2^3 Design
 Mathews Malnar and Bailey, Inc.

Quality engineering, applied statistical consulting,
and training services for R&D, product, process,
and manufacturing engineering organizations.		 Run Chart

Meeting announcements are in reverse order with the most recent meeting at the top. All meetings are free and everyone is welcome to present or to recommend a topic. Lakeland also hosts an ISO/QS/Baldrige network. Please e-mail Tammy Bailey at tbailey@lakelandcc.edu to be added to the mailing list.

Use these meetings to earn recertification units (RUs) for your ASQ certifications!

Rectifying Inspection Using the Dodge-Romig Method, Paul Mathews, 7:30-9:00 AM, 9 January 2008, Room T136.
At our last meetings we've discussed the attributes sampling standard ANSI/ASQ Z1.4 (formerly MIL-STD-105) and the variables sampling standard ANSI/ASQ Z1.9 (formerly MIL-STD-414). At the January 9th meeting as continuation of this topic we'll discuss the Dodge-Romig rectifying inspection method. In rectifying inspection a lot that is rejected in sample inspection is 100% inspected and all of the defective parts are replaced with good ones. Where the Z1.4 and Z1.9 standards are designed to have a high probability of passing lots with specified acceptable quality level (AQL), the Dodge-Romig plans are designed specifically to control the post-inspection rejectable quality level (RQL).


The ANSI/ASQ Z1.9 Acceptance Sampling Standard for Variables Data, Paul Mathews, 7:30-9:00AM, 5 December 2008, Room T136.
At last month's meeting we discussed the use and performance of two well known attribute acceptance sampling standards: ANSI/ASQ Z1.4 (formerly MIL-STD-105) and Squeglia's Zero Acceptance Number Sampling Plans. This month we'll extend the discussion to the lesser known and unfortunately under-used ANSI/ASQ Z1.9 variables sampling standard.


Evaluating Sampling Plans from ANSI/ASQ Z1.4 (Formerly MIL-STD-105) Using Operating Characteristic Curves, Paul Mathews, 7:30-9:00AM, 31 October 2008, Room E116.
ANSI/ASQ Z1.4 is the best known and most popular sampling standard for attributes. Paul Mathews will review the use of the standard including the switching rules between normal, tightened, and reduced inspection and he will discuss the use of operating characteristic (OC) curves to evaluate and compare sampling plans.

Paul Mathews will be teaching his new course Sample Size Calculations for Process Improvement at Lakeland from 8:00AM to 12:00PM on November 7, 14, 21, and December 5 in Room T136. The cost will be $399 (textbook included). Details for this course are here. Contact Paul at paul@mmbstatistical.com if you have any questions.


Comparing the Sample Sizes Required for Attribute and Variables Inspections, Paul Mathews, 7:30-9:00AM, 3 October 2008, Room T136.
A common mistake that quality technicians and engineers often make is to use an attribute (i.e. pass/fail) inspection when a suitable variables or measurements inspection is available. While the individual attribute inspections might be very fast to perform (such as with a snap gage), the number of observations required is usually prohibitively large compared to what a variables plan requires. Paul Mathews will show how to evaluate the trade off between the attribute and variables inspection methods using a simple calculation for the ratio of the two plans' sample sizes.

The next Quality Managers Network meeting will be held on 31 October 2008 in Room E116. (Note the room change!) Paul Mathews will discuss the use of operating characteristic (OC) curves to compare attribute inspection sampling plans and then use OC curves to evaluate plans from the ANSI/ASQ Z1.4 (formerly MIL-STD-105) sampling standard.

Paul Mathews will be teaching his new course Sample Size Calculations for Process Improvement at Lakeland from 8:00AM to 12:00PM on November 7, 14, 21, and December 5 in Room T136. The cost will be $399 (textbook included). Details for this course are here. Contact Paul at paul@mmbstatistical.com if you have any questions.


An Introduction to Sample Size Calculations II, Paul Mathews, 29 August 2008, 7:30-9:00AM, T136.
At the last meeting we discussed sample size calculations for confidence intervals and we looked at several examples including the sample sizes required to estimate: a small proportion (e.g. a small fraction defective), a moderate proportion (e.g. the fraction of the votes that a candidate will receive), a population mean, and the cpk process capability parameter. In this second session on the topic, Paul Mathews will show how to calculate sample sizes for hypothesis tests for simple one- and two-sample tests for proportions and means. He'll also demonstrate the use of Russ Lenth's free power and sample size calculation program called piface (http://www.stat.uiowa.edu/~rlenth/Power/).

Paul will be teaching his new course Sample Size Calculations for Process Improvement at Lakeland beginning some time in September. The class will meet for four four-hour sessions (16 hours total) and the cost will be $399 (textbook included). Details for this course are here. Class dates have yet to be determined, but if you have a preference please let Paul know at paul@mmbstatistical.com.


