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Based on the theory of probability and sampling, a young physicist Dr. Walter Shewhart discovered control charts that provide a powerful tool of identifying and correcting the assignable causes of variation outside the 'stable pattern' of chance causes enabling us to stabilize and control our process at desired performances and thus bring the process under statistical control.

Control chart is a simple pictorial device for detecting unnatural variation in data resulting from repetitive process. That is it provides the criteria for detecting lack of statistical control. Control charts are simple to construct & easy to interpret and tell us at a glance whether the sample point falls with 3- σ control limit or not.

Any sample point going outside the 3- σ control limits is an indication of lack statistical control i.e. presence of some assignable causes of variation which must be traced, identified & eliminated.

A typical control chart consists of the following three horizontal lines:

i. A central line (

ii. Upper Control Limit (

iii. Lower Central Limit (

Together with a number sample points as exhibited in the following diagram.

In the control chart, upper control limit (UCL) and LCL are usually plotted as dotted lines and control line (CL) plotted as a bold line.

If t is the underlying statistic then these values depend on the sampling distribution of t and are given by -

UCL = E (t) + 3SE (t)

LCL = E (t) - 3SE (t)

CL = E (t)

Based on the theory of probability and sampling, a young physicist Dr. Walter Shewhart discovered control charts that provide a powerful tool of identifying and correcting the assignable causes of variation outside the 'stable pattern' of chance causes enabling us to stabilize and control our process at desired performances and thus bring the process under statistical control.

Control chart is a simple pictorial device for detecting unnatural variation in data resulting from repetitive process. That is it provides the criteria for detecting lack of statistical control. Control charts are simple to construct & easy to interpret and tell us at a glance whether the sample point falls with 3- σ control limit or not.

Any sample point going outside the 3- σ control limits is an indication of lack statistical control i.e. presence of some assignable causes of variation which must be traced, identified & eliminated.

A typical control chart consists of the following three horizontal lines:

i. A central line (

**CL**) to indicate the desired standard as level of process.ii. Upper Control Limit (

**UCL**)iii. Lower Central Limit (

**LCL**)Together with a number sample points as exhibited in the following diagram.

In the control chart, upper control limit (UCL) and LCL are usually plotted as dotted lines and control line (CL) plotted as a bold line.

If t is the underlying statistic then these values depend on the sampling distribution of t and are given by -

UCL = E (t) + 3SE (t)

LCL = E (t) - 3SE (t)

CL = E (t)

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