Blog posts and articles about testing hypotheses with the statistical method called the T-Test.

The
NCAA Tournament is right around the corner, and you know what that
means: It’s time to start thinking about how you’re going to fill
out your bracket! For the last two years I’ve used the Sagarin
Predictor Ratings to predict the tournament. However, there is
a problem with that strategy this year. The old method uses a
regression model that calculates the probability one team has
of beating... Continue Reading

Suppose that you have designed a brand new product with many
improved features that well help create a much better customer
experience. Now you must ensure that it is manufactured
according to the best quality and reliability standards, so that it
gets the excellent long-term reputation it deserves from potential
customers. You need to move quickly and seamlessly from Research
and Development into... Continue Reading

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Hypothesis testing is an essential procedure in statistics. A
hypothesis test evaluates two mutually exclusive statements about a
population to determine which statement is best supported by the
sample data. When we say that a finding is statistically
significant, it’s thanks to a hypothesis test. How do these tests
really work and what does statistical significance actually
mean?
In this series of... Continue Reading

In my previous post, I
showed you
how to set up data collection for a gage R&R analysis using
the Assistant in Minitab 17. In this case, the goal of the gage
R&R study is to test whether a new tool provides an effective
metric for assessing resident supervision in a medical facility.
As noted in that post, I'm
drawing on one of my favorite bloggers about health care quality,
David Kashmer of the... Continue Reading

Right
now I’m enjoying my daily dose of morning joe. As the steam rises
off the cup, the dark rich liquid triggers a powerful enzyme
cascade that jump-starts my brain and central nervous system,
delivering potent glints of perspicacity into the dark crevices of
my still-dormant consciousness.
Feels good, yeah! But is it good for me? Let’s see what the
studies say…
Drinking more than 4 cups of coffee... Continue Reading

It’s safe to say that most people who use statistics are more
familiar with parametric analyses than nonparametric analyses.
Nonparametric tests are also called distribution-free tests because
they don’t assume that your data follow a specific
distribution.
You may have heard that you should use nonparametric tests when
your data don’t meet the assumptions of the parametric test,
especially the... Continue Reading

I left off last with a
post outlining how the Six Sigma students at
Rose-Hulman were working on a project to reduce the amount of
recycling thrown in the normal trash cans in all of the academic
buildings at the institution.
Using the DMAIC methodology for completing improvement
projects, they had already defined the problem at hand: how could
the amount of recycling that’s thrown in the normal trash... Continue Reading

In my experience, one of the
hardest concepts for users to wrap their head around revolves
around the Power and Sample Size menu in Minitab's statistical software, and more specifically, the field that asks
for the "difference" or "difference to detect."
Let’s start with power. In statistics, the definition of power
is the probability that you will correctly reject the null
hypothesis when it is... Continue Reading

by Matthew Barsalou, guest
blogger.
E. E. Doc Smith, one of the greatest authors ever, wrote
many classic books such as The Skylark of Space and
his Lensman series. Doc Smith’s imagination knew no
limits; his Galactic Patrol had millions of combat fleets under its command
and possessed planets turned into movable, armored weapons
platforms. Some of the Galactic Patrol’s weapons may be well... Continue Reading

In my recent meetings with people from various companies in the
service industries, I realized that one of the problems they face
is that they were collecting large amounts of
"qualitative" data: types of product, customer profiles, different
subsidiaries, several customer requirements, etc.
As I discussed in my previous post, one way to look at
qualitative data is to use different types of... Continue Reading

In several previous blogs, I have discussed the use of
statistics for
quality improvement in the service sector. Understandably,
services account for a very large part of the economy. Lately, when
meeting with several people from financial companies, I realized
that one of the problems they faced was that they were
collecting large amounts of "qualitative" data: types of
product, customer... Continue Reading

If you’re not a statistician, looking through statistical output
can sometimes make you feel a bit like Alice in
Wonderland. Suddenly, you step into a fantastical world
where strange and mysterious phantasms appear out of nowhere.
For example, consider the T and P in your t-test results.
“Curiouser and curiouser!” you might exclaim, like Alice, as you
gaze at your output.
What are these values,... Continue Reading

Choosing
the correct linear regression model can be difficult. After all,
the world and how it works is complex. Trying to model it with only
a sample doesn’t make it any easier. In this post, I'll review some
common statistical methods for selecting models, complications you
may face, and provide some practical advice for choosing the best
regression model.
It starts when a researcher wants to... Continue Reading

As a member of Minitab's
Technical Support team, I get the opportunity to work with many
people creating control charts. They know the importance of
monitoring their processes with control charts, but many don’t
realize that they themselves could play a vital role in improving
the effectiveness of the control charts.
In this post I will show you how
to take control of your charts by using Minitab... Continue Reading

"Data! Data! Data! I can't make bricks without clay."
— Sherlock Holmes, in Arthur Conan Doyle's The Adventure
of the Copper Beeches
Whether you're the world's greatest detective trying to crack a
case or a person trying to solve a problem at work, you're going to
need information. Facts. Data, as Sherlock Holmes
says.
But not all data is created equal, especially if you plan to
analyze as part of... Continue Reading

Have you ever had a probability
plot that looks like this?
The probability plot above is based on patient weight (in
pounds) after surgery minus patient weight (again, in pounds)
before surgery.
The red line appears to go through the data, indicating a
good fit to the Normal, but there are clusters of plotting
points at the same measured value. This occurs on a probability
plot when there are many... Continue Reading

Analysis
of variance (ANOVA) is great when you want to compare the
differences between group means. For example, you can use ANOVA to
assess how three different alloys are related to the mean strength
of a product. However, most ANOVA tests assess one response
variable at a time, which can be a big problem in certain
situations. Fortunately, Minitab statistical software offers a... Continue Reading

Using a sample to estimate the properties of an entire population
is common practice in statistics. For example, the mean from a
random sample estimates that parameter for an entire population. In linear
regression analysis, we’re used to the idea that the regression coefficients are estimates of the
true parameters. However, it’s easy to forget that R-squared
(R2) is also an estimate.... Continue Reading

by Jasmin Wong, guest blogger
The combination of statistical methods and injection
moulding simulation software gives manufacturers a
powerful way to predict moulding defects and to
develop a robust moulding process at the part design
phase.
CAE (computer-aided engineering) is widely used in the injection
moulding industry today to improve product and mould designs as
well as to resolve or... Continue Reading

I’ve written about the importance of checking your residual plots when performing
linear regression analysis. If you don’t satisfy the assumptions
for an analysis, you might not be able to trust the results. One of
the assumptions for regression analysis is that the residuals are
normally distributed. Typically, you assess this assumption using
the normal probability plot of the residuals.
Are... Continue Reading