An Introduction to Sample Size Calculations I, Paul Mathews, 1 August 2008, 7:30-9:00AM, T136.
Every opportunity to collect data raises the issue of how much data are necessary. Collecting too few data might cause you to miss an important opportunity and collecting too many data wastes time and resources. Paul Mathews will explain how to right-size your data collection activities by calculating sample sizes that are consistent with the goals of those activities.


Managing Missing Data in Experiments, Paul Mathews, 27 June 2008, 7:30-9:00AM, T136.
When each level of a variable in an experiment has the same number of observations, the experiment is said to be balanced. Balanced experiments have desireable properties but when an experiment becomes unbalanced because of missing observations these properties are compromised - sometimes seriously. Paul Mathews will describe the consequences of unbalanced designs and suggest some strategies for managing missing data.


Arc Lamp Dose Development Using Mixture Designs, Tom Coffey, 30 May 2008, 7:30-9:00AM, T136.
Mixture designs are a special family of designed experiments used to determine the correct proportions of the components in a multi-component blend. Tom Coffey will describe the use of a mixture design by GE Lighting to determine the dose composition for a ceramic metal halide (CMH) arc lamp. CMH lamps offer high efficiency, excellent color, and long life and are becoming increasingly important as pressure grows to decrease commercial and residential energy consumption.


A Tour of a Dimensional Metrology Calibration Lab, Keith Kokal, 25 April 2008, 8:00-9:30AM, at Micro Laboratories, Inc., 7158 Industrial Park Blvd., Mentor, OH.
At our March meeting we discussed calibration uncertainty statements so it seems fitting that this month we should go see how calibrations are done. Keith Kokal is President of Micro Laboratories, Inc. located in Mentor, Ohio. Micro Laboratories is an ISO 17025 A2LA-accredited dimensional metrology calibration supplier. They can also do torque calibrations and are expanding their services to provide calibrations for pressure gages and electrical instruments. Keith and his staff will give us a tour of their lab which uses instruments and procedures similar to those used by NIST. Some of the lab areas have very tight heat and humidity tolerances so this tour is limited to 15 people and will be necessarily broken into small groups. If you'd like to come please RSVP by e-mail to Paul at paul@mmbstatistical.com. Note that this tour starts at 8:00AM and please park on the side of the building. Coffee and pastry will be provided.


Measurement Uncertainty in Calibration, Paul Mathews, 28 March 2008, 7:30-9:00AM, T136.
Measurement accuracy is established by calibration, but even the best calibration still contains errors (hopefully small ones) from many different sources. The combined effect of these potential calibration errors is called the measurement uncertainty. Paul Mathews will present an introduction to measurement uncertainty analysis as defined in ISO's Guide to Uncertainty in Measurement (GUM) and describe how measurement uncertainty analysis relates to measurement systems analysis (MSA) and gage error (GR&R) studies.


Resampling Statistics III, Paul Mathews, 29 February 2008, 7:30-9:00AM, T136.
Paul Mathews will continue with the demonstration of the free resampling software Statistics101 (www.statistics101.net) which reproduces many of the features of the commercial package Resampling Stats. In this presentation Paul will show how to construct bias-corrected confidence intervals for highly skewed (i.e. non-normal) data sets.


Resampling Statistics II, Paul Mathews, 25 January 2008, 7:30-9:00AM, T136.
At the last QMN meeting in November Paul Mathews presented an introduction to resampling statistics methods which provide distribution-free methods for constructing confidence intervals and performing hypothesis tests using sample data. At this second meeting on the same topic, Paul will present more detail on resampling methods and demonstrate John Grosberg's free resampling software Statistics101 (www.statistics101.net) which reproduces many of the features of the commercial package Resampling Stats.


Resampling Statistics I, Paul Mathews, 30 November 2007, 7:30-9:00AM, T136.
Classical inferential statistical methods make assumptions about the distributions of test statistics in order to calculate confidence intervals and hypothesis tests. For example, the classical methods assume normal or Student t distributions for sample means, chi-square distributions for sample variances, and F distributions for the ratios of two sample variances. When the conditions that justify these assumed distributions aren't satisfied, the classical inferential methods may fail. As a distribution-free alternative, resampling methods which rely heavily on the computer to build the sampling distributions have been developed. These methods are particularly useful for small or badly-behaved samples when the validity of the classical distributions cannot be assumed or tested. Paul Mathews will present an overview of resampling methods for constructing confidence intervals and performing hypothesis tests.


Quality management of half-normal and circular-normal distribution data, Paul Mathews 26 October 2007, 7:30-9:00AM, T136.
In engineering and manufacturing there are two special “relatives” of the normal distribution that appear frequently but are not usually recognized. These are the half-normal and circular-normal distributions.

In the half-normal case, deviations from the distribution mean are measured but the direction of these deviations is unknown so only positive values are observed. Such data result in a normal bell-shaped distribution that’s folded over about the mean, giving a double-amplitude normal curve for positive values and a completely truncated left tail for the negative values.

