This presentation gives a little more detail on the nuts and bolts of SPC. This was made for my monthly performance indicators session here at Hanford. I alternate months with a classroom topic one month, and a hands-on computer topic the next.

This presentation is provided for peer review and comment.

This presentation gives a little more detail on the nuts and bolts of SPC. This was made for my monthly performance indicators session here at Hanford. I alternate months with a classroom topic one month, and a hands-on computer topic the next.

This presentation is provided for peer review and comment.
Excellent non-technical presentation. Even the statistically-challenged can see the process is not "smoke and mirrors" - that it is a simple and straightforward process that returns more than its cost as value to the organization.

Thanks for sharing.

Thanks for sharing Steve, very nicely done.

Wayne

This is an excellent overview of a tool that befuddles or intimidates many into careless neglect of the procedure. I especially enjoyed likening it to a smoke detector (feel safer?) and the visuals/examples were excellent. Nice work Steve, thank you!

Jennifer (non-statician) Kirley

Steve,
I find the presentation very useful. A good refresher as well.
I am very interested in the slides 19 to 21.
"The existing average and control limits should NOT be changed unless a significant trend is detected."
I would appreciate if you can explain with an additional bullet point as to how you propose to detect the significant change in average?
Thanks,
Govind.

Steve,
I find the presentation very useful. A good refresher as well.
I am very interested in the slides 19 to 21.
"The existing average and control limits should NOT be changed unless a significant trend is detected."
I would appreciate if you can explain with an additional bullet point as to how you propose to detect the significant change in average?
Thanks,
Govind.
Not answering for Steve, of course, but changes in the process mean are always the result of a significant change in the process; this would show up in a histogram as a bimodal distribution. The problem is that if you wait for this kind of thing to show up on a chart, it's too late. The type of change I'm talking about is often deliberate--a tooling change is a good example. In such cases, it's advisable to allow the process to stabilize and then recalculate control limits. Steve's point about not changing the average is perhaps misworded; the average changes when it changes.

On the issue of changing the baseline average and control limits - Davis Ballestracci gave me a great quote once - the baseline is innocent until proven guilty. You do not adjust the baseline until there is a significant change or trend in the data. And the signal for a significant change or trend is tripping one of the SPC trend rules, such as 7 points above average. Now, sometimes the data return to the previous baseline after corrective actions are taken, and in that case the baseline would remain the same. But if the data steady out at a new baseline average (or standard deviation), then the baseline would be changed.

Have I posted "The Life Cycle of a Trend" here? I wrote that up with an illustrative example a few years back.

I liked the presentation, it gives a good overview of SPC without going into the how the UCL and LCL are derived which is a sleeper for non quality folks. In your discussion you may want to mention the whole idea of decision making is making those decisions by fact, not by feel. Good job!

I understand that the center line average is recalculated from the process data, however, how would you know if your process was out of control if you keep on readjusting the center line? We had an SPC specialist that had set up a control chart for our out of box quality defects. The center line and control limits kept changing with the data, the chart was useless to me. Maybe I missed something? Is it here that was posted that the center line is innocent until proven guilty?

I am going to attach here "The Life Cycle of a Trend". That will help explain what is happening.

The idea is the EITHER a circle, or a shift in the baseline is indication of a shift in the process.

If the center line is proven guilty, your prediction of the future based upon that center line and control limits is no longer useful. You must revise the center line and control limits so that your prediction of the future is more accurate.

The Life Cycle of a Trend (http://elsmar.com/Forums/showthread.php?t=12427)

I am going to attach here "The Life Cycle of a Trend". That will help explain what is happening.

The idea is the EITHER a circle, or a shift in the baseline is indication of a shift in the process.

If the center line is proven guilty, your prediction of the future based upon that center line and control limits is no longer useful. You must revise the center line and control limits so that your prediction of the future is more accurate.


