Pyzdek Institute Black Belt’s Book Clarifies Confusing Concepts in Statistics

In preparation for the Black Belt certification exam, he started writing his own explanations, augmented by diagrams and pictures. These helped him ace the exam (along with our training, of course!)

andy-jawlik-book-front-cover-jpgI’ve taught the Six Sigma process quality methods to many students over the years. Invariably, it’s the statistics that confuses people the most. There has been a real need for a book that clears up this confusion. Now, a graduate of the Pyzdek Institute Black Belt program has done something about that.

Andrew A. Jawlik has written a book Statistics from A to Z: Confusing Concepts Clarified, published by Wiley.

Despite having an MS in Math, Andy – like many other intelligent, technical people – found the statistics in most college statistics courses to be difficult and confusing. And existing statistics books weren’t much help. In preparation for the Black Belt certification exam, he started writing his own explanations, augmented by diagrams and pictures. These helped him ace the exam (along with our training, of course!)

He showed some of this work to me and asked if I thought he should pursue writing a book. I encouraged him to do so. Two and a half years later, he completed a 418-page book.

The book is alphabetically arranged, like a mini-encyclopedia, comprised of 74 succinct articles on more than 60 statistical concepts. Each article starts with a one-page list of about 5 “Keys to Understanding”, so that you can see everything you need to know on a single page. Subsequent pages in the article provide expanded explanations of these Keys. There is liberal use of graphics like concept flow diagrams, compare-and-contrast tables, and even cartoons to provide visual reinforcement of the concepts and how they interrelate.

You can see excepts from the book on the website at the link below. There is also a blog on the website in which Andy publishes a Statistics Tip of the Week. And he has just begun uploading videos to his YouTube channel about individual statistical concepts in the book.

Useful Links

– book website: statisticsfromatoz.com

– blog: statisticsfromatoz.com/blog

– YouTube channel: http://bit.ly/2dD5H5f

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DMAMIC – A New Way to Teach Six Sigma

DMAMIC

In the beginning of Six Sigma there was MAIC: Measure-Analyze-Improve-Control. Then General Electric added Define. And that’s where things have been for 25+ years. Six Sigma is DMAIC. There’s just one problem: it doesn’t work very well for teaching purposes. DMAIC as a framework for executing Six Sigma projects works just fine. I’ve led hundreds of DMAIC projects and reviewed thousands more for certification and other purposes. But teaching Six Sigma in the DMAIC sequence is problematic and should not be done.

The Two-Measure-Phase Teaching Model

When I teach Six Sigma I break the Measure phase in to two distinct sets of lessons.

Measure1 – Descriptive Measurements for Characterizing Processes

The first set of Measure topics is largely non-technical. I teach students how to understand and interpret variation, what measurements are, operational definitions, scales of measurement, etc.. I also teach them how to sample and collect data and use it to measure the current baseline and to perform exploratory data analysis to let data talk to them. I also teach the use of simple descriptive statistical methods to display properties of data distributions such as central tendency, spread and shape. I show students how to use existing and newly collected data to drill down from higher levels of cause-and-effect to more granular levels by using stratification. I teach about the pros and cons of using historical data.

These Measure tools help students to develop theories and do some preliminary work in checking their theories with facts and data. They are mainly graphical: run charts, histograms, boxplots and the like. Some statistics are used to quantify otherwise vague concepts such as “center” and “spread.” I view the first phase of Measure training as something of an extension of the Define phase. However, where Define focuses on the project, M1 concerns itself with the process. It is where the team puts the project definition to the test by using data. The deliverable from M1 is a more detailed process definition and a rough model of how the process creates the outcome which the project aims to improve.

Measure2 – Measurement Systems Analysis

Measurement Systems Analysis (MSA) is the rigorous evaluation of measurement systems. When an MSA is completed we will be able to categorically answer the question: can the measurement system adequately characterize the process on which it is being used? In the case of Green Belts this question is answered using control charts and their associated statistics. Black Belt training goes deeper and partitions the variation into variation in the process and variation from other sources, such as different inspectors and repeated readings.

