As we said in yesterday’s post, it’s been exciting for Minitab to be a supporter of the ASQ World Conference on Quality and Improvement taking place this week in Indianapolis. There have been many great sessions and an abundance of case studies shared that highlight how quality teams worldwide are improving the performance of their businesses.

One session that generated a lot of interest from the conference participants was conducted by Minitab trainers Lou Johnson, Daniel Griffith and Jim Colton.

Their presentation, Sampling Plan for Expanded Gage R&R Studies, covered Gage R&R studies and how...

As a teenaged dishwasher at a local eatery, I had a boss who'd never washed dishes in a restaurant himself. I once spent 40 minutes trying to convince him that forks and spoons should go in their holders with the business end up, while knives should go in point-down. Whatever I said, he didn't get it. We were ordered to put forks and spoons in the holders with the handles up.

The outraged wait staff soon made clear what I hadn't: you can't immediately tell the difference between a fork and a spoon when all you can see is the handle! Explaning that in the right way would have minimized wasted...

Recently, Minitab News featured an article that talked about how to perform a Gage R&R Study with only one part. This prompted many users to contact our technical support team with questions about next steps, like these:

• What can I do with the output of a Gage study with only one part? 
• How can I use the variance component estimates to obtain meaningful information about my measurement system?

By themselves, the variance component estimates from the ANOVA output for a Gage study with just one part are not particularly useful. However, if we combine what we’ve learned about the variance for...

Measurement systems analysis (MSA) is essential to the success of any data analysis. If you cannot rely on the tool you’re using to take measurements, then why bother collecting data to begin with? It would be like trying to lose weight while relying on a scale that doesn’t work. What’s the point in weighing yourself?

Minitab Statistical Software offers many types of tools that you can use to assess your measurement system, including:

• Gage R&R Study (Crossed)
• Gage R&R Study (Nested)
• Attribute Agreement Analysis

Destructive Testing Defined

In MSA studies for continuous measurements (e.g. weight,...

I recently got a request from one of our Facebook fans to do a post about orthogonal regression, which I admit is not a subject I’m very familiar with. However, with a little help from Minitab’s help resources and by consulting a few Minitab experts, I think I came up with a post that will be useful. I thought it would help to discuss orthogonal regression with an example, but first...

What the Heck Is Orthogonal Regression?

Orthogonal regression is also known as “Deming regression” and examines the linear relationship between two continuous variables. It’s often used to test whether two...

The Minitab Fan section of the Minitab blog is your chance to share with our readers! We always love to hear how you are using Minitab products for quality improvement projects, Lean Six Sigma initiatives, research and data analysis, and more. If our software has helped you, please share your Minitab story, too!

Throughout my 15 years as a Six Sigma Initiative Leader, Consultant, Trainer, Black Belt and master Black Belt I have been enthusiastic about Minitab Statistical Software, starting from release 11 to now.

Minitab's graphics are outstanding in their ability to present messages and...

Have you ever found yourself switching back and forth between a Microsoft Excel file and Minitab Statistical Software just to complete a single analysis? Which software will give me the accurate results I need quickly?

I decided to put a few important factors to the test—workflow, organization, quality focus, and help. The review below provides my own two cents on which software seems to work best in a different situations.

Creating Graphs with Raw Data Easily

Microsoft Excel is a general spreadsheet software program. It is great for compiling, sorting, and highlighting large amounts of data....

There are some sounds that are quintessential summertime…the whir of the lawnmower, shrieks of children splashing in the pool, the crackle of a campfire. I’m sure we could think of a hundred more. For me, one sound that comes to mind in particular is the chirp of crickets in the evening. In this blog we'll recreate an old country trick using cricket chirps and, I hope, learn some new Minitab tricks along the way!

Our story starts with Amos Dolbear, an American physicist and inventor who lived in the late 1800s. Dolbear invented several elements of the telephone and was, therefore, attuned to...

I remember sitting in my ninth-grade chemistry class when my teacher mentioned that the day’s lesson would include a discussion about accuracy and precision, and how both relate to making experimental measurements. I’ve always been more of a liberal-arts-minded individual, and I initially thought, Is there really a difference between the two terms? In fact, I even remembered using the words interchangeably in my writing for English class!

However, as I continued through more advanced science and math courses in college, and eventually joined Minitab Inc., I became tuned in to the important...

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,...

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...

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...

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...

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...

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 bulding 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...

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 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...

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...

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...

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...