I see, we are using our charts for in line quality measures, real time, less for prediction going forward. Excellent paper I will use it as a reference the next time we do an SPC audit. The trouble with being an auditor is trying to maintain some level of proficiency in subjects we might only audit once a year. This is an excellent reference, thanks! :thanx:

Last edited by Craig H. : 1 Hour Ago at 10:58 AM. Reason: Add link to the moved article

Craig H.:

I cannot get the link to work. Suggestions?

Last edited by Craig H. : 1 Hour Ago at 10:58 AM. Reason: Add link to the moved article

Craig H.:

I cannot get the link to work. Suggestions?


Yeah, wait a few minutes 'til Marc has a chance to fix it. He just PMed me that he got the message I sent him when I realized that my handiwork was not quite working as planned. Sorry for the confusion.

Don't worry, I am not going to quit my day job....


ADDED LATER: Marc has fixed the link. It is worth a look. Thanks, Marc, and thanks, Steve!!!

I understand that the center line average is recalculated from the process data, however, how would you know if your process was out of control if you keep on readjusting the center line? We had an SPC specialist that had set up a control chart for our out of box quality defects. The center line and control limits kept changing with the data, the chart was useless to me. Maybe I missed something? Is it here that was posted that the center line is innocent until proven guilty?

Steve's description of trend's life cycles is excellent (I couldn't get the link to work but I saved a version of it the last time). The only problem is that the SPC specialist may be recalculating the control limits every time new data is added. If the process is stable, there is no reason to do that unless there is a cause as described by Steve in his paper. Many software packages allow the control limits to be recalculated each time unless you fix the control limits. Some allow data to be removed from the calculation even though it still appears on the chart as an outlier.

It probably doesn't change the limits that much in the short term but I can see why it would confuse an auditor who is not sufficiently trained in SPC. A good question for the auditor to ask is what is the purpose for changing the control limits each time and what review or corrective action is being taken for the out of control points.

Bill Pflanz

Good things you do! thanks for sharing!

I am going to attach here "The Life Cycle of a Trend". That will help explain what is happening.

The idea is the EITHER a circle, or a shift in the baseline is indication of a shift in the process.

If the center line is proven guilty, your prediction of the future based upon that center line and control limits is no longer useful. You must revise the center line and control limits so that your prediction of the future is more accurate.

The Life Cycle of a Trend (http://elsmar.com/Forums/showthread.php?t=12427)

Steve,

In your previous .ppt presentation (which is great btw), why were the center lines repeatedly re-calculated in your Hanford examples? Was there a trend in each case? I could not discern that from the slides and it was a bit confusing. If you have a minute, would you mind explaining that piece for me?

Thanks,
Scott

Steve,

In your previous .ppt presentation (which is great btw), why were the center lines repeatedly re-calculated in your Hanford examples? Was there a trend in each case? I could not discern that from the slides and it was a bit confusing. If you have a minute, would you mind explaining that piece for me?

Thanks,
Scott

In those cases, if you look back at the baseline and UCL/LCL in effect prior to the shift, you should be able to see the significant pattern (usually 7 in a row below average was most common) at the beginning of the new baseline time interval.

There is a bit of a problem that can occur in injury trending - sometimes an injury that starts out as non-reportable later becomes reportable due to the symptoms worsen. Carpal Tunnel is a good example. It may be several years from onset of initial symptoms until surgery is needed. But, the injury is charted on the first date of reporting. So sometimes past data may change aand may remove the decreasing trend. If data are affected withing the baseline time interval, I do recalculate the baseline. On very rare occasions, I have had to remove the baseline shift and go back to the old baseline.

Hello

I think it sounds correct to keep the same baseline until a significant change has been detected. We are currently looking at reprogramming our SPC software, and I would like to propose that we do not recalculate control limits/baseline periodically. I know, however that there are people in the company who disagree with me on this, I don't know why though.

I would like to know why literature (at least the books I have read) on this subject recommends recalculation. What is the origin of this approach? :confused:

Hello

I think it sounds correct to keep the same baseline until a significant change has been detected. We are currently looking at reprogramming our SPC software, and I would like to propose that we do not recalculate control limits/baseline periodically. I know, however that there are people in the company who disagree with me on this, I don't know why though.

