Last time, we talked about what resolution means in design of experiments (DOE). After you choose your resolution in Minitab Statistical Software, you need to choose the number of center points and the number of replicates for corner points. We can consider these two questions together because they’ll help determine the total size of the experiment.

Using center points to check your model

I alluded to center points when we talked about 2-level designs previously. Center points are experimental runs with the all of the continuous factor settings set halfway between the low level and the high...

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!

In early 2010, I discovered Minitab through recommendations from friends at the university. At first I found it very rigid and complicated, but with practice I discovered its simplicity. Minitab is great presentation tool with graphics, and helpful in designing experiments.

Wilfredo...

Unless you’ve been marooned on a desert island, you've probably been hearing a lot of hullaboo about the Higgs boson particle over the last few days.

Scientists claim they’ve finally proven the existence of this long sought-after “God particle,” which supports the standard model of the universe by identifying the particle that gives mass to other particles like protons and electrons.

If you’re keen on the statistics behind this discovery, you’ll notice that many news articles cite the fact that the scientists are certain of their results at the 5-sigma level, or a 99.9999% level of confidence.

Yo...

Now that we’ve settled on a 2-level factorial design, we’ll take a look at some of the different 2-level designs that we can run with 5 factors. Minitab gives us 3 options in design of experiments: a full factorial, a half fraction and a quarter fraction. In the statistical world of DOE, we say these designs offer different "resolutions" to an experiment.

You can think of choosing a statistical resolution in DOE as similar to choosing between cameras with 10 or 20 megapixels. In both designed experiments and with cameras, higher resolution is generally better. However, depending on your goal,...

Now that we’ve learned enough about design of experiments to understand the experimental designs that Minitab offers, it’s time  to consider which we should use to study the gummi bears. We'll consider the designs in the same order that we did in the earlier blog posts:

• General full factorial designs
• Split-plot designs
• Plackett-Burman designs

What Type of Design do I Need?

Here's a series of questions to ask:

1. Is it reasonable to assume there is a linear relationship between the factors and the response?

If you answer "yes" to that question, a factorial design is probably a good choice.

Remember,...

Design of experiments (DOEs) is a very effective and powerful statistical tool that can help you understand and improve your processes, and design better products.

DOE lets you assess the main effects of a process as well as the interaction effects (the effect of factor A, for example, may be much larger when factor B is set at a specific level, leading to an interaction). In science and in business, we need to perform experiments to identify the factors that have a significant effect. The objective of DOE is to reduce experimental costs—the number of tests—as much as possible while studying as...

In my work I get to learn about how companies use Minitab products to improve quality, and it's always a treat to learn how a company whose products I enjoy puts Minitab to use. So it was a real kick to read a Quality Progress article (free with registration at asq.org) about Starbucks. 

The article relates how, when voice-of-the-customer data revealed a need to fine-tune how the Starbucks’ one-pound packages of coffee were being sealed, the company corrected the issue.

Air oxidizes coffee and affects flavor, so protecting beans with an airtight seal was critical. But the package has to be easy...

I brainstorm quite frequently in my job—whether I’m trying to come up with topics that are “blog worthy” or if I’m thinking of a creative way to write content promoting a new feature of Minitab.

Sometimes I brainstorm alone, and sometimes I brainstorm with others during a meeting. Sometimes I brainstorm in a structured way by sharing ideas with colleagues in a circle, and sometimes I just throw ideas onto a sticky note.

I studied advertising in college and I even spent one summer interning at an ad agency, and brainstorming sessions were built right into the nuts and bolts of the creative...

Suppose that on your way to a summer holiday resort (a very distant place), your car breaks down. You might just call the roadside assistance and wait for your car to be towed to a repair shop. But suppose that you think you are smarter than that, and you would like to solve the issue by yourself—or at least evaluate the repair cost. Vehicle breakdowns can occur for a large number of reasons.

Intuitively, when facing a complex problem, we tend to test different solutions as soon as they come to our mind. When we understand that one solution will not work, we will then look for the next...

In previous posts on design of experiments, or DOE, we’ve covered:

• General full factorial designs
• 2-level designs
• Plackett-Burman designs

Next on the list are split-plot experiments.

Split-plot designs are extremely popular in design of experiments because they cover a common case in the real world: when you have a factor that you want to study but can’t change as often as your other factors.

If you have an agricultural mind, as many scientists did back when they were inventing the name of this method, you’ll appreciate the language about "splitting a plot." Suppose that you wanted to study a...

Last time, we talked about why to use designs with just two levels. Now it’s time to discuss the two-level options in design of experiments, starting with Plackett-Burman designs.

