Control Chart Process: Detecting Shifts with 3 Sigma Limits and Mean Changes

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In summary, the process is considered out of control if a point plots outside the 3-sigma limit or 2 consecutive points plot between the 2-sigma and 3-sigma limits on the same side. The question is asking for the probability of detecting a shift in the process mean (from 200 to 209) on the first or second sample following the shift, using a control chart with 3-sigma limits.
  • #1
axnman
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Process is Out of control if 1 point plots outside 3-sigma limits or 2 consecutive points between 2-sigma & 3-sigma on same side...

if mean changes to mean1=mean0 + 9...what is probability to detect shift on 1st or 2nd sample following shift?

Point outside 3 sigma is ok but what @ the 2 consecutive points between...please help!
 
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  • #2
This is absolutely opaque. I think you are dealing with a statistics situation but it's not clear from what you have said. Please try to clarify exactly what your problem says.
 
  • #3
Ya statistics situation...sorry @ that,...

basically want to calculate the probability of detecting the shift on the first or the second sample when mean (say 200) shifts to a new value = mean + 9 (say 209)...(an out control situation is given as : if 1 point plots outside 3-sigma limits or 2 consecutive points between 2-sigma & 3-sigma on same side)
 
  • #4
here is the actual question :

Samples of n=4 items are taken from a process at regular intervals. Suppose process mean is 200 and stnd. dev. =10

Assume the following rule is applied to control chart with 3-sigma limits:

The process is declared out of control when either a point plots outside of the 3-sigma limit OR 2 consecutive points plot between the 2-sigma & 3-sigma limits on the same side of the chart. If the process mean shifts to 209, what is the probability that the shift will be detected on the first or the second sample following the shift?
 

What is a Control Chart?

A Control Chart is a statistical tool used to monitor a process and track its performance over time. It is a graph that shows how a process or system changes over time and helps identify any variation or patterns that may occur.

What are the benefits of using a Control Chart?

Using a Control Chart allows for early detection of any changes or variations in a process, which can help prevent defects or errors. It also provides a visual representation of the data, making it easier to understand and interpret. Additionally, Control Charts help identify areas for improvement and can guide decision-making for process improvement.

What are the different types of Control Charts?

There are several types of Control Charts, including X-Bar and R Charts, X-Bar and S Charts, Individuals and Moving Range Charts, and Cumulative Sum (CUSUM) Charts. Each type is used for different purposes, such as monitoring the average and range of a process, individual data points, or detecting small shifts in a process.

How do you interpret a Control Chart?

Interpreting a Control Chart involves looking for any patterns or trends in the data, such as a sudden spike or drop, a shift in the average, or an unusual pattern. These could indicate a problem or change in the process that needs to be addressed. It is also important to compare the data to established control limits and look for any points that fall outside of these limits.

What are the limitations of using a Control Chart?

While Control Charts are useful for monitoring and improving processes, they have some limitations. They may not be effective for detecting small changes in a process, and their accuracy can be affected by factors such as sample size, data collection methods, and the ability to identify and eliminate sources of variation. Additionally, Control Charts are not a substitute for proper process control and management.

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