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2011 Management Blog Roundup: Stats Made Easy

Posted on January 8, 2012  Comments (0)

The 4th Annual Management blog roundup is coming to a close soon. This is my 3rd and final review post looking back at 2001, the previous two posts looked at: Gemba Panta Rei and the Lean Six Sigma Blog.

I have special affinity for the use of statistics to understand and improve. I imaging it is both genetic and psychological. My father was a statistician and I have found memories of applying statistical thinking to understand a result or system. I also am comfortable with numbers, and like most people enjoy working with things I have an affinity for.

photo of Mark Anderson

Mark Anderson

Mark Anderson’s Stats Made Easy blog brings statistical thinking to managers. And this is not an easy thing to do, as one of his posts shows, we have an ability to ignore data we don’t want to know. Wrong more often than right but never in doubt: “Kahneman examined the illusion of skill in a group of investment advisors who competed for annual performance bonuses. He found zero correlation on year-to-year rankings, thus the firm was simply rewarding luck. What I find most interesting is his observation that even when confronted with irrefutable evidence of misplaced confidence in one’s own ability to prognosticate, most people just carry on with the same level of self-assurance.”

That actually practice of experimentation (PDSA…) needs improvement. Too often the iteration component is entirely missing (only one experiment is done). That is likely partially a result another big problem: the experiments are not nearly short enough. Mark offered very wise advice on the Strategy of experimentation: Break it into a series of smaller stages. “The rule-of-thumb I worked from as a process development engineer is not to put more than 25% of your budget into the first experiment, thus allowing the chance to adapt as you work through the project (or abandon it altogether).” And note that, abandon it altogether option. Don’t just proceed with a plan if what you learn makes that option unwise: too often we act based on expectations rather than evidence.

In Why coaches regress to be mean, Mark explained the problem with reacting to common cause variation and “learning” that it helped to do so. “A case in point is the flight instructor who lavishes praise on a training-pilot who makes a lucky landing. Naturally the next result is not so good. Later the pilot bounces in very badly — again purely by chance (a gust of wind). The instructor roars disapproval. That seems to do the trick — the next landing is much smoother.” When you ascribe special causation to common cause variation you often confirm your own biases.

Mark’s blog doesn’t mention six sigma by name in his 2011 posts but the statistical thinking expressed throughout the year make this a must for those working in six sigma programs.

Related: 2009 Curious Cat Management Blog Carnival – 2010 Management Blog Review: Software, Manufacturing and Leadership

Dr. Deming in 1980 on Product Quality in Japan and the USA

Posted on December 5, 2011  Comments (0)

I posted an interesting document to the Curious Cat Management Library: it includes Dr. Deming’s comments as part of a discussion organized by the Government Accounting Office in 1980 on Quality in Japan and the United States.

The document provides some interesting thoughts from Dr. Deming and others; Dr. Deming’s statements start on page 52 of the document. For those really interested in management improvement ideas it is a great read. I imagine most managers wouldn’t enjoy it though (it isn’t giving direct advice for today, but I found it very interesting).

Some selected quotes from the document follow. On his work with Japan in 1950:

This movement, I told them, will fail and nothing will happen unless management does their part. Management must know something about statistical techniques and know that if they are good one place, they will work in another. Management must see that they are used throughout the company.
Quality control must take root with simple statistical techniques that management and everyone in the company must learn. By these techniques, people begin to understand the different kinds of variation. Then quality control just grow with statistical theory and further experience. All this learning must be guided by a master. Remarkable results may come quick, but one has no right to expect results in a hurry. The learning period never ends.

The statistical control of quality is not for the timid and the halfhearted. There is no way to learn except to learn it and do it. You can read about swimming, but you might drown if you had to learn it that way!

One of the common themes at that time was Deming’s methods worked because Japanese people and culture were different. That wasn’t why the ideas worked, but it was an idea many people that wanted to keep doing things the old way liked to believe.