In the circular-normal case, the location of a feature moves about on a plane giving a two-dimensional normal distribution surface. (Picture a bell-shaped curve rotated about its mean.) Sometimes the feature’s deviations from the target location can be determined independently in the two dimensions, but in many cases only the radial distance between the observed and target locations can be determined. In the latter case, the distribution of the radial deviation data often follows a circular-normal distribution.

Paul Mathews will describe some situations that produce half-normal and circular-normal data and the corresponding methods for testing for these distributions, setting specifications, determining process yields, and setting statistical process control limits.


Are Your Data Normal?, Paul Mathews 28 Sept 2007, 7:30-9:00AM, T136
Many statistical analysis methods (e.g. SPC, acceptance sampling, GR&R studies, process capability studies, tolerancing) require that the distribution of measurement values under consideration follow the normal or bell-shaped curve. When these data don't follow the normal curve the usual methods of analysis may be incorrect. In many cases the problem can be resolved by applying a mathematical transformation (e.g. a square or square root operation) to the original measurement values but more difficult problems require special analyses. In the first part of a two-part presentation, Paul Mathews will demonstrate how to use normal probability plots and quantitative tests for normality to determine if data follow the normal distribution. He will also describe some common situations in which you can expect to use transformations. In the second installment of the two-part presentation, Paul will describe some special non-normal distributions that show up frequently in real life situations.

An Introduction to and Some Examples of Failure Mode and Effect Analysis (FMEA), Haans Petruschke, 27 July 2007, 7:30-9:00 AM, Room T136
Haans Petruscke is a Deming-trained quality engineer currently employed at Libra Industries in Mentor. At this meeting Haans will present a novice-level introduction to failure modes and effects analysis (FMEA) and several FMEA case studies. (A comment from Paul: I've seen Haans do this FMEA presentation and thought that he was a great speaker and VERY knowledgeable on this topic, among many others. Even if you're already an FMEA  expert, I think that you'll enjoy Haans' presentation and the lively discussion that I think will follow.)

Attribute GR&R Studies (III), Paul Mathews, 29 June 2007, 7:30-9:00 AM, Room T136.
At the last two network meetings Paul Mathews presented an overview of gage error studies (GR&R studies) for measurement and attribute data and methods of analyzing GR&R study data. At the last meeting the attendees took data for three GR&R studies: a measurement response study, a binary (pass/fail) response study, and an ordinal (severity index) response study. We ran out of time to analyze these data in detail, so at this month's meeting we will review the experiments that were run and analyze the data more carefully. If you have your own GR&R attribute response data that you'd like to share with the network, please forward your data to Paul at paul@mmbstatistical.com.


Attribute GR&R Studies (II), Paul Mathews, 1 June 2007, 7:30-9:00 AM, Room T136.
At the last network meeting (27 April 2007) we discussed different types of attribute gage error studies, their design, and analysis. At the next meeting (1 June 2007) we will design, collect the data for, and analyze three GR&R studies:
    1) A traditional gage error long study on a measurement response.
    2) An attribute gage error study on a binary (pass/fail) response.
    3) An attribute gage error study on an ordinal (defect severity) response.
The experimental GR&R study data will be analyzed using MINITAB.

If you have your own attribute GR&R study data that you'd like to share with the network members, please e-mail your data and a description of the situation to Paul Mathews at paul@mmbstatistical.com.


Attribute Gage Error Studies (I), Paul Mathews, 27 April 2007, 7:30 - 9:00 AM, Room T136.
Most of us are familiar with gage repeatability and reproducibility (GR&R) studies for measurement data, however, eventually everyone encounters an attribute inspection operation that must be validated with a gage error study. The design of attribute gage error studies isn't any different from the design of studies for measurement data, however, attribute GR&R studies are analyzed using different statistics and have different acceptance criteria. Paul Mathews will describe the calculation and interpretation of two common statistics used for analyzing attribute gage error studies: kappa and intraclass correlation. Then he will present examples of capable and incapable attribute gaging systems.


Interference Fit and Stack-Up Tolerancing, Paul Mathews, 30 March 2007, 7:30 - 9:00 AM, Room T136.
Paul Mathews will discuss tolerance calculations for interference fit and stack-up tolerancing problems, including methods for both normal and non-normal distributions. He will also demonstrate the use of simulation methods to analyze fictional stack-up assemblies using data from relatively small samples.


Excel Pivot Tables, Ray Tison, 2 March 2007, 7:30-9:00 AM, T136.
Pivot tables in Excel provide a powerful tool to subset and stratify your data. Ray Tison will demonstrate how to create a pivot table in Excel and how to use the pivot table to identify patterns in the data and extract summary statistics. Ray will also demonstrate how to use pivot tables in Access when your data set becomes too big for Excel.


