Blog posts and articles about using Minitab Statistical Software in Six Sigma and other quality projects.

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

Have
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

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If
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

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
better. Many
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
Minitab
Statisti... Continue Reading

Everyone who analyzes data regularly has the experience of
getting a worksheet that just isn't ready to use. Previously I
wrote about tools you can use to
clean up and eliminate clutter in your data and
reorganize your data.
In this post, I'm going to
highlight tools that help you get the most out of messy data by
altering its characteristics.
Know Your Options
Many problems with data don't become... 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

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

Isn't it great when you get a set of data and it's perfectly
organized and ready for you to analyze? I love it when the people
who collect the data take special care to make sure to format it
consistently, arrange it correctly, and eliminate the junk,
clutter, and useless information I don't need.
You've
never received a data set in such perfect condition, you say?
Yeah, me neither. But I can... Continue Reading

Predictions
can be a tricky thing. Consider trying to predict the number rolled
by 2 six-sided dice. We know that 7 is the most likely outcome. We
know the exact probability each number has of being rolled. If we
rolled the dice 100 times, we could calculate the expected value
for the number of times each value would be rolled. However, even
with all that information, we can't definitively predict... 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 validation:
Process Design,
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

Welcome to the Hypothesis Test Casino! The featured game of the
house is roulette. But this is no ordinary game of
roulette. This is p-value roulette!
Here’s how it works: We have two roulette wheels, the Null wheel
and the Alternative wheel. Each wheel has 20 slots (instead of the
usual 37 or 38). You get to bet on one slot.
What happens if the ball lands in the slot you bet on? Well,
that depends... Continue Reading

Like
many, my introduction to 17th-century French philosophy came at the
tender age of 3+. For that is when I discovered the
Etch-a-Sketch®, an entertaining ode to Descartes' coordinate plane.
Little did I know that the seemingly idle hours I spent doodling
on my Etch-a-Sketch would prove to be excellent training for the
feat that I attempt today: plotting an Empirical Cumulative
Distribution... Continue Reading

My colleague Cody Steele wrote a post that
illustrated how
the same set of data can appear to support two contradictory
positions. He showed how changing the scale of a graph that
displays mean and median household income over time drastically
alters the way it can be interpreted, even though there's no change
in the data being presented.
When we analyze data, we need to present the results in... 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

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

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
like Minitab.
One person may look at
this bar... 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

Have you ever wanted to know the odds of something happening, or
not happening?
It's the kind of question that students are frequently asked to
calculate by hand in introductory statistics classes, and going
through that exercise is a good way to become familiar with the
mathematical formulas the underlie probability (and hence, all of
statistics).
But let's be honest: when class is over, most... 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