There may be a lot of difference, I made the statement on my first visit there that a Japanese man was never too old nor too successful to learn, and to wish to learn; to study and to learn. I know that people here also study and learn. I’ll be eighty next month in October. I study every day and learn every day. So you find studious people everywhere, but I think that you find in Japan the desire to learn, the willingness to learn.

You didn’t come to hear me on this; there are other people here much better qualified than I am to talk. But in Japan, a man works for the company; he doesn’t work to please somebody. He works for the company, he can argue for the company and stick with it when he has an idea because his position is secure. He doesn’t have to please somebody. It is so here in some companies, but only in a few. I think this is an important difference.

At the time the way QC circles worked in Japan was basically employee led kaizen. So companies that tried to copy Japan told workers: now go make things better like the workers we saw in Japan were doing. Well with management not changing (and understanding Deming’s ideas, lean thinking, variation, systems thinking…) and staff not given training to understand how to improve processes it didn’t work very well. We (those reading this blog) may all now understand the advantages one piece flow. I can’t imagine too many people would jump to that idea sitting in their QC circle without having been told about one piece flow (I know I wouldn’t have), and all the supporting knowledge needed to make that concept work.

QC circles can make tremendous contributions. But let me tell you this, Elmer. If it isn’t obvious to the workers that the managers are doing their part, which only they can do, I think that the workers just get fed up with trying in vain to improve their part of the work. Management must do their part: they must learn something about management.

Continue reading →

Management Improvement Carnival #139

Posted on August 10, 2011  Comments (0)

comic showing the dangers of drawing false conclusion based on statistical significance

Randall Munroe illustrates RA Fisher point that you must think to draw reasonable conclusions from data. Click the image to see the full xkcd comic.


The Curious Cat Management Improvement Carnival has been published since 2006. We find great management blog posts and share them with you 3 times a month. We hope you find these post interesting and find some new blogs to start reading. Follow John Hunter online: Google+, Twitter, LinkedIn, more.

  • Questioning the Value of the P-Value by Jon Miller – “Father of modern statistics Ronald A. Fisher invented the p-value as an informal measure of evidence against the null hypothesis. Although often overlooked, Fisher called on scientists use other types of evidence such as the a priori plausibility of the hypothesis and the relative strengths of results from previous studies in combination with the p-value.”
  • Teachers Cheating and Incentives by Dan Ariely – “they began to do anything that would improve their performance on that measure even by a tiny bit—even if they messed up other employees in the process. Ultimately they were consumed with maximizing what they knew they would be measured on”
  • It’s About The Journey and Sometimes It Starts With Failure by Tim McMahon – “If we allow ourselves to become discouraged during the learning process we may give up right before we reach our goal. Anytime we learn from our efforts we are in the process of succeeding. Each lesson brings us closer to our intended result.”
  • When Patents Attack – “as many as 80 percent of software engineers say the patent system actually hinders innovation. It doesn’t encourage them to come up with new ideas and create new products. It actually gets in their way.” (I added “An outdated intellectual property system” as deadly management/economic disease number 9 – building on Deming’s 7 deadly
    diseases a few years ago – John). Also from NPR: The Patent War
  • 3 Things You Can Do When Your Manager Doesn’t Support Continuous Improvement by Ron Pereira – “So keep fighting… keep learning… keep improving. If you do this, one thing is for certain, you and the organization you work for will be better off even if they don’t realize it.”

    • Continue reading →

Best Selling Books In the Curious Cat Bookstore

Posted on August 3, 2011  Comments (0)

The most popular books in July at Curious Cat Books were, Statistics for Experiments (1st edition), followed by Statistics for Experiments (2nd edition) and the Leader’s Handbook by Peter Scholtes. These books are great, I am happy others have been finding them and reading them. Statistics for Experimenters is co-authored by my father.