I would like to know why literature (at least the books I have read) on this subject recommends recalculation. What is the origin of this approach? :confused:
If you recalculate the control limits despite nothing significant having changed, the control limits won't change significantly. In other words, it's mostly a waste of time but otherwise does no harm. I know that's not a justification for doing it, but it might be a justification for just letting them do it and not worrying about it.

But first - Do not calculate new average and control limits unless the existing one has been proven guilty by the data! Make sure your baseline has not become some sort of moving average - it needs to stay fixed in time, even if that was three years ago.

Con - People can get over anxious to rebaseline. And, in the timeframe just following a trend, you don't have much data to make a new baseline from. This may also be a production line where you want to take corrective action to get the data back to the old baseline. Dr. Wheeler tends to default to don't change the baseline unless you have both a data shift, and you know why the data shift occurred.

Pro - "The job of management is prediction" (Deming). The baseline average and control limits provide prediction. If the current baseline has been proven no good, it is no longer a good prediction. We want to detect when the data stabilize out again, and set up new predictions.

In either case, SPC is a remarkable self-healing, self-correcting process. If you shift the baseline too early, on a false alarm, the new baseline will be proven guilty and you will be back on the old baseline.

But first - Do not calculate new average and control limits unless the existing one has been proven guilty by the data! Make sure your baseline has not become some sort of moving average - it needs to stay fixed in time, even if that was three years ago.

Con - People can get over anxious to rebaseline. And, in the timeframe just following a trend, you don't have much data to make a new baseline from. This may also be a production line where you want to take corrective action to get the data back to the old baseline. Dr. Wheeler tends to default to don't change the baseline unless you have both a data shift, and you know why the data shift occurred.

Pro - "The job of management is prediction" (Deming). The baseline average and control limits provide prediction. If the current baseline has been proven no good, it is no longer a good prediction. We want to detect when the data stabilize out again, and set up new predictions.

In either case, SPC is a remarkable self-healing, self-correcting process. If you shift the baseline too early, on a false alarm, the new baseline will be proven guilty and you will be back on the old baseline.
I don't think we disagree on this. And permit me to reemphasize the thing about prediction. If you're doing inferential statistics and you can't make good predictions, something is wrong.

i think there is an interpretation cut short here in terms of using control charts. re-calculating baselines must also be linked to final product performace, reliability, and customer specifications. any process change leading to baseline change must be checked against these. by doing these, the changing baselines and control limits do tell us 2 things: feedback to current process & confidence in how the process will perform in the future.

good info.
want more information regarding SPC.

good info.
want more information regarding SPC.

Welcome to the Cove, Nehal :D

This place is loaded with information on SPC. Do a search of the Cove and you'll find lots of stuff, and if you have a specific question, someone here will be glad to help.

good info.
want more information regarding SPC.

There is the Hanford Trending Primer at http://www.hanford.gov/safety/vpp/trend.htm which includes more information, and information on the Hanford Performance Indicator Forum.

Steve,

Wonderful to see that you are one of the very few people who seem to be aware of the 99.7% control chart myth. This myth appears to have been propagated by popular authors such as Montgomery and has led to much misunderstanding.

NEW!

Based upon a request originally by Bechtel, but also supported by my employer Fluor, and other Department of Energy contractors here at Hanford, I've accumulated a lot of my papers into a two-day statistical training session. This covers SPC, and also choosing indictors, Dr. Deming's Red Bead Experiment and System of Profound Knowledge, some hands-on exercises, and some computer exercises. Fluor has given me permission to make the materials available on the internet, and the URL is

http://www.hanford.gov/rl/?page=1156&parent=1144

Since the materials were generated under US Government contract, there is not a copyright protection. You are welcome to make use of the materials if you find them useful. I would, of course, appreciate a mention as the source of the materials if you use them.

{Semi-Advert} Of course, if you'd like me to come make the training for your group, that could be arranged.

