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?”
Forecasting the future has always been one of man’s biggest desires and many approaches have been tried over the centuries. In this post we will look at a simple statistical method for time series analysis, called AR for Autoregressive Model. We will use this method to predict future sales data and will rebuild it to get a deeper understanding of how this method works, so read on!
Continue reading “Time Series Analysis: Forecasting Sales Data with Autoregressive (AR) Models”
Google does it! Facebook does it! Amazon does it for sure!
Especially in the areas of web design and online advertising, everybody is talking about A/B testing. If you quickly want to understand what it is and how you can do it with R, read on!
Continue reading “Learning Data Science: A/B Testing in Under One Minute”
It is not easy to create secure passwords. The best way is to let a computer do it by randomly combining lower- and upper-case letters, digits and other printable characters.
If you want to learn how to write a small function to achieve that read on!
Continue reading “Learning R: Build a Password Generator”
Our intuition concerning randomness is, strangely enough, quite limited. While we expect it to behave in certain ways (which it doesn’t) it shows some regularities that have unexpected consequences. In a series of seemingly random posts, I will highlight some of those regularities as well as consequences. If you want to learn something about randomness’ strange behaviour and gain some intuition read on!
Continue reading “Learning Statistics: Randomness is a Strange Beast”
About two years ago the renowned medical journal “The Lancet” came out with the rather sensational conclusion that there is no safe level of alcohol consumption, so every little hurts! For example, drinking a bottle of beer per day (half a litre) would increase your risk of developing a serious health problem within one year by a whopping 7%! When I read that I had to calm my nerves by having a drink!
Ok, kidding aside: in this post, you will learn how to lie with statistics by deviously mixing up relative and absolute changes in risks, so read on!
Continue reading “Lying with Statistics: One Beer a Day will Kill you!”
Star Wars is somewhat nerdy, R definitely is… what could be more worthwhile than combining both 😉
This Sunday was Star Wars Day (May the 4th be with you!) and suitable for the occasion we will do a little fun project and implement the following xkcd flowchart, which can give us more than 2 million different Star Wars plots.
Even if you are new to R, the used code should be comprehensible, so read on!
Continue reading “Learning R: Build xkcd’s Star Wars Spoiler Generator”
In one of my most popular posts So, what is AI really? I showed that Artificial Intelligence (AI) basically boils down to autonomously learned rules, i.e. conditional statements or simply, conditionals.
In this post, I create the simplest possible classifier, called ZeroR, to show that even this classifier can achieve surprisingly high values for accuracy (i.e. the ratio of correctly predicted instances)… and why this is not necessarily a good thing, so read on!
Continue reading “ZeroR: The Simplest Possible Classifier… or: Why High Accuracy can be Misleading”
The global lockdown has slowed down mobility considerably. This can be seen in the data produced by our ubiquitous mobile phones.
Apple is kind enough to make those anonymized and aggregated data available to the public. If you want to learn how to get a handle on those data and analyze trends with R read on!
Continue reading “COVID-19: Analyze Mobility Trends with R”