This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as…

You may even call it post-doctorship, as the level is beyond the traditional PhD degree. It is not a degree, not competing with university programs, but instead, akin to a fellowship or apprenticeship to learn doing state-of-the-art applied research, discover ground-breaking results or applications, and translate your discoveries into seminal material suitable for a broad audience. It is intended for professionals with substantial experience, perhaps to people who already have a PhD in a…

Here is our selection of featured articles and resources posted in the last few days:

• Weighted Linear Regression in R
• The R Medicare Factor
• First Doctorship in…

This article is by Bill Vorhies.

Summary: We’re stuck.  There hasn’t been a major breakthrough in algorithms in the last year.  Here’s a survey of the leading contenders for that next major advancement.

 …

Here is our selection of featured articles and technical resources posted in the last few days:

Featured Resources and Technical Contributions

• Neural network classification of data using Smile
• Credit Risk…

In my first article on this topic (see here) I introduced some of the complex stochastic processes used by Wall Street data scientists, using a simple approach that can be understood by people with no statistics background other than a first course such as stats 101. I defined and illustrated the continuous Brownian motion (the…

Selected articles and resources posted in the last few days:

Featured Resources and Technical Contributions

• Significance of p-value
• Difference Between Correlation and Regression in Statistics…

Myth #1: You can only do research in an academic setting. Not true. There are plenty of research labs owned by big and small companies and organizations, including government, as well as abroad. In my case, I own and manage my self-funded research lab, publishing in my own niche media outlets (see …

Here is our selection of forum questions, technical contributions and articles posted in the last few days:

Featured Resources and Technical Contributions

• 'Kindof' Big Data in R
• 5 Pro-Tips For Data Scientists To Write Good Code…