Plackett-Burman designs exist so that you can quickly evaluate lots of factors to see which ones are important. Plackett-Burman designs are often called “screening designs” because they help you screen out unimportant factors.

This fits in with our previously-stated goal for design of experiments: learn as much as possible from the smallest amount of data. If we’re not sure a factor is important, we don’t want...

In my last post, I talked about the danger of excluding interactions between factors in ANOVA and DOE models. Let’s now look at what can happen if you exclude an important factor altogether.

Warning: misleading high p-value up ahead...

Minitab regularly hosts webinars on different statistical topics. Let’s suppose we want to evaluate if certain webinar topics are more popular than others, so we collect data on the number of people who register for various sessions, including t-tests, control charts, design of experiments and Weibull analysis. Here’s an example of what the data might look like:

To...

I remember a time in my career when I mistakenly thought I knew statistics—really knew statistics. It was before I met Yanling Zuo, Michelle Paret, Eduardo Santiago and a whole host of other Minitab statistical experts. I was a Quality Engineer and I’d been applying statistics for years. I assumed that the ability to design and run an experiment meant that I understood DOE. I assumed that years of process control meant that I understood control charting. I assumed that I’d use this knowledge to jump on the “fast track” to technical stardom.

It does not, and I did not.

I, in fact, knew a lot...

In the last post, we discussed how general full factorial designs let you study factors at more than two levels. The remaining 4 options that Minitab offers for factorial design of experiments are all 2-level designs, including the Plackett-Burman design.

Because there are 4 different kinds of 2-level designs, one of which is selected by default, you can probably guess that 2-level designs are quite popular. So what’s special about a 2-level design, and why would we use one?

One of the benefits of using design of experiments to plan data collection is to learn as much as possible from the...

I think it’s neat to find examples of Lean Six Sigma techniques in restaurants and stores when I’m out to eat or shopping. What started as a philosophy in the manufacturing world seems to be transcending into our everyday shopping experiences, and even into the products we choose to add to our carts.

Here’s a recap from my latest “Lean” shopping escapade:

I was recently in a grocery store snack aisle when I came across a bag of dark potato chips.

I thought this was a great use of a snack manufacturer taking what is usually considered a defect and turning it into a source of revenue. Of course...

I don’t know about you, but I’m thankful we no longer live in a time when we feel compelled to swig swamp-root juice any time we want to feel better. The field of medicine couldn’t advance by relying solely on subjective anecdotes and testimonials, like those for Dr. Kilmer’s cure-all.

Using statistical analyses, we can now objectively evaluate various preventions and treatments in measurable, quantitative ways.

Curious to see what Minitab Statistical Software was up to in healthcare and medicine lately, I ran a Medline search. In the past year, the software has been busy making the rounds,...

Having spent some time figuring out what to do with the different variables for our gummi bear experiment, it’s time to get into Design of Experiments with Minitab. Opening Stat > DOE > Factorial > Create Factorial Design presents you with 5 options to choose from immediately:

The dialog box is helpfully explaining that some of these designs are for different numbers of factors. For example, if you want to create an experiment to study more than 15 factors in Minitab Statistical Software, you’re directed to a Plackett-Burman design.

This is somewhat helpful if you know what a factor is....

Do you prefer ketchup or soy sauce?

If someone asked you this question, your answer would likely depend upon what you were eating. You probably wouldn't dunk your spicy tuna roll in ketchup. And most people (pregnant moms-to-be excluded) don't seem to fancy eating soy sauce with hot French fries.

A Common Error When Using ANOVA or DOE to Assess Factors

Modeling techniques such as ANOVA or Design of Experiments (DOE) can determine if factors of interest impact a process. For example, you may want to evaluate how various time and temperature settings affect product quality. Or you may want to...

If you pay close attention to this series on using gummi bears to understand design of experiments, you noticed that in my last post I mentioned pressure as a variable for the first time. Pressure wasn’t on the fishbone diagram that I used when planning variables, even though it’s just as obvious as temperature and humidity.

I’ve been referring to the fishbone diagram quite a bit, but I didn’t spend much time creating it nor did I gather anyone else’s input. Therefore, there are probably lots of unconsidered variables that could make a difference in how far the gummi bears traveled. I can...

I recently had the opportunity to learn about how synthetic yarn manufacturer Unifi Manufacturing Inc. used Minitab to optimize its false-twist texturing process. Many of you probably have several connections to Unifi yarns that you just don’t know about!

In fact, you've probably found yourself wearing clothing made with Unifi yarns, or sitting on a couch made of stain-resistant fabric that was upholstered with Unifi yarn technologies.

What’s neat about Unifi products is that the company’s manufacturing processes turn raw and recycled materials and fibers into synthetic yarns that behave like...