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Analyze a DOE with the Assistant in Minitab 17

By now, you probably know that Minitab 17 includes Design of Experiments (DOE) in the Assistant. We already spent some time looking at 5 highlights when you create a screening experiment with the Assistant in Minitab 17.

But the Assistant can also help you make sense of the data you collect for your experiment. After you create a design with the Assistant, choose Assistant > DOE > Analyze and Interpret and you’re on your way. Exactly what you get depends on which type of design you’re analyzing, but there’s some really neat stuff to help you get the most out of your data. Here are 3...

ITEA Sneak-Peek: The Great Escape from Foam Defects

The 2014 ASQ World Conference on Quality and Improvement is coming up in early May in Dallas, and this year’s International Team Excellence Award Process (ITEA) will also come to a close at the conference, as winners from the finalist teams will be chosen for ASQ gold, silver, or bronze-level statuses.

What’s ITEA?

The annual ASQ ITEA process celebrates the accomplishments of quality improvement teams from a broad spectrum of industries from around the world. The ITEA is the only international team recognition process of its kind in the United States, and since 1985, more than 1,000 teams from...

Create a DOE Screening Experiment with the Assistant in Minitab 17

If you’ve been looking at Minitab 17, you’ve noticed a lot of new enhancements. For me, the biggest of these is the addition of Design of Experiments (DOE) to the Assistant. DOE in the Assistant has so many exciting aspects it’s hard to take it all in at once, but here are 5 highlights for when you plan and create a screening experiment:

1. Just-in-time guidance

If you’re lucky, you’ve had the chance to study DOE with an expert. If not, even the flow chart that opens with the Assistant to plan an experiment might seem intimidating. Fortunately, you don’t have to go scouring the thrift store for...

Opening Ceremonies for Bubble Plots and Poisson Regression

By popular demand, Release 17 of Minitab Statistical Software comes with a new graphical analysis called the Bubble Plot.

This exploratory tool is great for visualizing the relationships among three variables on a single plot.

To see how it works, consider the total medal count by country from the recently completed 2014 Olympic Winter Games. Suppose I want to explore whether there might be a possible association between the number of medals a country won and its maximum elevation. For that, I could use a simple scatterplot, right?

But say I want to throw a third variable into the mix, such as...

Histograms are Even Easier to Compare in Minitab 17

Minitab 17 came out yesterday and it’s got quite a few neat features in it. You can check some of them out on the What’s New in Minitab 17 page. But one of my very favorite things is related to one of my previous blog posts that showed how to make histograms that are easy to compare. Turns out, you don’t need those steps anymore. You can do it all with Minitab’s Assistant.

Here’s how to open the data that I’m using if you want to follow along.

  • Choose File > Open Worksheet.
  • Click Look in Minitab Sample Data Folder.
  • Select Cap.MTW and click Open.

You can still rearrange a paneled histogram to make...

Gauging Gage Part 3: How to Sample Parts

In Parts 1 and 2 of Gauging Gage we looked at the numbers of parts, operators, and replicates used in a Gage R&R Study and how accurately we could estimate %Contribution based on the choice for each.  In doing so, I hoped to provide you with valuable and interesting information, but mostly I hoped to make you like me.  I mean like me so much that if I told you that you were doing something flat-out wrong and had been for years and probably screwed somethings up, you would hear me out and hopefully just revert back to being indifferent towards me.

For the third (and maybe final) installment, I...

Gauging Gage Part 2: Are 3 Operators or 2 Replicates Enough?

In Part 1 of Gauging Gage, I looked at how adequate a sampling of 10 parts is for a Gage R&R Study and providing some advice based on the results.

Now I want to turn my attention to the other two factors in the standard Gage experiment: 3 operators and 2 replicates.  Specifically, what if instead of increasing the number of parts in the experiment (my previous post demonstrated you would need an unfeasible increase in parts), you increased the number of operators or number of replicates?

In this study, we are only interested in the effect on our estimate of overall Gage variation. Obviously,...

Applying Six Sigma to a Small Operation, Part 2

In my previous post, I shared a case study of how a small bicycle-chain manufacturing company in India used the DMAIC approach to Six Sigma to reverse declining productivity.

After completing the Define, Measure, and Analysis phases, the team had identified the important factors in the bushing creation process. Armed with this knowledge, they were now ready to make some improvements.

The Improve Phase

In the Improve phase, the team applied a statistical method called Design of Experiments (DOE) to optimize the important factors they'd identified in the initial phases.

Most of us learn in school...

Creating a Shatterproof Process: Students Use Six Sigma to Improve Window Manufacturing

I had the opportunity to speak with a great group of students from the New Jersey Governor’s School of Engineering and Technology—a summer program for high-achieving high school students. Students in the program complete a set of challenging courses while working in small groups on real-world research and design projects that relate to the field of engineering. Governor’s School students are mentored by professional engineers as well as Rutgers University honors students and professors, and they often work with companies and organizations to solve real engineering problems.

The team of students...

Doggy DOE Part III: Analyze This!

What factors significantly affect how quickly my couch-potato pooch obeys the “Lay Down” command?  

