Let’s consider the percentage yield of a process, which ranges from 74 to 94. This is a case of **continuous data**.

As we did for **discrete data**, we have constructed the histogram of the yield data by dividing the yield into some sub-classes followed by putting the data into the class it belongs.

Note:

Unlike discrete data, the bars of the present histogram are touching each other as this is a case of continuous data.

Above histogram tells us that the maximum data is clustered within 78-84. Looking at the graph, it appears that the batches with yield > 88 are outliers! but it’s true. What we should do to improve the process?

What we can do it to compare the process with yield range of 74-82 with the process having yield range of 88-94 and find out the difference.

*Hence, histogram gave a direction for continuous improvement.*

Let’s go a step ahead and plot the customer’s specification (LSL & USL) along with the histogram as shown below. This gives you the idea about process capability and outliers.

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__7QC Tools — How to Prioritize Your Work Using Pareto Chart?__

__7QC Tools — How to Interpret a Histogram?__

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__7QC Tools — Histogram of Discrete Data__

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