The language of statistics is a funny thing, but there usually
isn't much to laugh at in the consequences that can follow when
misunderstandings occur between statisticians and
non-statisticians. We see these consequences frequently in the
media, when new studies—that usually contradict previous ones—are
breathlessly related, as if their findings were incontrovertible
Similar, though less... Continue Reading
The line plot is an incredibly
agile but frequently overlooked tool in the quest to better
understand your processes.
In any process, whether it's baking a cake or processing loan
forms, many factors have the potential to affect the outcome.
Changing the source of raw
materials could affect the strength of plywood a factory produces.
Similarly, one method of gluing this plywood might be better... Continue Reading
If you’re familiar with Lean Six Sigma, then you’re familiar
DMAIC is the acronym for Define, Measure, Analyze, Improve and
Control. This proven problem-solving strategy provides a structured
5-phase framework to follow when working on an improvement
This is the first post in a five-part series that focuses on the
tools available in Minitab Statistical
Software that are most... Continue Reading
2016 comes to a close, it’s time to reflect on the passage of time
and changes. As I’m sure you’ve guessed, I love statistics and
analyzing data! I also love talking and writing about it. In fact,
I’ve been writing statistical blog posts for over five years, and
it’s been an absolute blast. John Tukey, the renowned statistician,
once said, “The best thing about being a statistician... Continue Reading
week we’re celebrating the annual Thanksgiving holiday in the
United States, which is not only a good time to reflect on the
things we’re grateful for, but it’s also a good time to stuff
yourself with turkey, mashed potatoes, green bean casserole, and
the usual suspects that find their way to the Thanksgiving
While I’m of course very thankful for my family, friends, home,
etc., I’m also... Continue Reading
In this day and age, it’s not uncommon that data entry errors
occur in data sets that are so large that looking for and
correcting the errors by hand is impractical. Fortunately, Minitab
includes tools that make it easy to get your data into shape, so
that you can proceed to getting the answers you need.
Let’s say, for example, that you were going to look at the
Density Database. It’s... Continue Reading
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
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
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
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
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
performed multiple linear regression and have settled on a model
which contains several predictor variables that are statistically
significant. At this point, it’s common to ask, “Which variable is
This question is more complicated than it first appears. For one
thing, how you define “most important” often depends on your
subject area and goals. For another, how you collect... Continue Reading
If you’re in the market for statistical software, there are many
considerations and more than a few options for you to evaluate.
Check out these seven questions to ask yourself before choosing
statistical software—your answers should help guide you towards the
best solution for your needs!
1. Who uses statistical software in your organization?
Are they expert statisticians, novices, or a mix of both?... Continue Reading
In regression, "sums of squares" are used to represent
variation. In this post, we’ll use some sample data to walk through
sample data used in this post is available within Minitab by
choosing Help > Sample Data,
or File > Open Worksheet >
Look in Minitab Sample Data folder (depending on
your version of Minitab). The dataset is called
ResearcherSalary.MTW, and contains data... Continue Reading
So the data you nurtured, that you worked so hard to format and
make useful, failed the normality test.
Time to face the truth: despite your best efforts, that data set
is never going to measure up to the assumption you may
have been trained to fervently look for.
Your data's lack of normality seems to make it poorly suited for
analysis. Now what?
Take it easy. Don't get uptight. Just let your data... Continue Reading
Often, when we start analyzing
new data, one of the very first things we look at is whether
certain pairs of variables are correlated. Correlation can tell if two variables have a
linear relationship, and the strength of that
makes sense as a starting point, since we're usually looking for
relationships and correlation is an easy way to get a quick handle
on the data set we're... Continue Reading
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