In Part 5 of our series, we began the analysis of
the experiment data by reviewing analysis of covariance and
blocking variables, two key concepts in the design and
interpretation of your results.
250-yard marker at the Tussey Mountain Driving Range, one of the
locations where we conducted our golf experiment. Some of the
golfers drove their balls well beyond this 250-yard maker during a
few of... Continue Reading
Part 3 of our series, we decided to test our 4
experimental factors, Club Face Tilt, Ball Characteristics, Club
Shaft Flexibility, and Tee Height in a full factorial design
because of the many advantages of that data collection plan.
In Part 4 we concluded that each golfer
should replicate their half fraction of the full factorial 5 times
in order to have a high enough power to detect... Continue Reading
3 in our DOE problem solving methodology is to determine how many
times to replicate the base experiment plan. The discussion in Part 3
ended with the conclusion that our
4 factors could best be studied using all 16 combinations of the
high and low settings for each factor, a full factorial. Each
golfer will perform half of the sixteen possible combinations and
each golfer’s data could stand as... Continue Reading
I read trade publications that cover everything from banking to
biotech, looking for interesting perspectives on data analysis and
statistics, especially where it pertains to quality
Recently I read a great blog post from Tony Taylor, an analytical
chemist with a background in pharmaceuticals. In it, he discusses
the implications of the FDA's updated guidance for industry analytical...Continue Reading
2 in our DOE problem-solving methodology is to design the data
collection plan you will use to study the factors in your
experiment. Of course, you will have to incorporate blocking and
covariates in your experiment design, as well as calculate the
number of replications of run conditions needed in order to be
confident in your results.
We will address these topics in future posts, but for... Continue Reading
Step 1 in our DOE problem-solving methodology
is to use process experts, literature, or past experiments to
characterize the process and define the problem. Since I had little
experience with golf myself, this was an important step for me.
This is not an uncommon situation. Experiment designers often
find themselves working on processes that they have little or no
experience with. For example, a... Continue Reading
we broke for lunch, two participants in the training class began to
discuss, debate, and finally fight over a fundamental task in
golf—how to drive the ball the farthest off the tee. Both were avid
golfers and had spent a great deal of time and money on
professional instruction and equipment, so the argument continued
through the lunch hour, with neither arguer stopping to eat.
Several other... Continue Reading
Repeated measures designs don’t fit our impression of a typical
experiment in several key ways. When we think of an experiment, we
often think of a design that has a clear distinction between the
treatment and control groups. Each subject is in one, and only one,
of these non-overlapping groups. Subjects who are in a treatment
group are exposed to only one type of treatment. This is the... Continue Reading
I started out on the blog, I spent some time
showing some data sets that would be easy to illustrate
statistical concepts. It’s easier to show someone how something
works with something familiar than with something they’ve never
thought about before.
Need a quick illustration to share with someone about how to
summarize a variable in Minitab? See if they have a magazine on
their desk, and... Continue Reading
It sometimes may be prohibitively expensive or time-consuming to
gather data for all runs for a designed experiment (DOE).
For example, a 6 factor, 2-level factorial design can entail 64
experimental runs, which may be too high a number for your
particular situation. We have seen how to handle these some of
these situations in previous posts, such as Design
of Experiments: "Fractionating" and...Continue Reading
Ever use dental floss to cut soft cheese? Or Alka Seltzer to
clean your toilet bowl? You can find a host of nonconventional uses for ordinary objects
online. Some are more peculiar than others.
Ever use ordinary linear regression to evaluate a response
(outcome) variable of counts?
Technically, ordinary linear regression was designed to evaluate
a a continuous response variable. A continuous... Continue Reading
When performing a design of experiments (DOE), some factor
levels may be very difficult to change—for example, temperature
changes for a furnace. Under these circumstances, completely
randomizing the order in which tests are run becomes almost
impossible.To minimize the number of factor level changes for a
Hard-to-Change (HTC) factor, a
split-plot design is required.
Why Do We Want to Randomize a... Continue Reading
I've never understood the fascination with selfies.
Maybe it's because I'm over 50. After surviving the slings and
arrows of a half a century on Earth, the minute or two I spend in
front of the bathroom mirror each morning is more than
enough selfie time for me.
Still, when I heard that Microsoft had an online app that estimates
the age of any face on a photo, I was intrigued.
How would the app... Continue Reading
my previous post, I wrote about the hypothesis testing ban in
the Journal of Basic and Applied Social Psychology. I
showed how P values and confidence intervals provide important
information that descriptive statistics alone don’t provide. In
this post, I'll cover the editors’ concerns about hypothesis
testing and how to avoid the problems they describe.
The editors describe hypothesis testing... Continue Reading
This week I'm at the American Society for Quality's World
Conference on Quality and Improvement in Nashville, TN. The ASQ
conference is a great opportunity to see how quality professionals
are tackling problems in every industry, from beverage distribution
to banking services.
Given my statistical bent, I like to see how companies apply
tools like ANOVA, regression, and especially... Continue Reading
As a Minitab
trainer, one of the most common questions I get from training
participants is "what should I do when my data isn’t normal?" A
large number of statistical tests are based on the assumption of
normality, so not having data that is normally distributed
typically instills a lot of fear.
Many practitioners suggest that if your data are not normal, you
should do a nonparametric version of... Continue Reading
2016 presidential race is becoming more real. We’ve had several
announcements with Ted Cruz, Rand Paul, Hillary Clinton, and Marco
Rubio officially entering the race to be President. While the
prospective Democratic candidates are down to one, or at most a
few, the Republican field is extra-large this election cycle. The
first order of business for a GOP candidate is to survive the
nomination... Continue Reading
As a member of Minitab's Technical Support team, I get the
opportunity to work with many people using DOE (Design of
People often will call after they've already chosen their
design, run the experiment, and
identified the important factors in their process. But
now what? They have to find the best settings, but with
several factors and responses, what should they do?
"I wish I had
a... Continue Reading
Imagine that you are watching a race and that you are located
close to the finish line. When the first and fastest runners
complete the race, the differences in times between them will
probably be quite small.
Now wait until the last runners arrive and consider their
finishing times. For these slowest runners, the differences in
completion times will be extremely large. This is due to the fact
that... Continue Reading
Suppose that you have designed a brand new product with many
improved features that well help create a much better customer
experience. Now you must ensure that it is manufactured
according to the best quality and reliability standards, so that it
gets the excellent long-term reputation it deserves from potential
customers. You need to move quickly and seamlessly from Research
and Development into... Continue Reading
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