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September 2010: Linear Regression Analysis 09/03/2010
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Should data be checked for normality prior to creating a linear regression? Or are the P and  R² values good enough to determine acceptance?

The Y and X data do not need to be Normal to do regression (neither the Ys nor the Xs, single X or multiple Xs).  Normality comes into play when examining the residuals.  When the residuals follow a Normal distribution (a Normal Probability Plot is my favorite tool), then the regression model should be sufficient for prediction.  If not, then another model or data transformation should be considered.

The P values and R2 values will help to determine the strength of the relationship – there is no connection to Normality.

In addition to testing the residuals for Normality, it is a good idea to plot them against the predicted values and the observed order to see if there is a pattern that is not random.

It is always a good idea to include a prediction interval, for either a point estimate or a regression plot of the data.
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February 2009: Go/No Go Gages 01/30/2009
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I work quality control for a small manufacturing company. I am looking for information about making go/no go gages for inspecting bores on parts we manufacture. We have been moving into Lean manufacturing and six sigma. We are ID grinding the bores and the part moves to the next operation. The parts are in a constant state of movement so there is no time to check all the bores on the CMM. This is why we are looking into making Go/NO gages for checking 100% of the bores. In the past we did batch manufacturing and had time to check 100% on the CMM. If I have a bore of 3.1505 +.0005 -0, where can I find the information on what size to make the go/no go gage?

Below you can see a picture of what a gage might look like.  The procedure that I would recommend is:

Perform a capability study of a sample (30 ?) pieces that have been bored.  Use the maximum inscribed circle or cylinder method to measure the diameter.  That should give you the confidence that your process is delivering the desired results.  For example, a Cpk (or Ppk) of 1.0 should result in about 3 defects per 1,000. Then you can use a Go – No Go gage to tell you when there has been a change to your process.

The diameter of the smaller cylinder is the Go gage.  Diameter dimension is 3.1505 as measured on the CMM using maximum circumscribed cylinder.  The gage should pass through the length of the bore.  This side of the gage must pass through the hole.  If it does not pass through, the bore is too small.
 

The diameter of the larger cylinder is the No Go gage.  Diameter dimension is 3.1510 as measured on the CMM using maximum circumscribed cylinder.  The gage should pass through the length of the bore.  This side of the gage must NOT pass through the hole.  If it does pass through, the bore is too large. 

A special thank you to Christine Henning, Inspection Supervisor at John Deere Horicon, for guidance on this reply.

Woody

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October 2008: What is Gemba? 12/18/2008
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What is "gemba"?  I've heard folks around my place of employment discussing gemba walks and standard work.  I asked one of them what it meant and they just answered that it was some Chinese or Japanese thing.......

I first encountered the term gemba over ten years ago from our marketing department.   They were doing customer and product research utilizing Quality Function Deployment (QFD).  They talked about the value and insight they obtained from their “gemba visits”.  They made arrangements with our customers to observe them using our products (in this case it was watching them mow grass).  More recently, the term gemba is being used by the folks that perform the assessments of our production system (over the past two years the Deere Production System and the Deere Product Quality System were the subject of our section’s monthly meetings).  We are transitioning from an assessment where presentations are made to the assessment team to where the assessor goes to the site (shop floor) to make their assessment.  They “go see” the processes and talk to the operators. 

This is what the web site gemba.com has to say:

Gemba is the Japanese word for "actual place" and in business terms it is where you create value for your customers through daily work.  Gemba is the place to go for improvement.

I find that many translations from Japanese (just the words) do not convey the full meaning – and several translation sources help me understand it better.  Run gemba on your favorite search engine (i.e. Google, Yahoo) for more understanding. 

I think you could also consider calling our section program meeting business tours “gemba.”  A purest may not consider them true gemba, but the spirit is similar.

Woody

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November 2008: Cp, Cpk, Pp, Ppk 12/05/2008
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I thought I understood Cp and Cpk, then I see Pp and Ppk and it looks the same to me. Help me understand this Cp, Cpk, Pp, Ppk stuff. And then there is long-term and short-term capability.  Can you help me sort this out?

I feel your pain and confusion regarding these Capability Indices – we are not alone.    Not all literature is in agreement and to further complicate matters, not all software treats them the same.  You need to know the differences whenever you use these indices.

Let me say it again, I have not found a definitive source that all can agree is the “right” way to calculate any of the indices. 

A little history about capability indices: prior to 25 years ago, Cr and Cp were the two predominant indices used.  Cr (Capability Ratio) is 1/Cp – they were convenient ways to describe the natural variation of the process compared to the tolerance. Their origins go back much farther and were associated with control charts.  Since control charts used Rbar/d2 to estimate standard deviation, the calculation of Cp used Rbar/d2.

The original definition and interpretation of Capability was from Statistical Quality Control Handbook Capability by the Western Electric Company (1956).  In the book, capability is defined as “the natural or undisturbed performance after extraneous influences are eliminated.  This is determined by plotting data on a control chart.”   This means that engineers used process capability studies to eliminate all special causes from a process before using that process on the “shop floor” and proved it using Rbar/d2 and control charts. The index Cpk is a more recent index that also accounts for the centering of the process, and is predominately utilized today.

If statistical accuracy is required, it is best to use Cpk. If repeatability or trend improvement is the primary concern then Ppk is adequate enough.  In other words, if you prefer stable results where long-term trends are of more interest, then Ppk is preferable.  If you prefer quick notification of variation, then Cpk may be preferable.

The majority of literature and software (but not all of them) say to use Rbar/d2 when calculating Cp and Cpk, and use the sample standard deviation for Pp and Ppk.  Among the literature that promote this are: AIAG; and “The Certified Six Sigma Green Belt Handbook” published by ASQ.

Here is how I think about it:

Compute both estimates of standard deviation.  If they are close to the same, it doesn’t matter which one you use (there is probably more error from the sample size than the computation method).  If they are different, then your sample probably includes some special cause variation (shift, drift, or outlier) – Ppk is probably the best choice.

When I am making the capability calculations:

If the data comes from a sequence (i.e. they are consecutive samples from the process – or you have a strong believe that there are no special causes acting on the sequence of samples drawn), use Rbar/d2 to calculate Cp and Cpk.  Otherwise use the sample standard deviation reported as Pp and Ppk. 

When I am reading a report of a capability study:

I should not accept the index value unless I know both how the data was collected and the method used to estimate the standard deviation.

So, the bottom line is – you need to know how the standard deviation was calculated regardless of whether it is called Cp, Cpk, Pp or Ppk.

Remember why we perform capability studies (Cpk or Ppk):  to compare tolerance to processes and determine how many good and bad parts the process will produce.  Under "ideal" conditions the result of Cpk and Ppk should be the same. Ideal conditions being: Normal distribution, sufficient data set size, no autocorrelated data, and data in statistical control.

I am going to leave the discussion about long-term and short-term and how it may relate to a one-and-a-half sigma shift for another day.  Happy calculating!

Woody

p.s. A thanks to Bob Einwalter, my editor on this reply.





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