Top sellers so far this year (adding together all editions, including Kindle):
1) The Leader’s Handbook
2) Statistics for Experimenters
3) New Economics
4) Abolishing Performance Appraisals
5) The Team Handbook
6) Out of the Crisis

The Leader’s Handbook is far away in the lead. The order of popularity on Amazon overall: 1) Out of the Crisis, 2) New Economics, 3) The Team Handbook, 4) Abolishing Performance Appraisals, 5) Statistics for Experimenters and 6) The Leader’s Handbook. The only thing that surprises me with the overall numbers is the Leader’s Handbook. The Amazon rankings are hugely biased by recent activity (it isn’t close to a ranking of sales this year). Still I expected the Leader’s Handbook would rank very well. It is the first book I recommend for almost any situation (the only exceptions are if there is a very specific need – for example Statistics for Experimenters for multi-factorial designed experiments or The Improvement Guide for working on the process of improvement.

My guess is Curious Cat site users (and I am sure a fair number of people sent by search engines) are much more likely to buy those books I recommend over and over. Still many books I don’t promote are bought and some books I recommend consistently don’t rack up many sales through Curious Cat.

I started this as a simple Google+ update but then found it interesting enough to expand to a full post. Hopefully others find it interesting also.

Related: Using Books to Ignite Improvement – Workplace Management by Taiichi Ohno – Problems with Management and Business Books – Management Improvement Books (2005)

One factor at a time (OFAT) Versus Factorial Designs

Posted on May 25, 2011  Comments (2)

Guest post by Bradley Jones

Almost a hundred years ago R. A. Fisher‘s boss published an article espousing OFAT (one factor at a time). Fisher responded with an article of his own laying out his justification for factorial design. I admire the courage it took to contradict his boss in print!

Fisher’s argument was mainly about efficiency – that you could learn as much about many factors as you learned about one in the same number of trials. Saving money and effort is a powerful and positive motivator.

The most common argument I read against OFAT these days has to do with inability to detect interactions and the possibility of finding suboptimal factor settings at the end of the investigation. I admit to using these arguments myself in print.

I don’t think these arguments are as effective as Fisher’s original argument.

To play the devil’s advocate for a moment consider this thought experiment. You have to climb a hill that runs on a line going from southwest to northeast but you are only allowed to make steps that are due north or south or due east or west. Though you will have to make many zig zags you will eventually make it to the top. If you noted your altitude at each step, you would have enough data to fit a response surface.

Obviously this approach is very inefficient but it is not impossible. Don’t mistake my intent here. I am definitely not an advocate of OFAT. Rather I would like to find more convincing arguments to persuade experimenters to move to multi-factor design.

Related: The Purpose of Factorial Designed Experiments – Using Design of Experiments – articles by R.A. Fisher – articles on using factorial design of experiments – Does good experimental design require changing only one factor at a time (OFAT)? – Statistics for Experimenters

Factorial Designed Experiment Aim

Posted on April 25, 2011  Comments (2)

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks?

When doing a DOE we select factors with levels to induce purposely changes in the response variable. Do we want the response variable to move within the specs of the customers? Or it doesn’t matter since we are learning about the process?

The aim needs to consider what you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.
Continue reading →

How to Manage What You Can’t Measure

Posted on August 4, 2010  Comments (7)

In Out of the Crisis, page 121, Dr. Deming wrote:

the most important figures that one needs for management are unknown or unknowable (Lloyd S. Nelson, director of statistical methods for the Nashua corporation), but successful management must nevertheless take account of them.

So what do you do then? I am a strong advocate of Deming’s ideas on management. I see understanding system thinking, psychology, the theory of knowledge and variation as the tools to use when you can’t get precise measures (or when you can).

Even if you can’t measure exactly what you want, you can learn about the area with related data. You are not able to measure the exact benefit of a happy customer but you can get measures that give you evidence of the value and even magnitude. And you can get measures of the costs of dis-satisfied customers. I just mention this to be clear getting data is very useful and most organizations need to focus on gathering sensible data and using it well.

