My father-in-law used to write down the numbers drawn on the lottery to find patterns, especially whether some numbers were “due” because they hadn’t been drawn for a long time. He is not alone! And don’t they have a point? Shouldn’t the numbers balance after some time? Read on to find out!
Continue reading “Learning Statistics: On Hot, Cool, and Large Numbers”
Some time ago I conducted a poll on LinkedIn that quickly went viral. I asked which of three different coin tossing sequences were more likely and I received exactly 1,592 votes! Nearly 48,000 people viewed it and more than 80 comments are under the post (you need a LinkedIn account to fully see it here: LinkedIn Coin Tossing Poll).
In this post I will give the solution with some background explanation, so read on!
Continue reading “The Solution to my Viral Coin Tossing Poll”
In 2018 the renowned scientific journal science broke a story that researchers had re-engineered the commercial criminal risk assessment software COMPAS with a simple logistic regression (Science: The accuracy, fairness, and limits of predicting recidivism).
According to this article, COMPAS uses 137 features, the authors just used two. In this post, I will up the ante by showing you how to achieve similar results using just one simple rule based on only one feature which is found automatically in no-time by the
OneR package, so read on!
Continue reading “Recidivism: Identifying the Most Important Predictors for Re-offending with OneR”
We have already covered the backtesting of trading strategies in this blog (see Backtest Trading Strategies Like a Real Quant), so let us up the ante: if you want to learn how to backtest options strategies, read on!
Continue reading “Backtesting Options Strategies with R”
Wikipedia defines Parrondo’s paradox in game theory as
A combination of losing strategies becomes a winning strategy.
If you want to learn more about this fascinating topic and see an application in finance, read on!
Continue reading “Parrondo’s Paradox in Finance: Combine two Losing Investments into a Winner”
In data science, we try to find, sometimes well-hidden, patterns (= signal) in often seemingly random data (= noise). Pseudo-Random Number Generators (PRNG) try to do the opposite: hiding a deterministic data generating process (= signal) by making it look like randomness (= noise). If you want to understand some basics behind the scenes of this fascinating topic, read on!
Continue reading “Pseudo-Randomness: Creating Fake Noise”
When you ask successful people for their advice on how to become successful you will often hear that you have to take risks, often huge risks.
In this post we will examine whether this is good advice with a simple multi-agent simulation, so read on!
Continue reading “How to be Successful! The Role of Risk-taking: A Simulation Study”
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”