Unbalanced Designs and Gage R&R (Expanded)

Last week, a customer called with an issue related to running a Gage R&R nested design in Minitab Statistical Software.  Everything initially looked okay, as he had the three columns necessary to perform a successful study: one for parts, one for operators, and another for the measurements.  However, when he tried to analyze his data using Stat > Quality Tools > Gage Study > Gage R&R (Nested), he would get this error message:

Gage R&R Study - Nested ANOVA

* ERROR * Design is not balanced; execution aborted.

One of the assumptions in performing a Nested Gage analysis is that the data is balanced,...

Gummi Bear Design of Experiments: Choosing Factors to Study

In recent posts, we’ve reviewed a number of Measurement Systems Analysis (MSA) studies: Type I Gage Studies, Linearity and Bias Studies, and Gage R&R Studies. Before that, we took a look at a cause and effect diagram, also called a fishbone diagram. And we did all this because we were getting ready to practice a designed experiment.


Remember the fishbone diagram?

On the fishbone diagram, I came up with 24 variables that might affect how far the gummi bears go. I’d like to study as many of these factors as possible, but first need to decide which ones I can both manipulate and measure. Here...

Gummi Bear Measurement Systems Analysis (MSA): The Gage R&R Study

So this time, we finally have the fun of getting to look at the full gage R&R results. Here’s how the data turned out, and some of the exciting stuff that went along with getting it.

RunOrder Operators Parts Measurements 1 J1 Red 37.9375 2 J1 Green 28.375 3 J1 Yellow 72.25 4 J2 Yellow 72.0625 5 J2 Red 38.25 6 J2 Green 28.4375 7 Kevin Red 41.0625 8 Kevin Green 22.25 9 Kevin Yellow 72.1875 10 J3 Green 22.1875 11 J3 Yellow 72.3125 12 J3 Red 40.9375 13 M Red 40.8125 14 M Yellow 72 15 M Green 22.3125 16 J1 Yellow 72 17 J1 Red 38.125 18 J1 Green 28.4375 19 J2 Yellow 72 20 J2 Red 38.125 21 J2 Green...

Creating a Gage R&R Study Worksheet for Measurement Systems Analysis (MSA)

Now that we’ve explored using gummi bears to do a gage Linearity and Bias Study and a Type I Gage Study, it’s time to use gummi bears to practice the third and final type of measurement systems analysis that I’m planning to demonstrate: the gage R&R study.

"R&R" stands for Repeatability and Reproducibility, which are the two sources of variation we typically evaluate in a gage R&R study. Repeatability assesses how well the same person can get the same measurement over and over again when he or she measures the same part. Reproducibility assesses how well different people can get the same...


When I first started learning about Lean Six Sigma and familiarizing myself with all the different terminology and methodologies, I was a little overwhelmed and confused by all the acronyms. FMEA, C&E Matrix, Gage R&R, SIPOC, DMAIC…the list goes on and on!

However, I really got tripped up by the similar-sounding acronyms of DMAIC, DMADV, and DFSS. These acronyms stand for the most common Lean Six Sigma project methodologies, which help quality improvement practitioners keep their projects focused with an established route to follow for completion. Each methodology is made up of five phases and...

Gummi Bear Measurement Systems Analysis: The Gage Linearity and Bias Study

Last time, we set up a worksheet for doing a Gage Linearity and Bias Study in Minitab Statistical Software. This time, we’ll take a look at my sample data and see what we might learn from a Gage Linearity and Bias Study. Getting comfortable with the variation present in measurement systems will go a long way towards building your confidence with quality statistics. Remember too that the experience of collecting data will help you to understand some of the issues that arise when you want to do process improvement projects.   

If you got started early, and followed my measurement process, let me...

Measurement Systems Analysis Needs a Stratified Random Sample (Even with Gummi Bears)

Today, we’re going to get ready to do a Gage Linearity and Bias Study with gummi bears. But to do the linearity and bias study, you first have to talk more about how to collect the data. The Gage Linearity and Bias study has a complication that wasn’t present in the Type 1 Gage Study.

The point of the gage linearity and bias study is to assess the bias of a gage across its operating range, not just in one place. That way, we can learn if large measurements or small measurements are harder to get right. The NIST Engineering Statistics Handbook suggests that you need a minimum of 5 standards...

Gummi Bear Measurement Systems Analysis: Type 1 Gage Study

Gummi bears have more to teach us about measurement systems analysis.

Today, we’ll look at doing a Type 1 Gage Study, which compares the measurement variation to the specifications for your process, to judge whether a gage is measuring well enough. The Type 1 Gage Study is a starting point because it evaluates accuracy, precision, and consistency, but only for a single case. Later, we’ll look at the Gage Linearity and Bias Study and the Gage R&R Study.

I’ve mentioned that we will study the effect of different factors on how far a gummi bear flies, but the real goal of knowledge about factor...

Statistics with Gummi Bear Catapults part 2: Measurement System Analysis Considerations

What can gummi bears tell us about
measurement system analysis

We're still practicing statistics with gummi bears, because they don't bounce or slide off of popsicle sticks. In my last post, we looked at some factors that might affect how far a gummi bear flies off of a popsicle stick catapult. Next, what we'd really like to do is to pick some factors to study and do a designed experiment to determine their effect on how far a gummi bear flies, but that's not what we're going to do first. First, we're going to see how well we can measure how far a gummi bear flies.

By "how well," I mean in...

How to Interpret Gage R&R Output - Part 2

Another common question with Gage Crossed is what table to look at when assessing your measurement system.  By default, Minitab gives a %Contribution table and %Study Variation table. Which one should you use when assessing where the variation is mostly coming from? Well, you could use either of them.

The %Contribution table can be convenient because all sources of variability add up nicely to 100%. Example:

The %Study Variation table doesn’t have the advantage of having all sources add up nicely to 100%, but it has other positive attributes. Because standard deviation is expressed in the...

Understanding "Number of Distinct Categories" in Your Gage R&R Output

Recently I've been thinking about common questions that customers ask when running a Gage R&R analysis in Minitab.

For example, when you run a Gage R&R, the last result that shows up in the session window is a value for the ‘Number of Distinct Categories’.  This one metric is something that customers seem to overlook when they call to discuss their Gage studies.
This value represents the number of groups your measurement tool can distinguish from the data itself. The higher this number, the better chance the tool has in discerning one part from another.

So how do you know if your...

Don't Fear Statistics

“Fear leads to anger, anger leads to hate, hate leads to suffering.” Yoda

This quotation sums up the way most people’s relationship to statistics develops. But not me.

My name is Cody Steele, and I’m the guy who Eston Martz warned you about. From the time my 6-year-old self decided that Superman’s most amazing power was his intelligence, through buying a calculus book after college so that I could integrate functions for fun, I always liked math. After I took my first statistics course, I was hooked. It astounded me that statistics could help you make decisions when all you had to go on was...

Three "Measurement System Analysis" Questions to Ask Before You Take a Single Measurement

Data collection involves taking measurements, and this seems like a simple thing when the subject is relatively simple.  However, even the simplest of cases has the potential to be messed up.  I found this out the hard way once. I hope sharing it helps you avoid a similar experience.

Experienced researchers and quality practitioners know they need to verify that a measurement system provides valid results. For instance, you can use Minitab’s Gage R&R study tools and the Gage linearity and bias tool to determine whether your measurement system is accurate and precise from a statistical...