In both the Green Belt and Black Belt cases the MSA is, in essence, a designed experiment. Even though Green Belt training doesn’t cover the statistical analysis of designed experiments, it does cover the proper design of experiments. It also covers how to properly control experiments to assure that the results are not contaminated with variation from unintended sources. Now, the proper design and control of experiments are properly taught during the Analyze phase. This includes teaching things such as special and common cause variation, tests of hypotheses, partitioning of variance, statistical modeling, p-tests, statistical error, and much more.

In short, from a teaching perspective M2 training needs to come after the student has learned the topics taught during the Analyze phase.

Advantages to DMAMIC

When the traditional DMAIC approach to teaching Six Sigma is used, all of the Measure topics are presented immediately after the Define topics. When this approach is used instructors must cover many topics from the Analyze phase. This leads to confusion in the minds of students between the two phases of Six Sigma. Separating Measure into M1 (which uses simple descriptive and graphical tools to explore existing data) and M2 (which uses designed experiments to rigorously characterize measurement systems) allows the instructor to lay the groundwork needed to understand what is being presented. When training is complete and projects are actually being done, the Green Belt or Black Belt should use the traditional DMAIC approach. But the DMAMIC approach should be used to teach.

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Six Sigma Black Belt Information from Payscale.com

The majority of Six Sigma Black Belt Project Managers claim high levels of job satisfaction.

Six Sigma Black Belt (SSBB) project managers are highly specialized business professionals who have been trained and certified in the most effective techniques in the business world. Six Sigma is an education program that originated in 1985; this program develops individuals and gives them strategies and cognitive tools to improves processes at their company. This means that SSBB project managers are experts at identifying problems or issues in business processes and providing solutions to these problems. Usually, these types of individuals are employed in manufacturing, production or business settings. On a day to day basis, they are assigned to and oversee a particular process of a given company with the goal of minimizing variability and maximizing efficiency and workflow. Individuals who train with Six Sigma are also taught expert techniques in business communication and the administration of discipline and motivation.

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Continuous Improvement Myths

There seems to be a lot of mystery associated with how companies that make continuous quality improvement work actually do it. In his article Ten Common Misconceptions About Toyota Stewart Anderson sets the record straight about how Toyota actually works its magic. The gist of the article is that Toyota doesn’t follow some rigid set of rules mindlessly. They have a small number of guiding principles to guide them, and they apply them as consistently as is practical to do so

Toyota’s basic pattern for improving a process is based on a simple three-part model:

  1. Understanding the current condition.
  2. Developing and defining a target condition.
  3. Understanding and tackling problems which need to be overcome to move from the current condition to the target condition.

In other words, go from the staus-quo to a new state that is better. All of the hubub about Six Sigma, Lean, or any of the other approaches advanced during the past three-quarters of a century or so is nothing more than the attempt of managers who don’t really understand that this is all there is to it. The reason, I believe, is that our managers are taught by business school professors who are, philosophically, pragmatists. And pragmatism is all about eschewing guiding principles. In fact, they don’t even think there is such a thing as a principle. Pragmatists just want to know what works, not why it works.

This may sound like a good approach at first glance, but it’s a blind alley in the long run. For pragmatists — knowledge of the world is impossible to separate from actions upon it. There is no reality out there — both facts and values are products of men interacting with an environment and shaping it to their wills. If there are no facts per se, then what is the use of principles which are intended to guide action long-term? The goal of thought is merely to reconstruct the situation in order to solve the problem. If the proposal, when implemented, resolves the issue, then the idea is pragmatically true. Truth cannot be known in advance of action. One must first act and then think. Only then can reality be determined.

But there is a reality, and there are principles. Engineers and scientists — and Toyota — know that discovering what makes things tick is the only way to truly know how to make them better. This is the way to continuously improve.


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Minitab’s Quality Companion 3 Worth a Closer Look

One of the big problems facing many new Six Sigma Green Belts and Six Sigma Black Belts is staying organized when dealing with the complexity of their projects. Experienced Six Sigma Belts face a different problem: when several projects are in work simultaneously keeping track of everything can quickly become unmanageable. Minitab’s Quality Companion 3 (QC3) software, and its associated Quality Companion Dashboard, provide an excellent way of dealing with these issues.

