7QC Tools — Histogram of Continuous Data

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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.

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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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Related Blogs

7QC Tools: Flow Chart, Know Your Process Thoroughly

7QC Tools: Fish Bone or Ishikawa Diagram

7QC Tools: How to Extract More Information from the Scatter Plot?

7QC Tools: How to Draw a Scatter Plot?

7QC Tools: Scatter Plot — Caution! Misuse of Statistics!

7QC Tools: Scatter Plot

7QC Tools — How to Prioritize Your Work Using Pareto Chart?

7QC Tools — How to Interpret a Histogram?

7QC Tools — How to Draw a Histogram?

7QC Tools — Histogram of Discrete Data

7QC tools — Check List

Excellent Templates for 7QC tools from ASQ

What are Seven QC Tools & How to Remember them?

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What are Seven QC Tools & How to Remember them?

Understanding and using hard-core statistics for continuous improvement is an issue with the shop-floor people. In order to overcome this issue it was felt necessary to present statistics in graphical forms so that everyone can understand it.

The 7QC tools made the quality control more simpler so that it could be comprehended easily by all. Now statistics is not a prerogative of some experts in the company. It could easily be percolated down the ranks, irrespective whether someone has a statistical background or not.

7QC tools is a collection of statistical tools which need not to be applied in a particular sequence. However, to understand and remember it we need to connect them with each other.

  1. Flow chart
  2. Cause & Effect diagram
  3. Control charts
  4. Check list
  5. Histogram
  6. Pareto Chart
  7. Scatter Plot

One can easily remember the list by using following relationship between the above tools (you can develop some other relationship).

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If you want to remember 7QC tools then remember these sequence of events used in continuous improvement.

For starting any continuous improvement program, the first step is about defining the problem (quality characteristic ‘Y’ to be addressed). Once we define the problem, we need to understand the process in-depth using Process Flow Diagram to find the problem areas and non-value adding steps.

From the process flow diagram, find the probable sources of variations (X)  affecting the desired output (Y) using Cause & Effect Diagram.

Once we have identified the probable cause (X), then start monitoring ‘X’ and ‘Y’ using proper Control Charts. This will drop some of the ‘X’s’ came from the cause and effect diagram. Make note of ‘X’ that really affects the ‘Y’.

Once you have real ‘X’ that can affect ‘Y’ then prepare a plan for data collection using Check List to support the cause and effect relationship.

Data thus collected using check list is then arranged in graphical form using Histogram to have a quantitative pictorial view of the effect of ‘X’.

The bars of the histogram constructed above is then re-arranged in descending order to give Pareto Chart. This arranges the causes (X) in descending order of their effect on ‘Y’. Take the list of ‘X’ (usually top 3) having prominent effect on ‘Y’ for continuous improvement.

Finally show a quantitative relationship between top three ‘X’ and ‘Y’ using Scatter Plot in laboratory or by collecting more data from the plant and propose the improvement strategy by providing best conditions for ‘X’ so that ‘Y’ remains within the desired limits.

Related Blogs

7QC Tools: Flow Chart, Know Your Process Thoroughly

7QC Tools: Fish Bone or Ishikawa Diagram

7QC Tools: How to Extract More Information from the Scatter Plot?

7QC Tools: How to Draw a Scatter Plot?

7QC Tools: Scatter Plot — Caution! Misuse of Statistics!

7QC Tools: Scatter Plot

7QC Tools — How to Prioritize Your Work Using Pareto Chart?

7QC Tools — How to Interpret a Histogram?

7QC Tools — How to Draw a Histogram?

7QC Tools — Histogram of Continuous Data

7QC Tools — Histogram of Discrete Data

7QC tools — Check List

Excellent Templates for 7QC tools from ASQ

Kindly do provide feedback for continuous improvement

Discrete and Continuous Data

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Data that is being handled in statistics are of two types

  1. Quantitative
  2. Qualitative

QUANTITAIVE DATA

These data are either countable or measurable. Countable means that the data can take some predefined values as out come of a dice through (x = 1, 2, 3, 4, 5 and 6) — these type of data are known as DISCRETE DATA.

Measurable data are those whose possible values cannot be counted and can only be described using intervals on the real number line. e.g. height of children in a given school 120 ≤ x ≤ 150. These type of data are known as CONTINUOUS DATA.

Discrete and continuous data can be understood by following example. Suppose you are crossing a shallow river and there are two options available

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  1. Stepping stone bridge:
    • In order to cross the stream, you have to land on platform-1 then on platform-2 ….. finally on platform-5 to cross the river. You can’t land in-between the platforms. Similarly if a random variable can take some exact values it’s called as discrete data and that random variable can’t take any value between two adjacent data point, then that type of data is called as discrete data. They are countable i.e. you can count the possible values of the random variable involved.
    • Number of customers x = 0, 1, 2, …….
    • Number of phone calls x = 0, 1, 2, …….
    • Outcome of a dice throw x = 1, 2, 3, 4, 5 & 6
      • DISCRETE DATA ARE COUNTED
  2. A bridge on the same river:
    • where you can place your feet anywhere on the bridge to cross the river, there is no restriction, I can take as many steps of any size I want. I can take step size of 1 yard or half yard at a time. Similarly if a random variable can take any value between two given points, then that data is called as continuous data. They uncountable as they can take any value in-between two random variables.
    • Average purchase by a customer 100 ≤ x ≤ 200 dollars
    • Duration of the phone call 10 ≤  x ≤ 30 minutes
    • Distance covered by a car in 5 minutes 3≤  x ≤ 6 Km

CONTINUOUS DATA ARE MEASURED

 

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