In this post, I’ll address some common questions we’ve received
in technical support about
the difference between fitted and data means, where to find each
option within Minitab, and how Minitab calculates each.
let’s look at some definitions. It’s useful to have an example, so
I’ll be using the Light Output data set from Minitab’s Data Set
Library, which includes a description of the sample... 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
There are many reasons why a distribution might not be
normal/Gaussian. A non-normal pattern might be caused by several
distributions being mixed together, or by a drift in time, or by
one or several outliers, or by an asymmetrical behavior, some
out-of-control points, etc.
I recently collected the scores of three different teams (the
Blue team, the Yellow team and the Pink team) after a laser... Continue Reading
Back when I was an undergrad in
statistics, I unfortunately spent an entire semester of my life
taking a class, diligently crunching numbers with my TI-82, before
realizing 1) that I was actually in an Analysis of Variance (ANOVA)
class, 2) why I would want to use such a tool in the first place,
and 3) that ANOVA doesn’t necessarily tell you a thing about
Fortunately, I've had a lot more... 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
approaches, you are probably taking the necessary steps to protect
yourself from the various ghosts, goblins, and witches that are prowling
around. Monsters of all sorts are out to get you, unless they’re
sufficiently bribed with candy offerings!
I’m here to warn you about a ghoul that all statisticians and
data scientists need to be aware of: phantom degrees of freedom.
These phantoms... 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
Speaker John Boehner resigning, Kevin McCarthy quitting before the
vote for him to be Speaker, and a possible government shutdown in
the works, the Freedom Caucus has certainly been in the news
frequently! Depending on your political bent, the Freedom Caucus
has caused quite a disruption for either good or bad.
Who are these politicians? The Freedom Caucus is a group of
approximately 40... Continue Reading
You've collected a bunch of
data. It wasn't easy, but you did it. Yep, there it is, right
there...just look at all those numbers, right there in neat columns
and rows. Congratulations.
I hate to ask...but what are you
going to do with your data?
If you're not sure precisely
what to do with the data you've got, graphing it is a
great way to get some valuable insight and direction. And a good
graph to... 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
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
To make objective
decisions about the processes that are critical to your
organization, you often need to examine categorical data. You may
know how to use a t-test or ANOVA when you’re comparing measurement
data (like weight, length, revenue, and so on), but do you know how to compare
attribute or counts data? It easy to do with statistical software
One person may look at
this bar... Continue Reading
Just 100 years ago,
very few statistical tools were available and the field was largely
unknown. Since then, there has been an explosion of tools
available, as well as ever-increasing awareness and use of
most readers of the Minitab Blog are looking to pick up new tools
or improve their use of commonly-applied ones, I thought it would
be worth stepping back and talking about one... Continue Reading
If you've read the first two
parts of this tale, you know
it started when I published a post that involved transforming
data for capability analysis. When an astute reader asked why
Minitab didn't seem to transform the data outside of the capability
analysis, it revealed
an oversight that invalidated the original
removed the errant post. But to my
surprise, the reader who helped me... Continue Reading
The line plot is an incredibly
agile but frequently overlooked tool in the quest to better
understand your processes.
In any process, whether it's baking a cake or processing loan
forms, many factors have the potential to affect the outcome.
Changing the source of raw
materials could affect the strength of plywood a factory produces.
Similarly, one method of gluing this plywood might be better... Continue Reading
Since Minitab 17 Statistical
Software launched in February 2014, we've gotten
great feedback from many people have been using the General Linear
Model and Regression tools.
But in speaking with people as part of Minitab's Technical
Support team, I've found many are noticing that there are two
coding schemes available with each. We frequently get calls from
people asking how the coding scheme you... Continue Reading
By Erwin Gijzen, Guest Blogger
my previous post, we assessed the out-of-spec level for a
process with capability analysis and visualized process variability
using a control chart. Our goal is to reduce variability, but when
a process has a multitude of categorical and continuous variables,
identifying root causes can be a huge challenge. Analyzing
covariance—using the statistical technique... Continue Reading
by Erwin Gijzen, Guest
People who work in quality improvement know that the root causes
of quality issues are hard to find. A typical production process
can contain hundreds of potential causes. Additionally, companies
often produce products with multiple quality requirements, such as
dimensions, surface appearance, and impact resistance.
With so many variables, it’s no wonder many companies... Continue Reading
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