In my last post, I discussed how a DOE was
chosen to optimize a chemical-mechanical polishing process in
the microelectronics industry. This important process improved the
plant's final manufacturing yields. We selected an experimental
design that let us study the effects of six process parameters in
Analyzing the Design
Now we'll examine the analysis of the DOE results after the
actual... Continue Reading
I used to work
in the manufacturing industry. Some processes were so complex that
even a very experienced and competent engineer would not
necessarily know how to identify the best settings for the
You could make a guess using a general idea of what should be
done regarding the optimal settings, but that was not sufficient.
You need very precise indications of the correct... Continue Reading
P values have been around for nearly a century and they’ve been
the subject of criticism since their origins. In recent years, the
debate over P values has risen to a fever pitch. In particular,
there are serious fears that P values are misused to such an extent
that it has actually damaged science.
In March 2016, spurred on by the growing concerns, the American
Statistical Association (ASA) did... Continue Reading
Did you know that March is Women’s History Month? The
celebration was started in the 1980s by the U.S. government to pay
tribute to generations of influential women.
To celebrate, here’s a roundup of just some of the most
influential women in statistics:
While Florence Nightingale is known as the founder of modern
nursing, you might not know that she is also a... Continue Reading
In the world of linear models, a hierarchical model contains all
lower-order terms that comprise the higher-order terms that also
appear in the model. For example, a model that includes the
interaction term A*B*C is hierarchical if it includes these terms:
A, B, C, A*B, A*C, and B*C.
Fitting the correct regression model can be as
much of an art as it is a science. Consequently, there's not always
a... Continue Reading
How deeply has statistical content from Minitab blog posts (or
other sources) seeped into your brain tissue? Rather than submit a
biopsy specimen from your temporal lobe for analysis, take this
short quiz to find out. Each question may have more than one
correct answer. Good luck!
of the following are famous figure skating pairs, and which are
methods for testing whether your data follow a... Continue Reading
By Matthew Barsalou, guest
A problem must be understood before it can be properly
addressed. A thorough understanding of the problem is critical when
root cause analysis (RCA) and an RCA is necessary if an
organization wants to implement corrective actions that truly
address the root cause of the problem. An RCA may also be necessary
for process improvement projects; it is... Continue Reading
you know that November is World Quality Month? The American Society
for Quality is once again heading up this year’s festivities.
Throughout the month of November, ASQ will be promoting the use
of quality tools in businesses, communities, and institutions all
over the world. You can check it out at http://asq.org/world-quality-month/.
Here at Minitab, we’re also pretty excited about World... Continue Reading
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
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