An Introduction to Data Analysis Using R, Paul Mathews, 26 January 2007, 7:30-9:00 AM, T136.
R is free software used for graphical and statistical data analysis that can be downloaded from the Internet. Because R code is written and shared by many people around the world, it has a tremendous range of capabilities. Consequently, most graphical and statistical methods that have been implemented in any commercially-available statistical software package have been implemented in R.

Paul Mathews will present an introduction to data analysis using R including:
   * How to obtain and install R and R packages.
   * How to access limited  R functions from the R Commander GUI package.
   * How to enter your data into R.
   * How to graph your data.
   * How to perform basic statistical analyses.
   * How to analyze designed experiments.
   * How to save your work.
   * How to learn more about R.


Too Many Experiment Designs? (Part II), Paul Mathews, 1 December 2006, 7:30-9:00 AM, T136.
We had such good turnout at the 27 October meeting (about 20 people), and we didn't finish our discussion of the topic (Too Many Experiment Designs?), so we're going hold a second session on the same topic.

At the 27 October meeting, Paul Mathews described the various methods available for analyzing experiments with qualitative and quantitative responses. At the 1 December meeting, he'll review this topic by presenting examples of these methods. Then we'll procede in our discussion of types of experiment designs including qualitative and quantitative variables; crossed and nested variables; and full factorial, fractional factorial, response surface, and Taguchi orthogonal designs.


Too Many Experiment Designs?, Paul Mathews, 27 October 2006, 7:30-9:00 AM, T136.
There are so many different kinds of experiment designs available that it can be difficult to choose the right one for a particular situation. Paul Mathews will present a review of types experiment designs and their capabilities including designs from classical DOE, Taguchi, and Shainin methods. Then he will lead an exercise to develop a flow chart for experiment design selection.


Component Swap II, Paul Mathews, 29 September 2006, 7:30-9:00 AM, T136.
At this second session on the component swap method, Paul Mathews will discuss the use of ANOVA to analyze component swap data and severl network members will present their component swap case studies.


Component Swap I, Paul Mathews, 1 September 2006, 7:30-9:00 AM, T136.
When an assembly has several separable components and there is excess variability that causes some of the assemblies to be good and others to be bad, the component swap method introduced by Dorian Shainin may be used to identify the component, components, or component interactions that cause that excess variability. Paul Mathews will describe the component swap method and demonstrate how to perform both Shainin's simplified analysis and a formal statistical analysis of component swap data. If you have a component swap case study that you'd like to share, please contact Paul before the meeting at paul@mmbstatistical.com.


Quality Management Jeopardy, Paul Mathews, 23 June 2006, 7:30-9:00 AM, T136.
Paul Mathews will host three rounds of quality management Jeopardy! Bring your co-workers and test your knowledge of quality management, Six Sigma, and Lean against other network members. Attendees will be given access to these Jeopardy rounds and Paul will explain how to populate the Jeopardy program with your own questions. Go here to get your own copy of our Jeopardy Excel/VB interface so you can re-play these three rounds of Quality Management Jeopardy and create your own rounds.


Design and Analysis of Accelerated Life Tests, Paul Mathews, 2 June 2006, 7:30-9:00 AM, T136.
When a life test run under normal operating conditions would take more time than is available and when there is a known stress factor (temperature, voltage, load, ...) that decreases life in a predictable way, an accelerated life test can be performed to reduce test time. Paul Mathews will present some of the considerations in the design of accelerated life tests and then he will use MINITAB to analyze a stepped-stress experiment.


Reliability Demonstration Tests, Paul Mathews, 31 March 2006, 7:30 - 9:00 AM, T136
Common goals of reliability experiments are to: demonstrate that the mean life exceeds a minimum value; demonstrate that the reliability at a specified time exceeds a minimum value; and demonstrate that the percentile (time or number of cycles) associated with a specified reliability exceeds a minimum value. All of these goals can be achieved using a special family of tests called reliability demonstration tests. Paul Mathews will discuss the basic concepts and methods of reliability demonstration tests for exponential, normal, and Weibull reliability distributions and he will demonstrate how to design such tests using the new tools available in MINITAB V14.


Analysis of Experiments with Nested Variables, Paul Mathews, 24 February 2006, 7:30 - 9:00 AM, T136
Experiments that include all possible combinations of levels of two or more variables are called factorial experiments, however, sometimes the levels of an experimental variable are unique to the levels of another experimental variable. In this case we say that the levels of one variable are nested within the levels of another. For example, an operation with three shifts may have four operators on each shift, but obviously not the same four operators on all shifts, so operators are nested within shifts. In more complicated cases, there may be several levels of nesting.

Paul Mathews will demonstrate the statistical analysis and interpretation of experiments with nested variables using two examples. The first example will consider a gage R&R study where each operator measures different parts. The second example will consider the variation in concentration of the active ingredient in a dry powdered blended product which is subdivided into a series of smaller and smaller nested units. If you have data from your own nested experiment that you would like to share with the network members, please forward them to Paul at paul@mmbstatistical.com.


