The Fifth Principle of SPC states that it is possible to determine the shape of the distribution form the measurements of any process. We can learn abut what the process is doing, against what we want the process to do. For this we need to measure the output of the process with the design specifications.the process can be altered if we donot like the comparison, especially if we see a variation.
We need to address eh variation so that it falls in the required pattern. The variation is due to mainly of 2 types. Common Cause variation and Special Cause Variation.
If the variation in output is caused only by common causes, the output will vary in a normal and predictable manner. In such cases, the process is said to be “stable” or “in a state of Statistical Control”. While the individual measurements may differ from each other, they tend to follow a Normal Distribution.
The normal distribution is characterized by the following
We need to address eh variation so that it falls in the required pattern. The variation is due to mainly of 2 types. Common Cause variation and Special Cause Variation.
If the variation in output is caused only by common causes, the output will vary in a normal and predictable manner. In such cases, the process is said to be “stable” or “in a state of Statistical Control”. While the individual measurements may differ from each other, they tend to follow a Normal Distribution.
The normal distribution is characterized by the following
- Location (Typical Value)
- Spread – Amount by which the smaller values differ from the center.
The shape of the distribution will deviate from the normal curve in case of any un usual occourances. These changes can be called as Assignable causes.
The presence of assignable causes will result in difference from the usual normal curve, either in Shape, or in spread or a combination of both.
 |
| Non Normal |
some changes are given below.
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| Normal |
Non Normal
The above findings will lead us to the sixth principle of SPC – Variation due to assignable causes tend to distort the normal distribution curve.
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