Without precise measure though you have to use judgment. Judgment will often be better with an understanding of theory and repeated attempts to test those theories and learn. Understanding variation can be used even if you don’t have control charts and data. Over-reaction to special causes is very common. Even without data, this idea can be used to guide your thoughts.

The danger is that we mistake measures for the thing itself. Measures are a proxy and we need to understand the limitation of the data we use. The main point Deming was making was we can’t just pretend the data we have tells us everything we need to know. We need to think. We need to understand that the data is useful but the limitations need to be remembered.

Human systems involve people. To manage human systems you need to learn about psychology. Paying attention to what research can show about motivation, fear, trust, etc. is important and valuable. It aids management decisions when you can’t get the exact data that you would like. If people are unhappy you can see it. You may also be able to measure aspects of this (increased sick leave, increased turnover…). If people are unhappy they often will not be as pleasant to interact with as people who are happy. You can make judgments about the problems created by internal systems that rob people of joy in work and prevent them from helping customers.

For me the key is to use the Deming’s management system to guide action when you can’t get clear data. We should keep trying to find measures that will help. In my experience even though there are many instances where we can get definite data on exactly what we want we fail to get data that would help guide actions a great deal). Then we need to understand the limitations of the data we can gather. And then we need to continually improve and continually learn.

When you have clear data, Deming’s ideas are also valuable. But when the data is lacking it is even more important to take a systemic approach to making management decisions. Falling back into using the numbers you can get to drive decision making is a recipe for trouble.

LinkedIn discussion on the topic

Related: Manage what you can’t measure – Statistical Engineering Links Statistical Thinking, Methods and Tools – outcome measures

Actionable Metrics

Posted on July 22, 2010  Comments (1)

Metrics are valuable when they are actionable. Think about what will be done if certain results are shown by the data. If you can’t think of actions you would take, it may be that metric is not worth tracking.

Metrics should be operationally defined so that the data is collected properly. Without operationally definitions data collected by more than one person will often include measurement error (in this case, the resulting data showing the results of different people measuring different things but calling the result the same thing).

And without operational definitions those using the resulting data may well mis-interpret what it is saying. Often data is presented without an operational definition and people think the data is saying something that it is not. I find most often when people say statistics lie it is really that they made an incorrect assumption about what the data said – which most often was because they didn’t understand the operational definition of the data. Data can’t lie. People can. And people can intentionally mislead with data. But far more often people unintentionally mislead with data that is misunderstood (often this is due to failure to operationally define the data).

In response to: Metrics Manifesto: Raising the Standard for Metrics

Related: Outcome Measures – Evidence-based Management – Metrics and Software Development – Distorting the System (due to misunderstanding metrics) – Manage what you can’t measure

Statistical Engineering Links Statistical Thinking, Methods and Tools

Posted on June 3, 2010  Comments (0)

In Closing the Gap Roger W. Hoerl and Ronald D. Snee lay out a sensible case for focusing on statistical engineering.

We’re not suggesting that society no longer needs research in new statistical techniques for improvement; it does. The balance needed at this time, however, is perhaps 80% for statistics as an engineering discipline and 20% for statistics as a pure science.

True, though I would put the balance more like 95% engineering, 5% science.

There is a good discussion on LinkedIn:

Davis Balestracci: Unfortunately, we snubbed our noses at the Six Sigma movement…and got our lunch eaten. Ron Snee has been developing this message for the last 20 years (I developed it in four years’ worth of monthly columns for Quality Digest from 2005-2008). BUT…as long as people have a computer, color printer, and a package that does trend lines, academic arguments won’t “convert” anybody.
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Recently, we’ve lost our way and evolved into developing “better jackhammers to drive tacks”…and pining for the “good ol’ days” when people listened to us (which they were forced to do because they didn’t have computers, and statistical packages were clunky). Folks, we’d better watch it…or we’re moribund

