If you need to assess process
performance relative to some specification limit(s),
capability is the tool to use. You collect some accurate
data from a stable process, enter those measurements in Minitab,
and then choose Stat > Quality Tools >
Capability Analysis/Sixpack or Assistant
> Capability Analysis.
Now, what about sorting the data?
I’ve been asked “why does Cpk change when I... Continue Reading
How deeply has statistical content from Minitab blog posts (or
other sources) seeped into your brain tissue? Rather than submit a
biopsy specimen from your temporal lobe for analysis, take this
short quiz to find out. Each question may have more than one
correct answer. Good luck!
of the following are famous figure skating pairs, and which are
methods for testing whether your data follow a... Continue Reading
Having delivered training courses on
capability analyses with Minitab, several times, I have noticed
that one question you can be absolutely sure will be asked, during
the course, is: What is the difference between the Cpk
and the Ppk indices?
Ppk vs. Cpk indices
The terms Cpk and Ppk are often confused,
so that when quality or process engineers refer to the
Cpk index, they often actually intend to... Continue Reading
Control charts are a fantastic tool. These charts plot your
process data to identify common cause and special cause variation.
By identifying the different causes of variation, you can take
action on your process without over-controlling it.
Assessing the stability of a process can help you determine
whether there is a problem and identify the source of the problem.
Is the mean too high, too low,... Continue Reading
Don't be a grumpy cat when something on your capability report
doesn't smell right. After
pressing that OK button to run your analysis, allow your inner cat to
understand how and why certain statistics are being used. To help
you along, here are some capability issues that customers have
brought up recently.
Cp is missing
You’ve generated a capability analysis report with the Johnson
transformation... 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
you know that November is World Quality Month? The American Society
for Quality is once again heading up this year’s festivities.
Throughout the month of November, ASQ will be promoting the use
of quality tools in businesses, communities, and institutions all
over the world. You can check it out at http://asq.org/world-quality-month/.
Here at Minitab, we’re also pretty excited about World... Continue Reading
By Matthew Barsalou, guest
Teaching process performance and capability studies is easier
when actual process data is available for the student or trainee to
practice with. As I have previously
discussed at the Minitab Blog, a catapult can be used to
generate data for a capability study. My last blog on using a
catapult for this purspose was several years ago, so I would like
to revisit... Continue Reading
I read trade publications that cover everything from banking to
biotech, looking for interesting perspectives on data analysis and
statistics, especially where it pertains to quality
Recently I read a great blog post from Tony Taylor, an analytical
chemist with a background in pharmaceuticals. In it, he discusses
the implications of the FDA's updated guidance for industry analytical...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
Whatever industry you're in, you're going to need to buy
supplies. If you're a printer, you'll need to purchase inks,
various types of printing equipment, and paper. If you're in
manufacturing, you'll need to obtain parts that you don't make
But how do you know you're making the right choice when you have
multiple suppliers vying to fulfill your orders? How can you
be sure you're... Continue Reading
Last time, I told you how I had double-checked the analysis in
a post that involved running the Johnson transformation on a set of
data before doing normal capability analysis on it. A reader asked why the transformation didn't
work on the data when you applied it outside of the capability
I hadn't tried transforming the data that way, but if the transformation worked when performed
as... Continue Reading
I don't like the taste
of crow. That's a shame, because I'm about to eat a huge helping of
I'm going to tell you
how I messed up an analysis. But in the process, I learned some new
lessons and was reminded of some older ones I should remember to
apply more carefully.
This Failure Starts in
My mistake originated in the
2015 Triple Crown victory of American Pharoah. I'm no... Continue Reading
Before I joined Minitab, I worked for many years in Penn State's
College of Agricultural Sciences as a writer and editor. I
frequently wrote about food science and particularly food safety,
as I regularly needed to report on the research being conducted by
Penn State's food safety experts, and also edited course materials
and bulletins for professionals and consumers about ensuring they
had safe... Continue Reading
When data are collected in subgroups, it’s easy to understand
how the variation can be calculated within each of the subgroups
based the subgroup range or the subgroup standard deviation.
When data is not collected in subgroups (so the subgroup size is
1), it may be a little less intuitive to understand how
within-subgroup standard deviation is calculated. How does
Minitab Statistical Softwarecalcu... 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
previous posts, I discussed the results of a recycling project done
by Six Sigma students at Rose-Hulman Institute of Technology last
spring. (If you’re playing catch up, you can read Part I and Part II.)
The students did an awesome job reducing the amount of recycling
that was thrown into the normal trash cans across all of the
institution’s academic buildings. At the end of the spring... 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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