There is more than just the p value in a probability plot—the
overall graphical pattern also provides a great deal of useful
information. Probability plots are a powerful tool to better
understand your data.
In this post, I intend to present the main principles of
probability plots and focus on their visual interpretation using
some real data.
In probability plots, the data density distribution... Continue Reading
In April 2012, I wrote a short paper on
binary logistic regression to analyze wine tasting data. At
that time, François Hollande was about to get elected as French
president and in the U.S., Mitt Romney was winning the Republican
primaries. That seems like a long time ago…
Now, in 2014, Minitab 17 Statistical Softwarehas just been released. Had Minitab 17, been available in 2012,
would have I... Continue Reading
My main objective is to encourage greater use of statistical
techniques in the service sector and present new ways to implement
In a previous blog, I presented an approach you can use to
identify process steps that may be improved in the service sector
analysis). In this post I'll show how
nonparametric distribution analysis may be implemented in the
service sector to analyze... Continue Reading
parts that are truly interchangeable is a critical aspect of modern
manufacturing. The same parts may be manufactured in different
plants spread around the globe or by suppliers located far away.
Parts need to be manufactured to specifications to ensure that they
are almost identical to allow an easy assembly of new products.
Interchangeability is increasingly important in the... Continue Reading
In this post I'll show how we can use a multivariate statistical
analysis (in this case, a factorial analysis) to better understand
data on social progress and economic development. This is a very
simple and practical example of a factorial analysis performed
using Minitab Statistical Software.
Factorial analysis is often considered to be a complex and
advanced statistical technique, but I hope... Continue Reading
value of analyzing data is well established in industries like
manufacturing and mining, but data-driven process and quality
improvement is increasingly being adopted in service industries
like retail sales and healthcare, too. In this blog post, I'll
discuss how a simple data analysis may be used to improve processes
in the service sector.
Suppose we want to improve the way incoming calls are... Continue Reading
Most of the data that one can collect and analyze follow a
normal distribution (the famous bell-shaped curve). In fact, the
formulae and calculations used in many analyses simply take it for
granted that our data follow this distribution; statisticians call
this the "assumption of normality."
For example, our data need to meet the normality assumption
before we can accept the results of a one- or... Continue Reading
When trying to solve complex problems, you should first list all
the suspected variables identify the few critical factors and
separate them from the trivial many, which are not essential to
understanding the cause.
Many statistical tools enable you to efficiently identify the
effects that are statistically significant in order to converge on
the root cause of a problem (for example ANOVA,... Continue Reading
processes are affected by various
sources of variations over time. Products which are designed
based on optimal settings, will, in reality, tend to drift away
from their ideal settings during the manufacturing process.
Environmental fluctuations and process variability often cause
major quality problems. Focusing only on costs and performances is
not enough. Sensitivity to deterioration and... Continue Reading
Design of experiments (DOEs) is a very effective and powerful
statistical tool that can help you understand and improve your
processes, and design better products.
DOE lets you assess the main effects of a process as well as the
interaction effects (the effect of factor A, for example, may be
much larger when factor B is set at a specific level, leading to an
interaction). In science and in... Continue Reading
Suppose that on your way to a summer holiday resort (a very
distant place), your car breaks down. You might just call the
roadside assistance and wait for your car to be towed to a repair
shop. But suppose that you think you are smarter than that, and you
would like to solve the issue by yourself—or at least evaluate the
repair cost. Vehicle breakdowns can occur for a large number of
Intuit... Continue Reading
Macros are very useful for automating and processing sequences
of repetitive tasks. For example, generating periodic reports
(weekly, monthly or quarterly) often becomes a very tedious and
time-consuming activity. The graphs and statistical analyses
contained in these reports need to be updated regularly and are
always the same (although the data change).Fortunately, these repetitive tasks can... Continue Reading
Multivariate statistics can be used to better understand the
structure of large data sets, typically customer-related data.
Suppose you have a large amount of data about your customers
(preferences, degree of satisfaction, expectations,
dislikes etc…), and a large number of variables you need to
Your data might seem somewhat chaotic at first, and you might
consider the use of many different...Continue Reading
I'm Bruno Scibilia. I joined the
Paris-based Minitab team in 2007 as a Technical Support Specialist,
responding to customer queries regarding the installation and use
of Minitab Statistical Software and the project management software
Companion by Minitab.
I now lead a number of the Minitab Public Training courses
I obtained a Ph.D. in Design of Experiments (DOE), and
then worked as a... Continue Reading
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