Was there really a good old days when business listened to statisticians? Of course occasionally they did, but “good old days”? Here is a report from 1986 the theme of which seems to me to be basically how to get statisticians listened to by the people that make the important decisions: The Next 25 Years in Statistics, by Bill Hunter and William Hill. Maybe I do the report a disservice with my understanding of the basic message, but it seems to me to be how to make sure the important contributions of applied statisticians actually get applied in organizations. And it discusses how statisticians need to take action to drive adoption of the ideas because currently (1986) they are too marginalized (not listened to when they should be contributing) in most organizations.
Continue reading →

Taxes per Person by Country

Posted on April 7, 2010  Comments (0)

I think that the idea that data lies is false, and that such a notion is commonly held a sign of lazy intellect. You can present data in different ways to focus on different aspects of a system. And you can make faulty assumptions based on data you look at.

It is true someone can just provide false data, that is an issue you have to consider when drawing conclusions from data. But often people just don’t think about what the data is really saying. Most often when people say data lies they just were misled because they didn’t think about what the data actually showed. When you examine data provided by someone else you need to make sure you understand what it is actually saying and if they are trying to support their position you may be wise to be clear they are not misleading you with their presentation of the data.

Here is some data from Greg Mankiw’s Blog. He wants to make his point that the USA is taxed more on par with Europe than some believe because he want to reduce current taxes. So he shows that while taxes as a percent of economic activity is low in the USA taxes per person is comparable to Europe.

Taxes/GDP x GDP/Person = Taxes/Person

France .461 x 33,744 = $15,556

Germany .406 x 34,219 = $13,893

UK .390 x 35,165 = $13,714

US .282 x 46,443 = $13,097

Canada .334 x 38,290 = $12,789

Italy .426 x 29,290 = $12,478

Spain .373 x 29,527 = $11,014

Japan .274 x 32,817 = $8,992

The USA is the 2nd lowest for percent of GDP taxes 28.2% v 27.4% for Japan. But in taxes per person toward the middle of the pack. France which has 46% taxes/GDP totals $15,556 in tax per person compared to $13,097 for the USA. Both measures of taxes are useful to know, in my opinion. Neither lies. Both have merit in providing a understanding of the system (the economies of countries).

Related: Fooled by Randomness – Simpson’s Paradox – Mistakes in Experimental Design and Interpretation – Government Debt as Percentage of GDP 1990-2008 by Country – Communicating with the Visual Display of Data – Illusion of Explanatory Depth

Soren Bisgaard

Posted on December 22, 2009  Comments (1)

photo of Soren Bisgaard

Soren Bisgaard died earlier this month of cancer. Soren was a student of my father’s who shared the commitment to making a difference in people’s lives by using applied statistics properly. I know this seem odd to many (I tried to describe this idea previously, also read his acceptance of the 2002 William G. Hunter award). Soren served as the director of the director of the Center for Quality and Productivity Improvement at the University of Wisconsin-Madison (founded by William Hunter and George Box) for several years.

Most recently Soren Bisgaard, Ph.D. was Professor of technology management at Eugene M. Isenberg School of Management at the University of Massachusetts – Amherst. He was an ASQ Fellow; recipient of Shewart Medal, Hunter Award, George Box Medal, among many others awards.

I will remember the passion he brought to his work. He reminded me of my father in his desire to improve how things are done and allow people to have better lives. Those that bring passion to their work in management improvement are unsung heroes. It seems odd, to many, to see that you can bring improvement to people’s lives through work. But we spend huge amounts of our time at work. And by improving the systems we work in we can improve people’s lives. Soren will be missed, by those who knew him and those who didn’t (even if they never realize it).

Contributions in honor of Søren may be made to The International Mesothelioma Program or to the European Network for Business and Industrial Statistics. Read more articles by Søren Bisgaard.

