At the inaugural Minitab Insights Conference in September,
presenters Benjamin Turcan and Jennifer Berner discussed
how to present data effectively. Among the considerations they
discussed was choosing the right graph.
Different graphs are good for different things. Of course,
opinions about which graph is best can, and do, differ. Dotplot
devotees might decide that they are demonstrably... Continue Reading
In Part 1 of this
blog series, I wrote about how statistical inference uses data
from a sample of individuals to reach conclusions about the whole
population. That’s a very powerful tool, but you must check your
assumptions when you make statistical inferences. Violating any of
these assumptions can result in false positives or false negatives,
thus invalidating your results.
The common data... Continue Reading
If your work involves quality improvement, you've at least
heard of Design of Experiments (DOE). You probably know
it's the most efficient way to optimize and improve your process.
But many of us find DOE intimidating, especially if it's not a tool
we use often. How do you select an appropriate design, and ensure
you've got the right number of factors and levels? And after you've
gathered your... Continue Reading
No matter how experienced you are at analyzing data,
communicating about your results can be a tremendous challenge. So
it's not surprising that "Effectively Reporting Your Data Analysis"
was one of the best-attended sessions at the inaugural Minitab
Insights Conference last month.
The presenters, Benjamin Turcan and Jennifer Berner of First
Niagara Bank, have a great deal of experience improving...Continue Reading
Statistical inference uses data from a sample of individuals to
reach conclusions about the whole population. It’s a very
powerful tool. But as the saying goes, “With great
power comes great responsibility!” When attempting to make
inferences from sample data, you must check your assumptions.
Violating any of these assumptions can result in false positives or
false negatives, thus invalidating... Continue Reading
Data mining can be helpful in the exploratory phase of an
analysis. If you're in the early stages and you're just figuring
out which predictors are potentially correlated with your response
variable, data mining can help you identify candidates. However,
there are problems associated with using data mining to select
In my previous post, we used data mining to settle on
the following... Continue Reading
Since the release of Minitab
Express in 2014, we’ve often received questions in technical
support about the differences between Express and Minitab 17.
In this post, I’ll attempt to provide a comparison between these
two Minitab products.
What Is Minitab 17?
Minitab 17 is an all-in-one graphical and statistical analysis
package that includes basic analysis tools such as hypothesis
testing,... Continue Reading
A reader asked a great
question in response to a post I wrote
about Pareto charts. Our readers typically do ask great questions,
but this one turned out to be more difficult to answer than it
My correspondent wrote:
My understanding is that when you have count data, a
bar chart is the way to go. The gaps between the bars emphasize
that the data are not measured on a continuous scale.... Continue Reading
The ultimate goal of most quality improvement projects is clear:
reducing the number of defects, improving a response, or making a
change that benefits your customers.
We often want to jump right in and start gathering and analyzing
data so we can solve the problems. Checking your measurement
systems first, with methods like attribute agreement analysis or
Gage R&R, may seem like a needless waste... Continue Reading
We hosted our first-ever Minitab Insights conference in
September, and if you were among the attendees, you already know
the caliber of the speakers and the value of the information they
shared. Experts from a wide range of industries offered a lot of
great lessons about how they use data analysis to improve business
practices and solve a variety of problems.
I blogged earlier about five key...Continue Reading
watched an old motorcycle flick from the 1960s the other night, and I
was struck by the bikers' slang. They had a language all their own.
Just like statisticians, whose manner of speaking often confounds
those who aren't hep to the lingo of data analysis.
It got me thinking...what if there were an all-statistician
biker gang? Call them the Nulls Angels. Imagine them in their
colors, tearing... Continue Reading
If you were among the 300 people who attended the first-ever
Minitab Insights conference in September, you already know how
powerful it was. Attendees learned how practitioners from a
wide range of industries use data analysis to address a variety of
problems, find solutions, and improve business practices.
In the coming weeks and months, we will share more of the great
insights and guidance shared... Continue Reading
Face it, you love regression analysis as much as I do.
Regression is one of the most satisfying analyses in Minitab:
get some predictors that should have a relationship to a response,
go through a model selection process, interpret fit statistics like
adjusted R2 and predicted R2, and make
predictions. Yes, regression really is quite wonderful.
Except when it’s not. Dark, seedy corners of the data... Continue Reading
We’ve got a plethora of case studies showing how businesses from different
industries solve problems and implement solutions with data
analysis. Take a look for ideas about how you can use data analysis
to ensure excellence at your business!
Boston Scientific, one of the world’s leading developers of
medical devices, is just one organization who has shared their
story. A team at their Heredia,... Continue Reading
True or false: When comparing a parameter for two sets of
measurements, you should always use a hypothesis test to determine
whether the difference is statistically significant.
The answer? (drumroll...) True!
To understand this paradoxical answer, you need to keep in mind
the difference between samples, populations, and descriptive and
Descriptive Statistics and... Continue Reading
mining uses algorithms to explore correlations in data sets. An
automated procedure sorts through large numbers of variables and
includes them in the model based on statistical significance alone.
No thought is given to whether the variables and the signs and
magnitudes of their coefficients make theoretical sense.
We tend to think of data mining in the context of big data, with
its huge... Continue Reading
September 16, is World Ozone Day. You don't hear much about the
ozone layer any more.
In fact, if you’re under 30, you might think this is just
another trivial, obscure observance, along the lines of International Dot Day (yesterday) or National Apple Dumpling Day (tomorrow).
But there’s a good reason that, almost 30 years ago, the United
Nations designated today to as a day to raise... Continue Reading
I confess: I'm not a natural-born decision-maker. Some people—my
wife, for example—can assess even very complex situations, consider
the options, and confidently choose a way forward. Me? I get
anxious about deciding what to eat for lunch. So you can imagine
what it used to be like when I
needed to confront a really big decision or problem. My approach,
to paraphrase the Byrds, was "Re:... Continue Reading
To assess if a process is stable and in statistical control, you
can use a control
chart. It lets you answer the question "is the process that you
see today going to be similar to the process that you see
tomorrow?" To assess and quantify how well your process falls
within specification limits, you can use capability
Both of these tools are easy to use in Minitab, but you
first need to... Continue Reading
There may be huge potential benefits waiting in the data in your
servers. These data may be used for many different purposes. Better
data allows better decisions, of course. Banks, insurance firms,
and telecom companies already own a large amount of data about
their customers. These resources are useful for building a more
personal relationship with each customer.
Some organizations already use... Continue Reading
Minitab is the leading provider of software and services for quality
improvement and statistics education. More than 90% of Fortune 100 companies
use Minitab Statistical Software, our flagship product, and more students
worldwide have used Minitab to learn statistics than any other package.
Minitab Inc. is a privately owned company headquartered in State College,
Pennsylvania, with subsidiaries in the United Kingdom, France, and
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countries around the world.