How lucrative stocks are in the long run is not only dependent on the length of the investment period but even more on the actual date the investment starts and ends!
Return Triangle Plots are a great way to visualize this phenomenon. If you want to learn more about them and how to create them with R read on!
Continue reading “Create Return Triangle Plots with R”
COVID-19 has the world more than ever in its grip – but there is hope: several vaccines have been developed which promise to deliver “95% efficacy”.
When people read this many assume that it means that 95% of vaccinated persons will be protected from infection – but that is not true. Even many (science) journalists get it wrong! If you want to understand what it really means, read on!
Continue reading “COVID-19 vaccine “95% effective”: It doesn’t mean what you think it means!”
We already had a lot of examples that make use of the
OneR package (on CRAN), which can be found in the respective Category: OneR.
Here we will give you some concrete examples in the area of research on Type 2 Diabetes Mellitus (DM) to show that the package is especially well suited in the field of medical research, so read on!
Continue reading “OneR in Medical Research: Finding Leading Symptoms, Main Predictors and Cut-Off Points”
We already covered Neural Networks and Logistic Regression in this blog.
If you want to gain an even deeper understanding of the fascinating connection between those two popular machine learning techniques read on!
Continue reading “Logistic Regression as the Smallest Possible Neural Network”
One of my starting points into quantitive finance was Bernie Madoff’s fund. Back then because Bernie was in desperate need of money to keep his Ponzi scheme running there existed several so-called feeder funds.
One of them happened to approach me to offer me a once in a lifetime investment opportunity. Or so it seemed. Now, there is this old saying that when something seems too good to be true it probably is. If you want to learn what Benford’s law is and how to apply it to uncover fraud, read on!
Continue reading “How to Catch a Thief: Unmasking Madoff’s Ponzi Scheme with Benford’s Law”
How can the Normal Distribution arise out of a completely symmetric set-up? The so-called Central Limit Theorem (CLT) is a fascinating example that demonstrates such behaviour. If you want to get some intuition on what lies at the core of many statistical tests, read on!
Continue reading “The Central Limit Theorem (CLT): From Perfect Symmetry to the Normal Distribution”
In From Coin Tosses to p-Hacking: Make Statistics Significant Again! I explained the general principles behind statistical testing, here I will give you a simple method that you could use for quick calculations to check whether something fishy is going on (i.e. a fast statistical BS detector), so read on!
Continue reading “3.84 or: How to Detect BS (Fast)”
Networks are everywhere: traffic infrastructure and the internet come to mind, but networks are also in nature: food chains, protein-interaction networks, genetic interaction networks and of course neural networks which are being modelled by Artificial Neural Networks.
In this post, we will create a small network (also called graph mathematically) and ask some question about which is the “most important” node (also called vertex, pl. vertices). If you want to understand important concepts of network centrality and how to calculate those in R, read on!
Continue reading “Network Analysis: Who is the Most Important Influencer?”
Do you cheat on your partner? Do you take drugs? Are you gay? Are you an atheist? Did you have an abortion? Will you vote for the right-wing candidate? Not all people feel comfortable answering those kinds of questions in every situation honestly.
So, is there a method to find the respective proportion of people without putting them on the spot? Actually, there is! If you want to learn about randomized response (and how to create flowcharts in R along the way) read on!
Continue reading “Local Differential Privacy: Getting Honest Answers on Embarrassing Questions”
The Kalman filter is a very powerful algorithm to optimally include uncertain information from a dynamically changing system to come up with the best educated guess about the current state of the system. Applications include (car) navigation and stock forecasting. If you want to understand how a Kalman filter works and build a toy example in R, read on!
Continue reading “Kalman Filter as a Form of Bayesian Updating”