As someone who has collected and analyzed real data for a
living, the idea of using simulated data for a Monte Carlo
simulation sounds a bit odd. How can you improve a real product
with simulated data? In this post, I’ll help you understand the
methods behind Monte Carlo simulation and walk you through a
simulation example using Companion by Minitab.
Companion by Minitab is a software platform that... Continue Reading
you ever tried to install ventilated shelving in a closet?
You know: the heavy-duty, white- or gray-colored vinyl-coated wire
shelving? The one that allows you to get organized, more efficient
with space, and is strong and maintenance-free? Yep, that’s the
one. Did I mention this stuff is strong? As in,
really hard to cut?
It seems like a simple 4-step project. Measure the closet, go
the... Continue Reading
shopping. For some, it's the most dreaded household activity. For
others, it's fun, or perhaps just a “necessary evil.”
Personally, I enjoy it! My co-worker, Ginger, a content manager
here at Minitab, opened my eyes to something that made me love
grocery shopping even more: she shared the data behind her family’s
shopping trips. Being something of a data nerd, I really geeked out
over the... Continue Reading
you regularly perform regression analysis, you know that
R2 is a statistic used to evaluate the fit of your
model. You may even know the standard definition of R2:
the percentage of variation in the response that is explained
by the model.
Fair enough. With Minitab Statistical Software doing all the heavy
lifting to calculate your R2 values, that may be all you
ever need to know.
But if you’re... Continue Reading
Parts 1 and
2 of Gauging Gage we looked at the numbers of parts, operators,
and replicates used in a Gage R&R Study and how accurately we
could estimate %Contribution based on the choice for each. In
doing so, I hoped to provide you with valuable and interesting
information, but mostly I hoped to make you like me. I mean
like me so much that if I told you that you were doing... Continue Reading
Earlier, I wrote about the
different types of data statisticians typically encounter. In
this post, we're going to look at why, when given a choice in the
matter, we prefer to analyze continuous data rather than
categorical/attribute or discrete data.
As a reminder, when we assign something to a group or give it a
name, we have created attribute or
categorical data. If we count something,
like... Continue Reading
You run a capability analysis
and your Cpk is bad. Now what?
First, let’s start by defining
what “bad” is. In simple terms, the smaller the Cpk, the more
defects you have. So the larger your Cpk is, the
practitioners use a Cpk of 1.33 as the gold standard, so we’ll
treat that as the gold standard here, too.
Suppose we collect some data and run a capability analysis using
by Kevin Clay, guest blogger
In transactional or service processes, we often deal with
lead-time data, and usually that data does not follow the normal
Consider a Lean Six Sigma project to reduce the lead time
required to install an information technology solution at a
customer site. It should take no more than 30 days—working 10 hours
per day Monday–Friday—to complete, test and... Continue Reading
"You take 10 parts and have 3 operators measure each 2
This standard approach to a Gage R&R experiment is so
common, so accepted, so ubiquitous that few people ever question
whether it is effective. Obviously one could look at whether
3 is an adequate number of operators or 2 an adequate number of
replicates, but in this first of a series of posts about
"Gauging Gage," I want to look at... Continue Reading
In Part 1 of this blog series, I
compared Six Sigma to a diamond because both are valuable, have
many facets and have withstood the test of time. I also explained
how the term “Six Sigma” can be used to summarize a variety of
concepts, including philosophy, tools, methodology, or metrics. In
this post, I’ll explain short/long-term variation and
between/within-subgroup variation and how they help... Continue Reading
In my last post, I wrote about
making a cluttered data set easier to work with by removing
unneeded columns entirely, and by displaying just those columns you
want to work with now. But
too much unneeded data isn't always the problem.
What can you do when someone
gives you data that isn't organized the way you need it to be?
That happens for a variety of
reasons, but most often it's because the... Continue Reading
In its industry guidance to companies that manufacture drugs and
biological products for people and animals,
the Food and Drug Administration (FDA) recommends three stages for
Process Qualification, and Continued Process Verification. In
this post, we we will focus on that third stage.
Stage 3: Continued Process Verification
Per the FDA guidelines, the goal of... Continue Reading
People can make mistakes when they test a hypothesis with
statistical analysis. Specifically, they can make either Type I or
Type II errors.
As you analyze your own data and test hypotheses, understanding
the difference between Type I and Type II errors is extremely
important, because there's a risk of making each type of error in
every analysis, and the amount of risk is in your
if... Continue Reading
A recent discussion on the Minitab
Network on LinkedIn pertained to the I-MR chart. In the
course of the conversation, a couple of people referred to it as
"The Swiss Army Knife of control charts," and that's a pretty great
description. You might be able to find more specific tools for
specific applications, but in many cases, the I-MR chart gets the
job done quite adequately.
When you're... Continue Reading
now I’m enjoying my daily dose of morning joe. As the steam rises
off the cup, the dark rich liquid triggers a powerful enzyme
cascade that jump-starts my brain and central nervous system,
delivering potent glints of perspicacity into the dark crevices of
my still-dormant consciousness.
Feels good, yeah! But is it good for me? Let’s see what the
Drinking more than 4 cups of coffee... Continue Reading
Statistics can be challenging, especially if you're not
analyzing data and interpreting the results every day. Statistical
software makes things easier by handling the arduous
mathematical work involved in statistics. But ultimately, we're
responsible for correctly interpreting and communicating what the
results of our analyses show.
The p-value is probably the most frequently cited
statistic. We... Continue Reading
As a person who loves baking (and eating) cakes, I find it
bothersome to go through all the effort of baking a cake when the
end result is too dry for my taste. For that reason, I decided to
use a designed experiment in Minitab to help me reduce the moisture
loss in baked chocolate cakes, and find the optimal settings of my
input factors to produce a moist baked chocolate cake. I’ll share
the... Continue Reading
Histograms are one of the
most common graphs used to display numeric data. Anyone who
takes a statistics course is likely to learn about the histogram,
and for good reason: histograms are easy to understand and can
instantly tell you a lot about your data.
Here are three of the most important things you can learn by
looking at a histogram.
Shape—Mirror, Mirror, On the Wall…
If the left side of a... Continue Reading
by Matthew Barsalou, guest
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
Genichi Taguchi is famous for his pioneering methods of robust
quality engineering. One of the major contributions that he made to
quality improvement methods is Taguchi designs.
Designed experiments were first used by agronomists during
the last century. This method seemed highly theoretical at first,
and was initially restricted to agronomy. Taguchi made the designed
experiment approach more... Continue Reading
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