Whhoooowweeeee - I've hit the Mother Lode.

Thanks a bunch!

I am getting confused between stability and capabitlity. Can someone list out the attributes to capability and stability separately?

I am getting confused between stability and capabitlity. Can someone list out the attributes to capability and stability separately?

Welcome to the Cove, shank :D

It's pretty simple. "Stability" refers to a state of statistical control, meaning that the process is subject only to inherent random variation. "Capability," on the other hand, has to do with how likely the process is to produce output meeting the specification limits. A process may be stable without being capable.

I am getting confused between stability and capabitlity. Can someone list out the attributes to capability and stability separately?

Hello shank,

Jim provided an excellent explanation of stability and capability.

You may also look at this forum (http://elsmar.com/Forums/forumdisplay.php?f=116)to learn more about these topics.

Welcome to The Cove Forums and come back often! :agree1:

Stijloor.

I am getting confused between stability and capabitlity. Can someone list out the attributes to capability and stability separately?

As a practical example, let us say that I am working on a construction project that is killing one worker per month on the average, and that is stable on a control chart.

The rate of deaths of workers is STABLE and PREDICTABLE. I can project forward and say how many deaths will occurr. This may sound morbid, but in the early 20th century there were rules of thumb of how many deaths to expect per million dollars of project cost.

I hope everyone will agree here that a stable non-zero rate of killing workers in not ACCEPTABLE. The stable rate is thus NOT CAPABLE.

Note: to make predictions of my capability to meet specifications, I must be stable first in order to the prediction to be accurate.

As a practical example, let us say that I am working on a construction project that is killing one worker per month on the average, and that is stable on a control chart.

The rate of deaths of workers is STABLE and PREDICTABLE. I can project forward and say how many deaths will occurr. This may sound morbid, but in the early 20th century there were rules of thumb of how many deaths to expect per million dollars of project cost.

I hope everyone will agree here that a stable non-zero rate of killing workers in not ACCEPTABLE. The stable rate is thus NOT CAPABLE.


Stability and capability are concepts that apply to processes, not to the means of counting them. It makes no sense to say that a count of accidental deaths isn't capable--it's the process that causes the deaths that's not capable, assuming zero deaths is the criterion. Whether or not deaths (or any other undesirable outcomes) is "acceptable" is a red herring. The is math concerned only with what is, and not what should be. We can predict that there will be x airline crash fatalities per y miles traveled, but the prediction (and its statistical foundation) have nothing to do with whether or not any number of deaths is considered acceptable.

how do create spc Specification Limits?

I assume you are asking how to create SPC Control Limits (not specification limits). The specification limits, if any, come from the customer requirement (cut a board to a 2 foot plus or minus 1/8 inch length).

I have a writeup on the internet on how to conduct SPC, including generation of the control limits from the data at http://www.hanford.gov/rl/?page=1148&parent=1144. There are also plenty of other writeups in stats textbooks, and other postings here on the Cove.

how do create spc Specification Limits?

Your question is a little unclear but this may help: "How to Establish
Manufacturing Specifications"

http://www.spcpress.com/pdf/Manufacturing_Specification.pdf

I assume you are asking how to create SPC Control Limits (not specification limits). The specification limits, if any, come from the customer requirement (cut a board to a 2 foot plus or minus 1/8 inch length).

I have a writeup on the internet on how to conduct SPC, including generation of the control limits from the data at http://www.hanford.gov/rl/?page=1148&parent=1144. There are also plenty of other writeups in stats textbooks, and other postings here on the Cove.

If you are doing precision machining - and you are using correct process control, the calculations for control limits are straight forward: 75% of the specification. To see why, and for more information you can read: Statistical Process Control for Precision Machining (http://elsmar.com/Forums/blog.php?b=79)

bobdoering,

I'm curious as to why you have a logo "Stop X-bar/R Madness" ?