Validating Your Process Capability Statistics, Paul Mathews, (27 January 2006, 7:30-9:00AM, Location T136)
With the popularity and growth of Six Sigma, the use of process capability and performance statistics has exploded, but few people recognize how sensitive these statistics are to underlying assumptions that usually go unchecked. Paul Mathews will use example data sets to demonstrate the importance of population normality, process stability, the presence of outliers, and sample size in the evaluation of process capability and present a procedure for validating process capability statistics.


Process Capability Analysis For Non-Normal Populations, Paul Mathews, (2 December 2005, 7:30-9:00AM, T136)
The popular process capability statistics like cp and cpk are only meaningful if the population being studied is normally distributed. When the population is known or suspected to be non-normal, alternative analysis methods should be used instead.

Paul Mathews will desmonstrate how the process fraction defective can be calculated directly from a cp/cpk pair and the consequences of a non-normal population. Then he will describe some appropriate alternative methods for analyzing and reporting process capability for non-normal populations.


Time-Weighted SPC Charts, Paul Mathews, (23 September 2005, 7:30-9:00AM, T136)
The most common control charts (e.g. x-bar and R, IMR, p, np, c, and u charts) plot statistics determined from the most recent process data. These charts are referred to as Shewhart charts because they follow the control chart principles developed by Walter Shewhart. A disadvantage of Shewhart charts is that they are relatively insensitive to small shifts in location. Even with the use of run or sensitizing rules (e.g. the Western Electric rules), Shewhart charts remain relatively weak to small shifts in location. This weakness is reduced by time-weighted control charts which plot statistics derived from the time-series of process data. Examples of time-weighted charts - which differ in the weights they apply to the time-series data - are cumulative sum (CUSUM) charts, exponentially-weighted moving average (EWMA) charts, and moving average (MA) charts. Paul Mathews will present an overview of time-weighted control charts, discuss some of their advantages and disadvantages, and analyze case study data using CUSUM, EWMA,  and MA methods.


Analysis Methods for Autocorrelated SPC Data, Paul Mathews (26 August 2005, 7:30-9:00AM, T136)
 Time-series data frequently display autocorrelation, that is, observations are not independent of each other but are serially correlated. For example, the temperature of a furnace, the dimension of a part feature manufactured with a wearing tool, and the sales of a seasonal product all frequently exhibit autocorrelation. When SPC charts are constructed for autocorrelated data, the control limits calculated by the usual methods are inappropriate and impractical, however, these difficulties can be resolved using the appropriate analysis.

Paul Mathews will demonstrate how to detect autocorrelation in time-series data, how to extract the necessary statistics from the data to account for autocorrelation, and how to construct an appropriate control chart with meaningful control limits for such data.

If you think that you have time series data that suffer from autocorrelation and are willing to share your data with the network members, please forward your data to Paul in Excel or MINITAB format at paul@mmbstatistical.com.


Experiments with Attribute Responses - Binary Responses III, Paul Mathews, Ray Tison, and Bob Anastos (22 July 2005, 7:30-9:00AM, T136)
At our last two meetings we discussed experiments that involved binary (e.g. pass/fail) responses. Now that we know how to fit and interpret binary logistic regression models and validate those models using lack of fit and residuals an alyses, it's time to look at some more case studies. Ray Tison from Dominion Gas Company will present a collections study; he needs to develop a model from historical data that will allow Dominion to predict whether a customer will pay their past-due gas bill or not. Bob Anastos will present data from a Plain Dealer article about the performance (number of educational goals met) of about 100 Cleveand area public schools as a function of number of students, median household income, spending per student, teacher pay, and other predictors. If you have your own binary response data set that you'd like to share with the group, please send it in advance to Paul Mathews at paul@mmbstatistical.com.


Experiments with Attribute Responses - Binary Responses II, Paul Mathews (24 June 2005, 7:30-9:00AM, T136)
At the last meeting (27 May 2005), Paul Mathews presented some basic concepts in the analysis of binary (pass/fail) responses and we analyzed a factorial experiment performed at Bescast to study the effect of three two-level process variables on a pair of binary responses. At the 24 June 2005 meeting, Paul will describe some of the diagnostic tools available to validate a binary logistic regression model and demonstrate them using the data from the Bescast case study. We will also discuss experimental design issues associated with binary response experiments and analyze data from several other experiments.


 Experiments with Attribute Responses - Binary Responses I, Paul Mathews (27 May 2005, 7:30-9:00AM, T136)
While many experiments generate responses that are quantitative, some experiments generate qualitative or attribute responses. There are three common familes of attribute responses: binary responses, ordinal responses, and nominal responses. Two-state (e.g. pass/fail or go/no-go) responses fall into the binary response category, ordinal responses have three or more levels related by size, and nominal responses group observations into three or more qualitative categories.
At this first in a series of sessions on analysis of attribute responses, Paul Mathews will present an overview of attribute response types with examples of each type. Then he will describe the analysis of binary response data and demonstrate these methods using the results from a factorial experiment performed on an investment casting process at Bescast. Subsequent meetings will consider other examples of binary response experiments and the other types of attribute responses.

