How to apply the statistical method called Design of Experiments (DOE) for quality improvement and research.

It’s
the most wonderful time of the year – the time for holiday bakers
and cookie monsters to unite! So what’s a quality improvement
professional to do when his favorite sugar cookie recipe produced
cookies that failed to hold their festive holiday shapes after
being baked? Run a Design of Experiment (DOE), of course!
A Fractional Factorial Experiment
Bill Howell, an avid baker and... Continue Reading

Design
of experiments, experimental design, or just "gathering some data."
Whatever you want to call it, your approach to doing it will affect
the results you get.
Have you ever wondered about all those contradictory studies in
the news, especially regarding what's good and bad for you? Coffee
is good for you, one headline says. It's bad for you, says the
next. And if you read beyond the headlines,... Continue Reading

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We’ve used design of experiments to look at the data. We’ve seen
that
the center points are statistically insignificant. We’ve seen
that
blocks help account for the unstable conditions during the
collection of the data. Now for the exciting part: let’s choose
a model to use to predict where the gummi bears will land when we
launch them.
Various criteria exist for how to choose a model, so we’re... Continue Reading

Last time I used design of experiments to look at the gummi bear
data, I
interpreted the center point data. The data say that I won’t
need any square or cubic terms to get a good fit to the data.
Traditionally, the next effect to look at in design of experiments
is the block effect.
I was worried that there would be a wearout effect acting on my
catapult, so I changed popsicle sticks and rubber... Continue Reading

When I chose a full factorial design for my gummi bear
experiment, I was using traditional design of experiments practice
to try to learn the most from the least amount of data. I wanted to
see if I could save myself the 10 or more data points I would need
to add to the design to estimate nonlinear effects. Now that I have
some data, the first thing I’m going to learn is: Do I need to
collect... Continue Reading

Back when I chose the factors to study for my gummi bear design
of experiments, I was thinking about the fact that something like
the position of the gummi bear and the position of the fulcrum
would probably interact. When I finished collecting the data, I was
eager to see if that effect showed up in my analysis.
Before we look at the distance parallel to the catapult, let's
look at the distance... Continue Reading

I collected my first block of data for the gummi bear design of
experiments this week. Why not all of it? Well, there’s lots you
can learn when you start collecting data for real. Here are some of
my thoughts:
Enter data quickly and accurately for design of
experiments
If you’re going to do anything with your data, it’s a lot easier
to have it in Minitab. If you followed my lead for doing design... Continue Reading

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. Today we learn how an avid gamer used design of
experiments to boost his performance in his favorite driving
game.
If our software has helped you, please sha... Continue Reading

by Manikandan Jayakumar, guest blogger
In an earlier post, I discussed
how to collect data in a Design of Experiments (DOE) to
optimize the value of an attribute or categorical response
(Pass/Fail, Accept/Reject, etc.). I then showed how to
convert the collected data into proportions and apply the arcsine
transformation using built-in calculator in Minitab Statistical
Software.
Now we’re ready... Continue Reading

Recently, we’ve discussed how to do the
design and
factor setup for design of experiments in Minitab
Statistical Software. We’re almost ready to launch some gummi
bears. But there’s something else to consider. When we produce the
data for design of experiments, how does the data get from the
measuring device to Minitab?
If you’re lucky, you have an electronic thingamajig that takes
measurements... Continue Reading

by Manikandan Jayakumar, guest blogger
We use Design of Experiments (DOE) to optimize the value of a
response (Y) by simultaneously changing the values of several
factors (X’s). The response will often be a continuous variable,
but in some scenarios you need to optimize an attribute or
categorical response (Pass/Fail, Accept/Reject, etc.).
Collecting the Data for an Attribute Response DOE
Let’s see... Continue Reading

Now that we've explored all of the DOE design choices in
Minitab
Statistical Software, it's time to think about the levels of
the factors. I chose these 5 factors
previously:
Position of catapult on the launch ramp
Angle of catapult
Number of rubber band windings
Position of gummi bear on the catapult
Position of fulcrum in the catapult
What Is an Effect in DOE?
In DOE, we're trying to detect the effect... Continue Reading

Last time, we talked about
center points and replicates in design of experiments. It turns
out that both are tools that you can use to increase the
probability of finding a statistically significant difference. But
what we really want to know is, how many center points and
replicates should be in the gummi bear experiment? To answer that
question, we have to estimate the standard deviation of... Continue Reading

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... Continue Reading

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... Continue Reading

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... Continue Reading

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... Continue Reading

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.
Intuit... Continue Reading

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... Continue Reading

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 fac... Continue Reading