Unlike Minitab, QC3 provides a relatively sparse toolset for performing statistical analysis. That’s not the point of QC3. Instead, the tools included within QC3 are intended to be used for helping organize data, reports, and forms within the project. Examples: project charter forms, value stream maps, flowcharts, C&E diagrams, C&E matrices, Gantt charts, etc.. Dozens of forms are included, and you can add your own custom forms. Other reasons to give Quality Companion a try:

  • Cross-populate forms with redundant data. (Xs and Ys show up in several places.)
  • Calculates simple statistics, such as takt time.
  • Make simple presentations.
  • Allows creation of custom templates.
  • Keep project data in a single place.
  • Basic scheduling tools (Gantt, Milestone.)
  • Track portfolios of projects. Not sure it can handle an enterprise, but perfectly serviceable for a Belt or a MBB managing several Belts.
  • Free trial. Free webinars. Several licensing options.
  • Integrates with Minitab. Allows importing from and exporting to viso, excel, powerpoint, and other commonly used software.

Disclaimer: The Pyzdek Institute provides all of its Green Belt and Black Belt students with one year licenses to QC3 as well as Minitab. The license is included with the training fee.

 


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Confronting Data

What is data and why do we care so much about it?

What are data, and why do people care so much about data? Assuming the data are from a ratio or interval scale (scale data,) data are merely numbers attached to things we are interested in. Normally we have no interest in the numbers per se. We are interested in making better decisions or choices, which is facilitated if we have more knowledge. The data are studied in an attempt to acquire this knowledge. The path from data to knowledge isn’t direct. Sometimes we begin with a decision we must make. Should we train our employees? Adjust the machine? Buy this car or that one? If the decisions are obvious and we already know what we need to know, we don’t need to study data and we make the decision directly. In other cases data may help us learn and make it more likely that we’ll make a better decision. At other times we are putting a more general question to the data: how can I improve this process? Or, how can I improve quality?

In both situations we are asking the data to help us formulate testable hypotheses. A reasonable question is “Why can’t I just get my hypotheses directly from the data?” The answer lies in the limits of human cognition. While it’s true that the raw data do contain all the information, we simply don’t have the mental hardware necessary to extract it. It’s too complex. Humans can hold perhaps 7 to 10 pieces of information in their minds at one time. A data set of tens, hundreds or thousands of numbers simply overwhelmes our mental mushware.

Exploring

First we obtain the data, either by collecting it ourselves or by getting it from some data source. Then we

  • QE 21, 4, 369 upper right paragraph. “In our model, hypotheses do not originate in the data, the originate in the confrontation between the data and the mental models that the inquirer entertains.”
  • Raw data are too complex for human brains.
  • Tables of statistical aggregates lose information that may be vital for EDA. Graphs and pictures are better.
  • Salient feature: look for the unexpected. Unexpected implies some reference expectation/distribution.
    • Once found, the salient feature can become the new expectation and we look for departures within it.
    • Uniform and normal are common reference statistical distributions, but there are others too (e.g., exponential for time data.)
    • Assumptions (independence, randomness, etc.) are also reference distributions.
  • Data “reveal” what we look for (or expect,) graphs reveal the unexpected.
  • Surprise is important (me.)
  • Hypotheses are not derived from facts, but invented to account for them.
  • What if you see the patterns, but still don’t have a clue?
    • Data torture (continue analyzing the data in the hope something pops out.)
    • Learn more (study the subject, go and look, collect additional data, etc.)
    • Cue acquisition.
  • EDA doesn’t prove anything. It’s retrospective (you need prospective) and data based (you need science.) Don’t use EDA for the wrong thing.
  • Classical SHIT is a hindrance to EDA and to learning.
  • Blind to the obvious (EDA often leads to insights that in retrospect are trivially obvious.) EDA can force the mind to see the obvious.
  • The bucket principle: putting the data into buckets, then comparing the buckets. Display data so you can Compare the between bucket variation to the within bucket variation, e.g., boxplots.
    • Time is a bucket variable.
    • Show individual data dots on x-bar charts to make within-group distributions obvious. (Or use boxplots with control limits?)

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