Lynda Finn, President of Statistical Insight, has written an article on how to create a control chart for seasonal or trending data (where there is an underlying structural variation in the data). Essentially you need to account for the structural variation to create the control limits for the control chart. She also provides a Minitab project file. Both are available for download from the Curious Cat Management Improvement Library.
Read a bit more can find a bit more on the Spanish edition, in Spanish. EstadÃstica para Investigadores DiseÃ±o, innovaciÃ³n y descubrimiento Segunda ediciÃ³n.
* Graphical Analysis of Variance
* Computer Analysis of Complex Designs
* Simplification by transformation
* Hands-on experimentation using Response Service Methods
* Further development of robust product and process design using split plot arrangements and minimization of error transmission
* Introduction to Process Control, Forecasting and Time Series
Book available via Editorial Reverte
The recipient of the 2008 William G. Hunter Award is Ronald Does. The Statistics Division of the American Society for Quality (ASQ) uses the attributes that characterize Bill Hunter’s (my father – John Hunter) career – consultant, educator for practitioners, communicator, and integrator of statistical thinking into other disciplines to decide the recipient. In his acceptance speech Ronald Does said:
For the past 15 years I have been the managing director of the Institute for Business and Industrial Statistics. This is a consultancy firm owned by the University of Amsterdam. The interaction between scientific research and the application of quality technology via our consultancy work is the core operating principle of the institute. This is reflected in the type of people that work for the institute, all of whom are young professionals having strong ambitions in both the academic world and in business and industry.
The kickoff conference attracted approximately 80 statisticians and statistical practitioners from all over Europe. ENBIS was officially founded in June 2001 as “an autonomous Society having as its objective the development and improvement of statistical methods, and their application, throughout Europe, all this in the widest sense of the words” Since the first meeting membership has grown to about 1300 from nearly all European countries.
Even if you are using full factorial to get the same amount of information as a fractional factorial test, it will take more time since you need more data to see statistically relevant differences between the many experiments. You might be wondering how fractional factorial can be accurate if interactions are possible?
Random interactions of high relevance are very rare, especially when looking for interactions of more than 2 factors. You really need to design tests where you look for meaningful interactions that are based on true business requirements rather than hoping for a random and low influence interaction between a red button, a hero shot and a headline.
Some good resources for more on the topics discussed above: What Can You Find Out From 8 and 16 Experimental Runs? by George Box – Statistics for Experimenters – Design of Experiments in Advertising.
This is a great example of a positive special cause. How would you identify this? First you would have to stratify the data. It also shows that sometimes looking at the who is important (the problem is just that we far too often look at who instead of the system so at times some get the idea that it is not ok to stratify data based on who – it is just be careful because we often do that when it is not the right approach and we can get fooled by random variation into thinking there is a cause – see the red bead experiment for an example); that it is possible to stratify the data by person to good effect.
The following 20 pages in the book are littered with very interesting details many of which tie to thinking systemically and the perils of optimizing part of the system (both when considering the system to be one person and also when viewing it as society).
I have recently taken to reading more and more about viruses, bacteria, cells, microbiology etc.: it is fascinating stuff.
Many people state that data can lie. Obviously data can’t lie.
There are three kinds of lies: Lies, damn lies and statistics – Mark Twain
Many people don’t understand the difference between being manipulated because they can’t understand what the data really says and data itself “lying” (which, of course, doesn’t even make sense). The same confusion can come in when someone just draws the wrong conclusion from the data that exists (and them blames the data for “lying” instead of themselves for drawing a faulty conclusion). The data can be wrong (and the data can even be made faulty intentionally by someone). Or someone can draw the wrong conclusion from data that is correct. But in neither case is the data lying. It is also common to believe the data means something other than what it does (therefore leading to a faulty conclusion).
For a very simple example, believing if the average height for adults in the USA is 5 feet 9 inches that half the people must be taller and half the people must be shorter. You could then draw the conclusion that half the adults must be shorter than 5 feet 9 inches. But that is not what an average height means (it is basically what median means, though if you want to get technical, it doesn’t mean exactly that). You might draw the conclusion that the average height of an adult in California is 5 feet 9 inches but that is not supported by only the data that says what the height of an average adult in the country is. The same hold for drawing the conclusion that 5 feet 9 inches is the average height of a women. Now in this simple example, hopefully people can see the faulty reasoning, but such reasoning often goes on without consideration.
In a great speech by Marisa Meyer she speaks of Google makes decisions using data and that data is apolitical. One benefit of this, she says, is that Google makes decisions on what the data supports not political considerations. The belief that basing decision on what the data supports leads to better decisions can seem false for those that accept the quote about 3 types of lies (or those that see there is some weakness to this point if those supposedly basis decisions on data don’t really understand how to do so).
“All Models Are Wrong But Some Are Useful” -George Box
A great quote. Here is the source: George E.P. Box, Robustness in the strategy of scientific model building, page 202 of Robustness in Statistics, R.L. Launer and G.N. Wilkinson, Editors. 1979.
See more quotes by George Box.
Performance Measures and Statistics Course [the broken link was removed] – free course materials from a 2 day training course by Steven Prevette. Topics include: Dr. Deming’s red bead experiment, operational definitions, selecting performance targets, SPC, theory of variation, case studies, control charts, pdsa, pareto charts, histograms…
Never a Dull Day: The Life of an Industrial Statistician by Gerry Hahn.
Gerry Hahn was one of the great applied statisticians of the last 50 years, working at GE for over 45 years. Six sigma has many variants, he is one of those that understood how to apply six sigma well.
- Has Six Sigma been a failure?
- Opportunities and Challenges for Industrial Statisticians in the 21st Century by Gerald J. Hahn
- 3 Six Sigma Articles [the broken link was removed] by Gerry Hahn
- Deming Retrospective Inputs by Gerry Hahn