Machining, where constant tool wear effects results (Machine shops, SIC 3599, one of over 400 industry codes) is obviously a special case outside the general discussion. Amongst the hundreds of industries there may also be other special cases, such as those involving PID control, which fall outside the general discussion of control charts.

bobdoering,

I'm curious as to why you have a logo "Stop X-bar/R Madness" ?

Machining, where constant tool wear effects results (Machine shops, SIC 3599, one of over 400 industry codes) is obviously a special case outside the general discussion.

I always find it amusing that precision machining is considered such an insignificant "special case".

Even if so, so what?

It exists, and its practitioners need to understand that they have been sold a bill of goods with traditional charting systems. The madness is specifically for those that have been driven to use that chart when it does not apply - specifically precision machining. It has driven people in precision machining mad - with ridiculously compressed control limits and over and under reported capabilities. Most have no idea what to do or where to go, and it has been my goal to correct that problem.

I do not doubt that there are truly naturally occurring normal processes, that meet the requirement of being set to a nominal value (mean), and stay at that value (some values a little lower, some values a little higher) without any interaction by an operator. There may be a bunch of them in the other SIC codes - good for them. They can use the myriad of textbooks to get the information they need. They were written just for them. I have even stronger belief that there are other exceptions - but that others affected by them have, too, been drilled into the normal fallacy, and the associated charting methods.

My point (and the point of my avatar) is to rid the madness in precision machining. I find it a very important cause, no matter how minute as a ratio of industry codes (as if they are equally distributed, and as if 200 or 300 more SIC codes might not have captured machining in their industry) or by any other "measure". This excuse of "being an exception" has been used by the SPC software suppliers, which do not provide the correct tool for precision machining to use - leaving them to fend for themselves with paper charts or Excel spreadsheets - or worst, the wrong control because they paid for garbage software sold by people who have no idea about how to control machining.

Others may have the opportunity to correct the rest of the world's statistical problems - at least for now.

Often when marketing a solution to a problem, you have to catch someone's attention to the fact that they have a problem. Apparently, your attention was caught - so there is clearly a level of success.

Besides, I was told I couldn't use my old avatar any longer.

I always find it amusing that precision machining is considered such an insignificant "special case".

Please read my post. I did not say that machining is "insignificant". I stated that machining is one of more than 400 industry codes.

Mis-using any chart may "drive people mad" as you suggest.

... but that others affected by them have, too, been drilled into the normal fallacy, and the associated charting methods.

The belief in the normality fallacy widespead. Processes do not need to be normal for the use of Shewhart control charts. I strongly recommend Don Wheeler's excellent book on the topic: "Normality and the Process Behaviour Chart".

Please read my post. I did not say that machining is "insignificant". I stated that machining is one of more than 400 industry codes.

Well, if significance was not the point of the 1 out of 400 statistic you mentioned, then your actual point remains clouded. Especially when that statistic has nothing to do with the amount of machining in the overall scope of all manufacturing industries, due to a vast number of captured shops not identified by their claimed SIC code.

The belief in the normality fallacy widespead. Processes do not need to be normal for the use of Shewhart control charts. I strongly recommend Don Wheeler's excellent book on the topic: "Normality and the Process Behaviour Chart".

I have read Dr. Wheeler's books, as he has read mine. It is true he has identified that processes do not need to be normal for the use of Shewhart control charts. However, that is no more of a 'law" than the need for the charts to be normal. Not all non-normal charts can successfully use the Shewhart control charts. Fact is, if you look at one of the points for the claim that processes do not need to be normal to use Shewhart charts, you will find the review of Burr's 27 non-normal distributions. One of the distributions not covered by Burrs 27 non-normal distributions is the continuous uniform distribution. Too bad, it clearly shows that it does not play by the traditional Shewhart control chart rules. But, the X hi-lo/R Chart for that distribution does support Shewhart's premises for economic control, as illustrated throughout many postings in this forum, suitable for searching.

By the way, Dr. Wheeler's response to my book was "I have to admit that I have not directly addressed in my books the complex processes you have addressed."