Multiple-Stream Processes II, Paul Mathews (22 April 2005, 7:30-9:00AM, T136)
At our last meeting, Paul Mathews described some of the quality control methods available for multiple stream processes. In this second installment on the same topic, Paul will present examples of multiple stream processes that demonstrate the strengths and weaknesses of the various methods. If you have data from a multiple stream process that you wish to share with the group, please forward them to Paul at paul@mmbstatistical.com.


Multiple-Stream Processes, Paul Mathews (24 March 2005, 7:30-9:00AM, T136)
Multiple-stream processes have two or more parallel paths along which parts or material passes. Ideally all of the paths operate identically, so that there are no differences between the product from the different process streams, but in practice this rarely happens. Examples of multiple-stream processes are multi-cavity molding operations, multi-head machining operations, and multiple service providers (e.g. cashiers in a grocery store, help desk operators, etc.). Although each stream of a multiple-stream process could be evaluated for process control and capability, the amount of data and work required makes this approach impractical. Paul Mathews will discuss multiple-stream processes and the special methods available for process control (SPC) and process capability evaluation for these situations. If you have data from a multiple-stream process that you would like to share with the network members, please bring it in an Excel or MINITAB formatted file or e-mail the data to Paul before the meeting.


A Trebuchet Experiment (Session II): Documenting the Experiment, Paul Mathews (25 Feb 2005, 7:30-9:00AM, T136)
 At our last meeting (February 4) we designed and ran a screening experiment to study the launch distance of a trebuchet. We ran into some problems though, or rather the projectile did, like the wall in front of the trebuchet, the wall behind it, and even the ceiling. (There's right and left censored data, but ceiling censored???) Although the response was compromised by these problems, the experiment was still sensitive enough to identify the key variables that affect the trebuchet. At this week's joint meeting between the Tools and Techniques and the ISO/QS subgroups, Paul Mathews will use the trebuchet experiment as an example to demonstrate three different forms of documentation for a designed experiment. Specifically, he will discuss the documentation components of: 1) the complete DOE project, 2) a Powerpoint presentation, and 3) a formal report. Click here for presentation notes.


Design of Experiments: A Trebuchet Experiment (Session 1) and How to Document and Report It (Session 2), Paul Mathews (4 Feb and 25 Feb 2005, 7:30-9:00AM, T136)
The next two sessions (February 4 and Feruary 25) of the Tools and Techniques subgroup will consider design of experiments (DOE) topics. At the February 4 meeting, we will run an experiment to characterize the performance of a trebuchet. (Trebuchet's are a form of catapult that were developed during the middle ages as siege weapons. The trebuchet that we'll use won't be so big, so if we storm any castles, they're going to be real small.) The experimental program will be structured using Mathews' 11-step procedure: 1) Perform the input/process/output (IPO) analysis, 2) Document the process, 3) Construct the problem statement, 4) Perform preliminary experiments, 5) Select the experimental variables, their levels, and the experiment design, 6) Determine the randomization and blocking plans, 7) Execute the experiment, 8) Analyze the data, 9) Interpret the results, 10) Run a confirmation experiment, and 11) Report the results.

At the February 25 meeting, which will be a joint session between the Tools and Techniques and the ISO/QS subgroups, we will talk about the documentation associated with designed experiments. We will use the information collected from the February 4 trebuchet experiment as an example to: 1) show what documentation should be kept by the DOE project team leader, 2) prepare a plan for writing a formal report, and 3) outline the Powerpoint slides required for a presentation to a management leadership team.

If you're planning to come to these presentations, especially the first one, please be sure to RSVP. If there are a lot of people coming, we'll try to have more than one trebuchet available.


Degrees of Freedom, Paul Mathews (3 Dec 2004, 7:30-9:00AM, T136)
In response to a recent query from one of the network members, the next T&T session will be on degrees of freedom. The concept of degrees of freedom is fundamental to statistical methods, linear regression, ANOVA, and designed experiments, but a deep understanding of degrees of freedom takes some practice and study to achieve. In this session, Paul Mathews will present an overview of the calculation and interpretation of degrees of freedom in common statistical analyses. His presentation will start from the simplest of problems – the one-sample t test for location – and make the logical progression through two-sample t tests for location (Did you know that there are three different two-sample t tests, with different degrees of freedom?); one-way and two-way ANOVA; full-factorial and nested designs; linear, polynomial, and multiple regression; two-level factorial designs with and without center cells; and response surface designs. If you’re studying for the ASQ CQE or CSSBB certifications, or just anxious to understand this initially abstract topic, this session will be of significant value to you. Click here for presentation notes.


