Talk:Control chart

This article should not be merged. Control charts are a related but very clearly separate issue from the common and special causes from which they arise. -- Phil 20:39, 14 December 2005 (UTC)

Adding example pictures

I think this whole area of different statistical charting could really benefit from some example pictures of the relevant chart. Anyone care to add some example charts? Thanks --Evolve2k 08:05, 6 January 2006 (UTC)

I second that - I'm following the "The Pareto chart is one of the seven basic tools of quality control, which include the histogram, Pareto chart, check sheet, control chart, cause-and-effect diagram, flowchart, and scatter diagram. See Quality Management Glossary." to see all the charts, and most have them, but this does not, and from the description it takes some time to figure out what is displayed, and how.

Other control charts besides Shewhart ones

Shewhart charts are most common in control charting because of their relative simplicity (over other charts), and therefore are an excellent jumping off point. However, there are control charts (CUSUM, EWMA, and multivariate forms of CUSUM, EWMA, and Shewhart charts) which differ considerably from the basic rules being introduced here. The article in its present form is very much targeted to Shewhart control charts and overlooks the fact that there are other types of control charts which also test to see whether a process mean or variance has shifted. Significant amounts of information need to be added to this article, and I envision much of the content presently in this one moving to a new article titled Shewhart control charts. I am going to try to add a little bit to this article to get it started. --Statwizard 15:03, 23 March 2006 (UTC)

Factual error

Statement from article: "In 1938, Shewhart's innovation came to the attention of W. Edwards Deming, then working at the United States Department of Agriculture but about to become mathematical advisor to the United States Census Bureau."

"1925-26 Deming worked at the Hawthorne plant of the Western Electric Company on research with telephone transmitters, thus becoming aware of Shewhart's work." A Brief History of Dr. W. Edwards Deming British Deming Association SPC Press, Inc. 1992

I made that change, but I may have been hasty, since this source mentions nothing about that [1]. I suppose he could have done the research as part of his PhD degree, so I'll look for that. Spalding 18:55, 18 June 2006 (UTC)

This source [2] shows that he spent summers there, so I'll leave the edit. Spalding 19:01, 18 June 2006 (UTC)

The Deming Institute chronology is not intended to be comprehensive. Deming met Shewhart in 1927. The 1930s reference in the article is to the lectures Shewhart made at the USDA which led to Deming editing these for publication as Shewhart's 1939 book. Deming and Shewhart had a history before the lectures at UDSA. Leaders100 19:05, 18 June 2006 (UTC)

Types of Charts Table

The Types of Charts table looks great and provides quick access to the articles on the various types of control charts. However, I'm concerned that it may be inaccurate or, at least, misleading. For instance, anyone reading would believe that Individuals (ImR or XmR) charts can only be used with variable data, but the XmR chart is also a substitute for the attributes charts (p-, np-, u- and c-chart) when the underlying assumptions for those charts are violated.

Any update that I can imagine for this table would make it harder to read, so the question is: do we update it, reduce the level of detail to eliminate the problem, or just leave it?

Alternatively, would it be appropriate to have a "chart chooser" flowchart to help clarify the role of and relationships between the different charts?

Tom Hopper 15:04, 17 March 2010 (UTC) —Preceding unsigned comment added by Thopper (talk • contribs)

• Do you have a source for this? Montgomery specifically calls out this practice in his textbook (Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc. p. 309. ISBN 9780471656319. OCLC 56729567.): "A Misapplication of ${\bar {x}}$ and R Charts" where a consultant recommended using a ${\bar {x}}$ and R chart to plot fraction nonconforming. You'd have a hard time convincing statisticians as the distributions underlying each type of chart are so different (particularly in range of values each can assume, in skewness, in the independence or dependence of the mean and standard deviation).

-- DanielPenfield (talk) 14:32, 18 March 2010 (UTC)

Daniel, Wheeler discusses, briefly, the correct use of attribute charts, and the substitution of individuals and moving range (XmR) charts (not ${\bar {X}}$ and R charts), in Understanding Variation, where he refers to XmR charts as the "swiss army knife" of control charts, and in this SPCToolkit article in Quality Digest, where he states "you can't go far wrong using an XmR chart with count data, and it is generally easier to work with empirical limits than to verify the conditions for a theoretical model." By this statement, he is referring to the fact that dispersion for XmR charts is calculated from the data, while for attributes charts the dispersion is estimated based on the mean. In the book, he gives two examples of count data where the assumptions for attributes charts are violated, producing inappropriate control limits, and shows that the XmR chart provides correct limits. According to the article Selecting the Right Control Chart, which also mentions this issue, the use of XmR charts for count data is also discussed in Wheeler and Poling's Building Continual Improvement, though I do not have a copy to confirm this.

Tom Hopper 18:18, 21 March 2010 (UTC) —Preceding unsigned comment added by Thopper (talk • contribs)

• What are the specific assumptions for which he's constructed examples? I'm talking about the examples you refer to in your statement "In the book, he gives two examples of count data..." Is it the constancy of p, perhaps? -- DanielPenfield (talk) 14:39, 22 March 2010 (UTC)

In the appendix starting on page 140, he gives two examples. In the first, the data is count of on-time shipments per month, where the probability p is not the same for all shipments in a given month. The resulting p-chart limits are too wide, while an XmR chart provides more useful limits. The second example uses data for percent of total shipments by premium freight. Here, the resulting p-chart limits are too narrow, which Wheeler attributes to a large area of opportunity (very high counts) and non-constant probability p.

Tom Hopper 08:48, 23 March 2010 (UTC)

• I was able to take a look at an earlier edition of his book over the weekend. I would consider his position fairly controversial for the reason that it looks like the processes he's trying to monitor are always out-of-control, strictly speaking. Imagine constructing a p-chart from observations randomly drawn from one or the other of two dice: One is a four-sided die and the other is a twenty-sided die and a "nonconformance" is defined as "the die for this particular observation comes up with the number one". So sometimes p = 0.25 and sometimes it's 0.05. Even if he plots these data on an individuals chart, ${\bar {X}}$ will be 0.25 for some samples and 0.05 for others (and the standard deviation will be 0.43 for some samples and 0.22 for others). To work as advertised, control charts must be constructed from identically-distributed observations when the process is deemed "in control". Furthermore, his claim that "the XmR chart will still work because it uses an empirical approach rather than being based on a specific probability model" (from p. 138 of 1993 edition) is also a flat-out contradiction of the individual chart requirement that observations be independent and normally-distributed (that is the specific probability model).

-- DanielPenfield (talk) 14:31, 29 March 2010 (UTC)