Fact is, if you look at one of the points for the claim that processes do not need to be normal to use Shewhart charts, you will find the review of Burr's 27 non-normal distributions. One of the distributions not covered by Burrs 27 non-normal distributions is the continuous uniform distribution. Too bad, ...

Don Wheeler's book "Normality and The Process Behaviour Chart", examines 1143 distributions (not 27), with many other than Burr. He gives an excellent validation to Shewharts's work.

Don Wheeler also examines uniform distributions on page 117, "Advanced Topics in SPC".

Don Wheeler also examines uniform distributions on page 117, "Advanced Topics in SPC".

It is a nice, generic justification for control limits, in general. I agree in the use of control limits, in general. But, it does not specifically support usage of the X-bar R chart (the original conversation) for a continuous uniform distribution. It may work for a discrete uniform distribution - if you find such a process to control, such as rolling a die. But, that does not relate to my point.

To use Dr. Wheeler's logic (page 116, "Advanced Topics in SPC"): the strongest justification that X hi/lo-R charts are ideal for precision machining is it works well in practice, and it provides effective action limits when applied to real world data.

What's more - it makes sense, and it make sense to the operator. It also provides far more useful data to the practitioner than the X-bar R chart. It can tell you when to make an adjustment, when to change a tool, and the tool wear rate. The X-bar R chart can not do that - and at best it generates overcontrol. It is easy to show how that can happen. I am sure if you have had an opportunity to properly implement this technique yourself, its benefits would be clear.

Again, these are issues that have already been discussed throughout the forum. Feel free to further search out the specifics.

It also provides far more useful data to the practitioner than the X-bar R chart. It can tell you when to make an adjustment, when to change a tool, and the tool wear rate. The X-bar R chart can not do that - and at best it generates overcontrol.

The X-bar R chart is not intended to tell you when to change a tool nor to measure tool wear rates. It is however an excellent tool for the majority of process applications, regardless of data distribution.

Don Wheeler also makes an excellent point on page 36 "Normality and The Process Behaviour Chart". Regarding the majority of processes, it takes 3,200 data points to test for lack of fit to a distribution out to +/-2.95 sigma ( or 1,480,000 observations to check for normality out to +/-4.5 sigma) ... and by the time such data is collected, the process will have changed.

The X-bar R chart is not intended to tell you when to change a tool nor to measure tool wear rates. It is however an excellent tool for the majority of process applications, regardless of data distribution.

The fact that X-bar R chart is not intended to tell you when to change a tool nor to measure tool wear rates shows its dramatic limitation in value in precision machining. It does have its purpose in other cases, and I am not denying that fact.

Don Wheeler also makes an excellent point on page 36 "Normality and The Process Behaviour Chart". Regarding the majority of processes, it takes 3,200 data points to test for lack of fit to a distribution out to +/-2.95 sigma ( or 1,480,000 observations to check for normality out to +/-4.5 sigma) ... and by the time such data is collected, the process will have changed.

Academically, and in many cases, that is true. But, the number of points correctly collected in precision machining required to establish the distribution depends on the tool wear. It might only take 10 points - it could take a week of points. The better the tool wear, the more points it takes. It is the significant sampling error in the X bar-R charting methodology that creates its misinformation in precision machining. As I have mentioned, all of its problems have been clearly illustrated elsewhere in the forum for those that care to study the problem, and do not need to be repeated here.

It is good to read Dr. Wheeler's books, but it is not a good idea to stop there. Every author has a limit to their experiences, and he is no exception. His work is academically rigorous, but the empirical evidence shows the need for another approach in precision machining. One that has been shown to be very effective. It is the fear of the practitioners imposed by those who are compelled to think that the X-bar R will control their process (it will not) by blindly citing such references that is the crux of the problem in the field. You have provided adequate evidence of that. And to your original question, that is the point of the avatar - my goal to put a stop to that madness. Its point is not to say X-bar R is never good, or that the rules for control are always wrong. There is no madness, no worries, no cares for those cases. But the fact that they work for them can not be extrapolated -or "rubber stamped" as is typically the case - to the case of precision machining - or perhaps other "exceptions" you cited earlier.