A Designed Experiment: The Bending of Beams, Paul Mathews (2 Nov 2004, 7:30-9:00AM, T136)
At the request of several network members, our next session will be on design of experiments (DOE). Classroom exercises in DOE and Six Sigma Black Belt courses usually involve paper helicopters and catapults. Another experiment that is easy to perform in a classroom setting is the study of the deflection of rectangular beams.

Paul Mathews will lead an experiment to study the deflection of simply supported rectangular beams as a function of beam height, width, span, and load. Paul will use a four variable Box-Behnken design to develop a response surface model for beam deflection. He will also show how to analyze the experimental data using a model derived from the mechanical analysis of beam bending. This case is of special interest because the two models, the first empirical and the second based on the first principles of mechanics, allow different interpretations and scopes of use.

If you plan to attend, please come promptly because the Box-Behnken design requires 27 experimental runs and we will have to work quickly to get through both the data collection and analysis steps.


Reliability IV, Paul Mathews (24 Sept 2004, 7:30-9:00AM, T136)
Based on the high degree of interest in the topic and the great attendance at the earlier sessions, we decided to hold another Tools and Techniques subgroup session on reliability. This time we'll talk about life studies that utilize accelerated testing. These tests employ an increased level of stress that decreases unit life, but the tests are performed in such a way that their results can be used to make life predictions under normal operating conditions. Some methods of accelerated testing are: running 120V appliances at 130V, running electrical components intended for use at room temperature at an elevated temperature, and running corrosion resistant components in higher-than-normal corrosive environments. Paul Mathews will provide an introduction to accelerated testing with some examples. If you have any data from an accelerated test that you would like to share, please forward the data to Paul at pmathews@apk.net or bring the data with you on a floppy disk in a MINITAB or Excel format.


Reliability III, Angela Lunato and Paul Mathews (3 Sept 2004, 7:30-9:00AM, T136)
In this third session on reliability, Angela Lunato will describe two recent experiments that she was involved in to study electric motor life and electrical cord strength.

She and Paul Mathews will present the analysis and the interpretation of the data using MINITAB. Paul will also demonstrate how to plan a life demonstration test and how to calculate the necessary sample size and acceptance requirements for the test. If you have any reliability study examples or case studies that you would like to share, please contact Paul at pmathews@apk.net.


Reliability II, Paul Mathews (30 July 2004, 7:30-9:00AM, T136)
At the last network meeting Paul Mathews presented an overview of reliability experiments, data, and analysis using exponential and Weibull reliability models. He also demonstrated how to construct confidence intervals, perform hypothesis tests, and calculate sample sizes for reliability problems. Since the examples that he used were chosen for their simplicity and good behavior, and now that you're all reliability experts, at this month's meeting he'll present some less well-behaved examples. If you have data that you'd like to share with the group please forward it to Paul at pmathews@apk.net.


Reliability I , Paul Mathews (25 June 2004, 7:30-9:00AM, T136)
In our next three meetings (June, July, and August) Paul Mathews will give presentations on the design and analysis of experiments to study reliability. In the first meeting (June 25, 2004) Paul will provide an introduction to reliability and describe the analysis of experiments that produce complete failure data, repairable systems data, and right censored data.  Examples from both life and strength testing situations will be presented and data will be analyzed using the Stat> Reliability/Survival tools in Minitab. Tentative plans for the second and third meetings are reliability demonstration tests and accelerated tests, however, suggestions for other reliability topics will be considered. If you have an interesting reliability problem that you think would be of general interest and would like to share with the Network members please contact Paul at pmathews@apk.net.


Management of Circular Normal Quality Characteristics, Paul Mathews (21 May 2004, 7:30-9:00AM, T136)
The normal distribution model is the one usually invoked to characterize measurement data, however, in some cases the normal model isn’t appropriate. One such case is common in manufacturing operations; if a position characteristic can vary in two perpendicular directions then the appropriate distribution model is possibly the two-dimensional normal or /circular normal/ distribution. Examples of the circular normal distribution are: the run-out of a lathe-turned part, the radial deviation of a feature on a plane from its target position, and the distribution of positions reported by a fixed global positioning system (GPS) receiver. Paul Mathews will talk about these and other cases in which circular normal data appear, how to confirm that data are circular normal, how to calculate fractions defective and set specifications for circular normal data, and how to perform SPC and process capability calculations for circular normal data. If you have data that you think might be circular normal please bring them to class in an Excel or Minitab file or forward them to Paul at pmathews@apk.net.


 Acceptance Sampling and Quality Cost, Paul Mathews (23 April 2004, 7:30-9:00AM, T136)
Although quality engineers frequently use the ANSI/ASQ Z1.4 standard (formerly Mil-Std-105) to design sampling plans for pass/fail inspection, Z1.4 does not explicitly take into account the costs associated with inspection and external failures. Paul Mathews will present an Excel spreadsheet demonstration that calculates and graphs the performance and quality costs associated with various sampling plans as a function of the sampling plan design, material/labor cost, inspection cost, and external failure cost. Each specified sampling plan is contrasted to the no-inspection and 100% inspection cases as benchmarks.