The Future of Quality Technology: From a Manufacturing to a Knowledge Economy and From Defects to Innovations (pdf) by Soren Bisgaard

Related: The Work of Peter Scholtes – Management Improvement Leaders – The Scientific Context of Quality Improvement by George Box and Soren Bisgaard, 1987 – Obituary Søren Bisgaard at ENBIS – Obituary: Soren Bisgaard, Isenberg Professor in Integrative Studies

Highlights from Recent George Box Speech

Posted on November 16, 2009  Comments (4)

The JMP blog has posted some highlights from George Box’s presentation at Discovery 2009

Infusing his entire presentation with humor and fascinating tales of his memories, Box focused on sequential design of experiments. He attributed much of what he knows about DOE [design of experiments] to Ronald A. Fisher. Box explained that Fisher couldn’t find the things he was looking for in his data, “and he was right. Even if he had had the fastest available computer, he’d still be right,” said Box. Therefore, Fisher figured out how to study a number of factors at one time. And so, the beginnings of DOE.

Having worked and studied with many other famous statisticians and analytic thinkers, Box did not hesitate to share his characterizations of them. He told a story about Dr. Bill Hunter and how he required his students to run an experiment. Apparently a variety of subjects was studied [see 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments]
…
According to Box, the difficulty of getting DOE to take root lies in the fact that these mathematicians “can’t really get the fact that it’s not about proving a theorem, it’s about being curious about things. There aren’t enough people who will apply [DOE] as a way of finding things out. But maybe with JMP, things will change that way.”

George Box is a great mind and great person who I have had the privilege of knowing my whole life. My father took his class at Princeton, then followed George to the University of Wisconsin-Madison (where Dr. Box founded the statistics department and Dad received the first PhD). They worked together building the UW statistics department, writing Statistics for Experimenters and founding the Center for Quality and Productivity Improvement among many other things.

Statistics for Experimenters: Design, Innovation, and Discovery shows that the goal of design of experiments is to learn and refine your experiment based on the knowledge you gain and experiment again. It is a process of discovery. If done properly it is very similar to the PDSA cycle with the application of statistical tools to aid in determining the impact of various factors under study.

Related: Box on Quality – George Box Quotations – posts on design of experiments – Using Design of Experiments

Statistical Learning as the Ultimate Agile Development Tool by Peter Norvig

Posted on November 5, 2009  Comments (1)

Interesting lecture on Statistical Learning as the Ultimate Agile Development Tool by Peter Norvig. The webcast is likely to be of interest to a fairly small segment of readers of this blog. But for geeks it may be interesting. He looks at the advantages of machine learning versus hand programming every case (for example spelling correction).

Google translate does a very good job (for computer based translation) based on machine learning. You can translate any of the pages on this blog into over 30 languages using Google translate (using the widget in the right column).

Via: @seanstickle

Related: Mistakes in Experimental Design and Interpretation – Does the Data Deluge Make the Scientific Method Obsolete? – Website Data – An Introduction to Deming’s Management Ideas by Peter Scholtes (webcast)

Communicating with the Visual Display of Data

Posted on October 26, 2009  Comments (1)

graphs showing data sets with different looks even though some statistical characteristics are the same
Anscombe’s quartet: all four sets are identical when examined statistically, but vary considerably when graphed. Image via Wikipedia.

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Anscombe’s quartet comprises four datasets that have identical simple statistical properties, yet are revealed to be very different when inspected graphically. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician F.J. Anscombe to demonstrate the importance of graphing data before analyzing it, and of the effect of outliers on the statistical properties of a dataset.

Of course we also have to be careful of drawing incorrect conclusions from visual displays.

For all four datasets:

Property Value
Mean of each x variable 9.0
Variance of each x variable 10.0
Mean of each y variable 7.5
Variance of each y variable 3.75
Correlation between each x and y variable 0.816
Linear regression line y = 3 + 0.5x

Edward Tufte uses the quartet to emphasize the importance of looking at one’s data before analyzing it in the first page of the first chapter of his book, The Visual Display of Quantitative Information.

