Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

As your customers demand more and organizations fight to stay competitive, the battle to create more value with fewer resources is ever present. How can you leverage Lean tools to thrive in your business?

In
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
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

In Part 1 of Gauging Gage, I looked at how adequate a
sampling of 10 parts is for a Gage R&R Study and providing
some advice based on the results.
Now I want to turn my attention to the other two factors in the
standard Gage experiment: 3 operators and 2 replicates.
Specifically, what if instead of increasing the number of parts in
the experiment (my previous post demonstrated you would need... Continue Reading

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
distribution.
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
times."
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

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

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

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

Did
you know the most popular diamond cut is probably the Round
Brilliant Cut? The first early version of what would become the
modern Round Brilliant Diamond Cut was introduced by an Italian
named Vincent Peruzzi, sometime in the late 17th century. In
the early 1900s, the angles for an "ideal" diamond cut were
designed by Marcel Tolkowsky. Minor changes have been made
since then, but the angles... Continue Reading

B'gosh
n' begorrah, it's St. Patrick's Day today!
The day that we Americans lay claim to our Irish heritage by
doing all sorts of things that Irish people never do. Like dye your
hair green. Or tell everyone what percentage Irish you are.
Despite my given name, I'm only about 15% Irish. So my Irish
portion weighs about 25 pounds. It could be the portion that hangs
over my belt due to excess potatoes... 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

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
control.
So
if... 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