Blog posts and articles about using Weibull distribution analysis in statistics and quality improvement.

Reliability and survival analysis is used most frequently in
manufacturing. Companies use these methods to estimate the
proportion of units that will fail within, or survive beyond, a
given period of time. But could these
reliability and survival analysis techniques prove
useful in a zombie apocalypse, too? Today's blog post explores that
chilling scenario.
Think. This is what Zachary is telling... Continue Reading

Since it's the Halloween season, I want to share how a classic
horror film helped me get a handle on an extremely useful
statistical distribution.
The
film is based on John W. Campbell's classic novella "Who Goes
There?", but I first became familiar with it from John
Carpenter's 1982 film The Thing.
In the film, researchers in the Antarctic encounter a predatory
alien with a truly frightening... Continue Reading

by Matthew Barsalou, guest
blogger.
The old saying “if it walks like a duck, quacks like a duck and
looks like a duck, then it must be a duck” may be appropriate in
bird watching; however, the same idea can’t be applied when
observing a statistical distribution. The dedicated ornithologist
is often armed with binoculars and a field guide to the local birds
and this should be sufficient. A... Continue Reading

To choose the
right statistical analysis, you need to know the distribution of
your data. Suppose you want to assess the capability of your
process. If you conduct an analysis that assumes the data follow a
normal distribution when, in fact, the data are nonnormal, your
results will be inaccurate. To avoid this costly error, you must
determine the distribution of your data.
So, how do you determine... Continue Reading

Over the last year or so I’ve
heard a lot of people asking, “How can I calculate B10 life in
Minitab?” Despite being a statistician and industrial engineer
(mind you, one who has never been in the
field like the customers asking this question) and having taken a
reliability engineering course, I’d never heard of B10 life. So I
did some research.
The B10 life metric originated in the ball and roller... Continue Reading

Previously,
I’ve written about when to choose nonlinear regression and
how to model curvature with both linear and
nonlinear regression. Since then, I’ve received several
comments expressing confusion about what differentiates nonlinear
equations from linear equations. This confusion is understandable
because both types can model curves.
So, if it’s not the ability to model a curve, what isthe... Continue Reading

There is more than just the p value in a probability plot—the
overall graphical pattern also provides a great deal of useful
information. Probability plots are a powerful tool to better
understand your data.
In this post, I intend to present the main principles of
probability plots and focus on their visual interpretation using
some real data.
In probability plots, the data density distribution... Continue Reading

We're
frequently asked whether Minitab has been validated by the U.S.
Food and Drug Administration (FDA) for use in the pharmaceutical
and medical device industries.
Minitab does extensive testing to validate our software
internally, but Minitab’s statistical software
is not—and cannot be—FDA-validated out-of-the-box.
Nobody's can.
It is a common misconception that software vendors can go
through a... Continue Reading

We
all seem to have our favorite statistical or quality improvement
tool. Jim Frost wrote a tribute to regression analysis. Dawn Keller seems to enjoy
control charts. Eston Martz discusses reliability analysis and the Weibull distribution pretty regularly. So, I
started thinking … what’s my favorite quality tool?
I’ve always been drawn to process mapping, or what's sometimes
referred to as ‘flow... Continue Reading

Over on the Indium Corporation's blog, Dr. Ron Lasky has been
sharing some interesting ideas about using the Weibull distribution
in electronics manufacturing. For instance, check out this discussion of how dramatically an early
first-failure can affect an analysis of a part or component (in
this case, an alloy used to solder components to a circuit
board).
This got me thinking again about all the... Continue Reading

“Shall I compare thee to a standard normal distribution?
Thou art more symmetric and more bell-shaped…”
— Melvin Shakespeare (William’s lesser-known statistician
brother)
The Greek philosopher Aristotle believed that symmetry was one
of the primary elements of the universal ideal of beauty. Over 2000
years later, emerging research seems to bear him out.
Studies suggest we tend to be more... Continue Reading

In
college I had a friend who could go anywhere and fit right
in. He'd have lunch with a group of professors, then play
hacky-sack with the hippies in the park, and later that evening
he'd hang out with the local bikers at the toughest bar in the
city. Next day he'd play pickup football with the jocks before
going to an all-night LAN party with his gamer pals. On an average
weekend he might catch... Continue Reading

In my previous post, you learned
how to prepare your data for capability analysis in Minitab.
Now let's see where we need to go in the statistical software to
run the correct Capability Analysis.
When it comes to capability analysis, Minitab offers a few
different choices. We offer Normal Capability Analysis for when
your data follow a normal distribution. If your data follow a
different... Continue Reading

Recently I've been refreshing my knowledge of reliability
analysis, which is the use of data to assess a product's ability to
perform over time. Quality engineers typically use reliability
analysis to predict the likelihood that a certain percentage of
products will fail over a given amount of
time.
Statistical software will do the calculations involved in a
reliability analysis, but there's a... Continue Reading

My wife and I are expecting a baby girl soon—very soon, in fact,
as in "Will this blog post be published before the baby is
born?" soon. The due date given is May 19th, but we stat geeks
know that a point estimate just isn't good enough...we want
probability intervals that reflect the uncertainty in the
data.
I found a chart that lets me know the number of babies born to
"spontaneous labor" by each... Continue Reading

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 webinarson different statistical topics. Let’s suppose we want to
evaluate if certain webinar topics are more popular than others, so
we... Continue Reading

Many
statistical analyses require an assumption of normality. In cases
when your data are not normal, sometimes you can apply a function
to make your data approximately normal so that you can complete
your analysis.
If you've seen any of the Transformers movies, you know
that these extraordinary robots can, with some Hollywood magic,
turn themselves into apparently normal items like cars... Continue Reading

In my previous post, we identified the distribution of the
body fat data. Today, we're going to explore
several benefits of knowing the distribution, with a special
emphasis on creating informative graphs! After all, if you are not
sure what a specific distribution with such and such parameters
looks like, a graph gives you the picture!
Using the Distribution Information
So far, we have identified... Continue Reading

I love all data, whether it’s normally distributed or downright
bizarre. However, many people are more comfortable with the
symmetric, bell-shaped curve of a normal distribution. It is not as
intuitive to understand a Gamma distribution, with its shape and
scale parameters, as it is to understand the familiar Normal
distribution with its mean and standard deviation.
However, it's a fact of life... Continue Reading

What,
it’s still not March? Blasted February, why won't you just
end already! Oh well, at least it gives us time for some more data
analysis.
In my last post, I used Minitab’s Fitted Line Plot
to create a regression model that predicted the probability of a
home team winning a basketball game based on the difference in
ranks between the two teams. This model had an r-squared value of
95.2%, which... Continue Reading