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
You need to consider many factors when you’re buying a used car.
Once you narrow your choice down to a particular car model, you can
get a wealth of information about individual cars on the market
through the Internet. How do you navigate through it all to find
the best deal? By analyzing the data you have available.
Let's look at how this works using
the Assistant in Minitab Statistical... Continue Reading
Design of Experiments is an extremely
powerful statistical method, and we added a DOE tool to the
Assistant in Minitab to make it more accessible to more
Since it's summer grilling season, I'm
applying the Assistant's DOE tool to outdoor
cooking. Earlier, I showed
to set up a designed experiment that will let you optimize how
you grill steaks.
If you're not already using it and you... Continue Reading
Earlier this month, PLOS.org
published an article titled "Ten Simple Rules for Effective Statistical
10 rules are good reading for anyone who draws conclusions and makes decisions
based on data, whether
you're trying to extend the boundaries of scientific knowledge or
make good decisions for your business.
Carnegie Mellon University's
Robert E. Kass and several co-authors devised... Continue Reading
You often hear the data being
blamed when an analysis is not delivering the answers you wanted or
expected. I was recently reminded that the data chosen or collected
for a specific analysis is determined by the analyst, so there is
no such thing as bad data—only bad
This made me think about the
steps an analyst can take to minimise the risk of producing
analysis that fails to answer... Continue Reading
outlier is an observation in a data set that lies a substantial
distance from other observations. These unusual observations can
have a disproportionate effect on statistical analysis,
such as the mean, which can lead to misleading results.
Outliers can provide useful information about your data or process,
so it's important to investigate them. Of course, you have to find
Finding... Continue Reading
Technology is very much part of
our lives nowadays. We use our smartphones to have video calls with
our friends and family, and watch our favourite TV shows on
tablets. Technology has also transformed the fitness industry with
the increasing popularity of fitness trackers.
Recently, I got myself a fitness watch and it's becoming my
favourite gadget. It can track how many steps I’ve taken, my... Continue Reading
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