇢ (6). Weak Negative
     ⇢ (7). Random Pattern

Random and Negative Relation



Benefits of Scatter Diagram:

→ 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 cause and effect.
→ Plotting the diagram is relatively simple.

Limitation:

→ 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.

👉 Also Read:
      2. Cause & Effect Diagram (Fishbone or Ishikawa)



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