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

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

Minitab 17 gives you the confidence you need to improve quality.

Download the Free Trial
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

I got lost a lot as a child. I got lost at malls, at museums,
Christmas markets, and everywhere else you could think of. Had it
been in fashion to tether children to their parents at the time,
I'm sure my mother would have. As an adult, I've gotten used to
using a GPS device to keep me from getting lost.
The Assistant in
Minitab is like your GPS for statistics. The Assistant is there to
provide you... Continue Reading

Using
statistical techniques to optimize manufacturing processes is
quite common now, but using the same approach on social topics is
still an innovative approach. For example, if our objective is to
improve student academic performances, should we increase teachers
wages or would it be better to reduce the number of students
in a class?
Many social
topics (the effect of increasing the minimum... Continue Reading

Previously, I showed why there is no R-squared for nonlinear regression. Anyone
who uses nonlinear regression will also notice that there are no P
values for the predictor variables. What’s going on?
Just like there are good reasons not to calculate R-squared for
nonlinear regression, there are also good reasons not to calculate
P values for the coefficients.
Why not—and what to use instead—are the... Continue Reading

by The Discrete Sharer, guest blogger
As Minitab users, many of us have found
staged control charts to be an effective tool to
quantify and demonstrate the “before and after” state of our
process improvement activities.
However, have you ever considered using them to demonstrate the
effects of changes to compensation/incentive plans for your
employees?
Here's an example of how a mid-sized... Continue Reading

Screening experimental designs allow you to study a very large
number of factors in a very limited number of runs. The objective
is to focus on the few factors that have a real effect and
eliminate the effects that are not significant. This is often the
initial typical objective of any experimenter when a
DOE (design of experiments) is performed.
Table of Factorial Designs
Consider the table below.... Continue Reading

In
Part I and
Part II we learned about the experiment and the survey,
respectively. Now we turn our attention to the results...
Our
first two participants, Danielle and Sheryl, enter the conference
room and are given blindfolds as we explain how the experiment will
proceed. As we administer the tasting, the colors of the wine
are obvious but we don't know the true types, which have been
masked... Continue Reading

In
Blind Wine Part I, we introduced our experimental setup, which
included some survey questions asked ahead of time of each
participant. The four questions asked were:
On a scale of 1 to 10, how would you rate your knowledge of
wine?
How much would you typically spend on a bottle of wine in a
store?
How many different types of wine (merlot, riesling, cabernet,
etc.) would you buy regularly (not as... Continue Reading

After upgrading to the latest
and greatest version of our statistical software, Minitab 17, some
users have contacted tech support to ask "Wait a minute, where is
that Two-Way ANOVA option in Minitab 17?"
The answer is that it’s not
there. That’s right! The 2-Way ANOVA option that was available in
Minitab 16 and prior versions was removed from Minitab 17.Why would this feature be removed from the... Continue Reading

We received the following question via social media
recently:
I am using Minitab 17 for ANOVA.
I calculated the mean and standard deviation for these 15 values,
but the standard deviation is very high. If I delete some values, I
can reduce the standard deviation. Is there an option in Minitab
that will automatically indicate values that are out of range and
delete them so that the standard... Continue Reading

Control charts are some of the most useful tools in statistical
science. They track process statistics over time and detect when
the mean or standard deviation change from what they have been. The
signals that control charts send about special causes can help you
zero in on the fastest ways to improve any process, whether you’re
making tires,
turbines, or
trying to improve patient care.
I’ve
menti... Continue Reading