But the fact that they work for them can not be extrapolated -or "rubber stamped" as is typically the case - to the case of precision machining - or perhaps other "exceptions" you cited earlier.

If you take the time to read Dr Wheeler's books thoroughly, you will find many practical examples and very detailed analyses, with no signs of "rubber stamping" as you suggest.

If you take the time to read Dr Wheeler's books thoroughly, you will find many practical examples and very detailed analyses, with no signs of "rubber stamping" as you suggest.

I own his books, and have read them. They were a very good foundation and starting place to progress from. And, we have progressed beyond the trend line charting analysis he has proposed for tool wear. Its limitations have been established (some of which you mentioned), easy to prove, and resolved with newer methodology. The beauty of the newer methodology is that it is so clear, logical and practical that it does not require very detailed analysis. Its elegance is in its simplicity and effectiveness.

The "rubber stamping" does not refer to his work, but the incorrect application of the statistical process control concepts in the field.

Hello everybody,
I have a pre study material, where some charts and questionys are offen for me.
Could somebody help me with MSA, SPC, FMEA questions in the zip file?
It would be very helpful for me to take the TS evaluation!:thanx::thanks:

Hello everybody,
I have a pre study material, where some charts and questionys are offen for me.

Offen?

Stijloor.

O excuse me for my not real excellent english.
I mean I can not answer the questions in the application excercises.

O excuse me for my not real excellent english.
I mean I can not answer the questions in the application excercises.

Do you have (access to) the AIAG Core Tools manuals?

Stijloor.

Actually no, because I got the evaluation date too late and I had no time to purchase the AIAG docs, therefore I seek answers to those questions.

Actually no, because I got the evaluation date too late and I had no time to purchase the AIAG docs, therefore I seek answers to those questions.

Let's be realistic. You uploaded a zip file that contains nine folders, and a total of about 30 files. There are dozens of questions involved. You can't expect anyone here to take the time to go through all of that and do your work for you. You've come to a place where there's lots of information on all of the topics involved, and there are a lot of people who are willing to help you learn, but no one here is going to turn you into an expert overnight. Search the Cove and ask specific questions and you'll get plenty of help.

I can not answer the questions in the application excercises.
In the zip file there is SPC with some charts and questions , which are at the end of the doc file
I would appreciate if anybody could help me only with these questions.:thanx:

I can not answer the questions in the application excercises.
In the zip file there is SPC with some charts and questions , which are at the end of the doc file
I would appreciate if anybody could help me only with these questions.:thanx:

snowman6705 - why don't you post the exact question you are having trouble with along with any related data and perhaps we can help. A lot of people here are willing to help - but we can't do the work for you.

OK then,
here are the questions and the charts.

OK then,
here are the questions and the charts.

snowman6705 - I do not mean to be harsh - but we can not answer all the questions for you -

I downloaded your zip file and opened the word document under the SPC section. The first line states

"The following pages give examples of the level of knowledge required. If you cannot answer all of these sample questions below, it is recommended you do further pre-study prior to the training course."

Your above posts contain all the questions listed in the word document.

We would be remiss in giving you the answers. You will learn nothing. And you won't find many folks here that would do this.

If you are struggling with a specific question, please ask - we can help - but we won't "give" you the answers.

;)This presentation gives a little more detail on the nuts and bolts of SPC. This was made for my monthly performance indicators session here at Hanford. I alternate months with a classroom topic one month, and a hands-on computer topic the next.

This presentation is provided for peer review and comment.

hi thanking you,

i have lot data with me & i want to make control chart for that but i dont have software like minitab for this can you help me

for making control chart i required a software, can you help

i have lot data with me & i want to make control chart for that but i dont have software like minitab for this can you help me
It is hard to answer a question like this correctly so generically. It would be good to know what characteristics and processes are you studying. Also, it would be good to know if you did a capability study, and if so, how did you collect the data.

for making control chart i required a software, can you help

Excel will work you just have to type in the formulas and create the chart