The cushiness of the floor surface? The tone of voice used? The type of reward she gets? How hungry she is?

I created a 1/8 fraction Resolution IV design for 7 factors and collected response data for 16 runs. Now it’s time to analyze the data in Minitab, using  Stat > DOE > Factorial > Analyze Factorial Design.

After removing insignificant terms from the model, one at a time, starting with the highest-order interaction, here's the final model:

Of the original 7 factors in the screening experiment,...

Four Tips on How to Perform a Regression Analysis that Avoids Common Problems

In my previous post, I highlighted recent academic research that shows how the presentation style of regression results affects the number of interpretation mistakes. In this post, I present four tips that will help you avoid the more common mistakes of applied regression analysis that I identified in the research literature.

I’ll focus on applied regression analysis, which is used to make decisions rather than just determining the statistical significance of the predictors. Applied regression analysis emphasizes both being able to influence the outcome and the precision of the predictions.

Tip...

Doggy DOE Part II: Create Your Design

Nala, our 6-year-old golden retriever, loves her dogma. That's her sitting in front of church on Sunday morning.

But she's not crazy about her catechism. For example, she doesn't always dutifully follow the "Lay Down" commandment.  

What factors may be influencing her response? We're performing a DOE screening experiment to find out.

In this post, we'll use Minitab Statistical Software to

  • Create the design for the experiment
  • Determine the confounding pattern for this design
  • Set up the data collection worksheet

Create the Design for the Experiment

In the previous post, we used the Display Design dialog...

Making a Difference in How People Use Data

A colleague of mine at Minitab, Cheryl Pammer, was recently featured in "A Statistician's Journey," a monthly feature that appears in the print and online versions of the American Statistical Association's AMSTAT News magazine.  

Each month, the magazine asks ASA members to talk about the paths they took to get to where they are today. Cheryl is a "user experience designer" at Minitab. In other words, she's one of the people who help determine how our statistical softwaredoes what it does, and tries to make it as helpful, useful, and beneficial as possible. Cheryl is always looking for ways to...

Doggy DOE Part I: Design on a Dime

Design of experiments (DOE) is an extremely practical and cost-effective way to study the effects of different factors and their interactions on a response.

But finding your way through DOE-land can be daunting when you're just getting started. So I've enlisted the support of a friendly golden retriever as a guide dog to walk us through a simple DOE screening experiment.

Nala, the golden retriever, is shown at right. Notice how patiently she sits as her picture is being taken. She's a  true virtuoso with the "Sit" command.

But "Lay Down" is another story...

Formulate the Objective

Although Nala know...

Using Prediction Intervals to Define Process Windows

Making parts that are truly interchangeable is a critical aspect of modern manufacturing. The same parts may be manufactured in different plants spread around the globe or by suppliers located far away. Parts need to be manufactured to specifications to ensure that they are almost identical to allow an easy assembly of new products.

Interchangeability is increasingly important in the service industry as well. Because customers expect similar standards from a service company wherever it does business around the globe, best practices need to be deployed throughout a company and...

Itchy, Sneezy, Stuffy: Delivering Relief with Nasal Spray and DOE

Recently, a customer called our Technical Support team about a Design of Experiment he was performing in Minitab Statistical Software. After they helped to answer his question, the researcher pointed our team to an interesting DOE he and his colleagues conducted that involved using nasal casts to predict the drug delivery of nasal spray.

The study has already been published, and you can read more about it here, but I wanted to highlight this use of the DOE tools in Minitab in this blog post.

Using Nasal Casts to Predict Nasal Spray Drug Delivery

The nose is a convenient route of administration...

Using Multi-Vari Charts to Analyze Families of Variations

When trying to solve complex problems, you should first list all the suspected variables identify the few critical factors and separate them from the trivial many, which are not essential to understanding the cause.

 

    

 

Many statistical tools enable you to efficiently identify the effects that are statistically significant in order to converge on the root cause of a problem (for example ANOVA, regression, or even designed experiments (DOEs)). In this post though, I am going to focus on a very simple graphical tool, one that is very intuitive, can be used by virtually anyone, and does not...

The Gentleman Tasting Coffee: A Variation on Fisher’s Famous Experiment

by Matthew Barsalou, guest blogger

In the 1935 book The Design of Experiments, Ronald A. Fisher used the example of a lady tasting tea to demonstrate basic principles of statistical experiments. In Fisher’s example, a lady made the claim that she could taste whether milk or tea was poured first into her cup, so Fisher did what any good statistician would do—he performed an experiment.

The lady in question was given eight random combinations of cups of tea with either the tea poured first or the milk poured first. She was required to divide the cups into two groups based on whether the milk or...

A Brief Illustrated History of Statistics for Industry

by Matthew Barsalou, guest blogger

The field of statistics has a long history and many people have made contributions over the years. Many contributors to the field were educated as statisticians, such as Karl Pearson and his son Egon Pearson. Others were people with problems that needed solving, and they developed statistical methods to solve these problems.

The Standard Normal Distribution

One example is Karl Gauss and the standard normal distribution, which is a key element in statistics. The distribution was used by Gauss to analyze astronomical data in the early nineteenth century and is...