Related: Great Charts – Simpson’s Paradox – Seeing Patterns Where None Exists – Visible Data – Control Charts – Edward Tufte’s: Beautiful Evidence

YouTube Uses Multivariate Experiment To Improve Sign-ups 15%

Posted on August 17, 2009  Comments (2)

Google does a great job of using statistical and engineering principles to improve. It is amazing how slow we are to adopt new ideas but because we are it provides big advantages to companies like Google that use concepts like design of experiments, experimenting quickly and often… while others don’t. Look Inside a 1,024 Recipe Multivariate Experiment

A few weeks ago, we ran one of the largest multivariate experiments ever: a 1,024 recipe experiment on 100% of our US-English homepage. Utilizing Google Website Optimizer, we made small changes to three sections on our homepage (see below), with the goal of increasing the number of people who signed up for an account. The results were impressive: the new page performed 15.7% better than the original, resulting in thousands more sign-ups and personalized views to the homepage every day.
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While we could have hypothesized which elements result in greater conversions (for example, the color red is more eye-catching), multivariate testing reveals and proves the combinatorial impact of different configurations. Running tests like this also help guide our design process: instead of relying on our own ideas and intuition, you have a big part in steering us in the right direction. In fact, we plan on incorporating many of these elements in future evolutions of our homepage.

via: @hexawise – My brother has created a software application to provide much better test coverage with far fewer tests using the same factorial designed experiments ideas my father worked with decades ago (and yet still far to few people use).

Related: Combinatorial Testing for Software – Statistics for Experimenters – Google’s Website Optimizer allows for multivariate testing of your website. – Using Design of Experiments

Revealed Preference

Posted on May 16, 2009  Comments (1)

Revealed Preference: the preference consumers display by their action, in contrast to what they may say they prefer. While surveys may be useful people often say they will do one thing and actually when given the choice to do so, don’t.

Normally what matters is not what people say they want but what they actually will choose. For that reason revealed preference is a better measure than stated preference. Stated preference is often used as a proxy for actual preference (which may be fine) but it is important to understand that it is just a proxy for actual preference.

See more explanations from the Curious Cat Management Dictionary.

Related: Packaging Improvement – All Models Are Wrong But Some Are Useful – Dangers of Forgetting the Proxy Nature of Data – Confirmation Bias – Be Careful What You Measure

How to Create a Control Chart for Seasonal or Trending Data

Posted on March 25, 2009  Comments (1)

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.

Related: Control Charts in Health Care – Common Cause Variation – Managing with Control Charts – Measurement and Data Collection – Fourth Generation Management

Friday Fun: Correlation

Posted on March 6, 2009  Comments (0)

Correlation doesn't imply causation

From the excellent xkcd comic.

Related: Correlation is Not Causation – Does the Data Deluge Make the Scientific Method Obsolete? – Understanding Data – Theory of Knowledge – What Makes Scientists Different :-) – Dangers of Forgetting the Proxy Nature of Data – Seeing Patterns Where None Exists

Statistics for Experimenters in Spanish

Posted on January 5, 2009  Comments (0)

book cover of Estadística para Investigadores

Statistics for Experimenters, second edition, by George E. P. Box, J. Stuart Hunter and William G. Hunter (my father) is now available in Spanish.

Read a bit more can find a bit more on the Spanish edition, in Spanish. Estadística para Investigadores Diseño, innovación y descubrimiento Segunda edición.

Statistics for Experimenters – Second Edition:

Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.
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* 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

Related: Statistics for Experimenters Review – Correlation is Not Causation – Statistics for Experimenters Data – posts on design of experiments

ASQ William Hunter Award 2008: Ronald Does

Posted on November 12, 2008  Comments (0)

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:

The first advice I received from my new colleagues was to read the book by Box, Hunter and Hunter. The reason was clear. Because I was not familiar with industrial statistics I had to learn this from the authors who were really practicing statisticians. It took them years to write this landmark book.
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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.
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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.

Related: 2007 William G. Hunter Award – The Importance of Management Improvement – Resources on using statistical thinking to improve management

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