 If you have a case that you would like Paul to discuss in his presentation please forward him your sampling plan design and cost information at pmathews@apk.net.


 How to Use Statistical Methods to Analyze Investments, Paul Mathews and George Braidich (26 March 2004, 7:30-9:00AM, T136)
Statistics are an extremely powerful and reliable tool in determining an investment’s short-term performance. Still today they are not widely talked about. This presentation will illustrate how to use basic statistics to determine if an investment is a winner, loser, or just plain too risky.

In the first half of this presentation, Paul Mathews will review some basic statistical concepts. In the second half George Braidich will illustrate how to apply these concepts to evaluate financial investments.


 How to Read a Financial Statement - An Overview for Non-Financial Managers, George Braidich (20 Feb 2004, 7:30-9:00AM, T136)
Financial statements are the instrument panel of a business enterprise. They constitute a report on current managerial performance and flash warning signals of future impending difficulties. To read a complex instrument panel, one must understand its gages and their calibration to make sense of the data they convey. Various ratios and other mathematical techniques are used in a financial analysis. George will discuss these ratios and what each ratio attempts to measure. He will also discuss the “Quality of Earnings” in a business entity.


 The Use of Gage Error Studies to Guide An Instrument Purchasing Decision, Paul Mathews (23 Jan 2004, 7:30-9:00AM, L104)
Prior to making a large purchase of new large diameter bore gages, a network member ran a series of gage error studies of gages from four different manufacturers. The purpose of the study was to determine if there were differences in the repeatability and reproducibility of the different gages (there were!) and to use these observations to help guide the purchasing decision. Even if you’re not interested in large diameter bore gages, this case study effectively demonstrates the use of gage error studies to guide an important business decision – one that you’ll probably have to live with for years after the purchasing decision is made.


 Are Your Data Normal?, Paul Mathews (20 Nov 2003, 7:30-9:00AM, T136)
Most QC methods for products and processes assume that the distribution of errors follows the normal or bell-shaped curve. When this assumption is violated those methods can give erroneous results that frustrate quality managers and customers. One common example of this problem appears in manufacturing situations that involve positional deviations in two dimensions, such as run-out from an axis of rotation in a turning operation or the deviation of a feature from a target position on a two-dimensional surface. In these cases the normal distribution model underestimates the severity of positional problems and provides inadequate QC tools, however, there are appropriate QC tools available to manage this kind of data. Paul Mathews will demonstrate how to calculate specification limits, construct control charts, and evaluate process capability for such responses. If you have responses that you think might behave in the manner described please bring it on a floppy disk in Excel or Minitab format to share with the network members.


 Are Your Data Normal?, Paul Mathews (23 Sept and 23 Oct, 2003, 7:30-9:00AM, T136)
Many statistical analysis methods (e.g. SPC, acceptance sampling, GR&R studies, process capability studies, tolerancing) require that the distribution of measurement values under consideration follow the normal or bell-shaped curve. When these data don't follow the normal curve the usual methods of analysis may be incorrect. In many cases the problem can be resolved by applying a mathematical transformation (e.g. a square or square root operation) to the original measurement values but more difficult problems require special analyses. In the first part of a two-part presentation, Paul Mathews will demonstrate how to use normal probability plots and quantitative tests for normality to determine if data follow the normal distribution. He will also describe some common situations in which you can expect to use transformations. In the second installment of the two-part presentation, Paul will describe some special not-quite-normal distributions, specifically the half-normal and circular normal distributions, that show up frequently in real life situations.


 Methods of Setting Specifications, Paul Mathews (25 July and 22 August, 2003, 7:30-9:00AM, T136)
Quality characteristics that are critical to quality (CTQ) for your customer must have their specifications set to meet your customer’s requirements. However, the specifications for many other quality characteristics that are not critical to quality are often set by observation – if experience has shown that a certain range of values is acceptable then that range is taken to be the specification. Despite the lesser importance of these non-CTQ quality characteristics, valid methods of setting their specifications are very important because the non-CTQ quality characteristics greatly outnumber the CTQ quality characteristics.

In this two part presentation Paul Mathews will demonstrate statistically valid methods of setting specifications for quality characteristics that are not CTQ. In the first part (July 25) Paul will demonstrate a nonparametric or distribution-free method of setting specs that does not require the demonstration or assumption that the quality characteristic follows a normal or some other well behaved distribution. In the second part (August 22) Paul will demonstrate the tolerance limit method. This method is used when it is safe to assume that the quality characteristic is normally distributed – an assumption which must be carefully tested. Specifications determined from both methods, the nonparametric and tolerance limit methods, are determined from random samples drawn from the product or process being studied.

 Just in case you’re wondering if you slept through or missed the class in mechanical or manufacturing engineering school when these methods were taught, most such programs never or only weakly address these problems so come join us to finally learn how setting specifications should really be done.


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