## Learning Statistics: On Hot, Cool, and Large Numbers

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!
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## The Solution to my Viral Coin Tossing Poll

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!
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## Recidivism: Identifying the Most Important Predictors for Re-offending with OneR

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!
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## Parrondo’s Paradox in Finance: Combine two Losing Investments into a Winner

Wikipedia defines Parrondo’s paradox in game theory as

A combination of losing strategies becomes a winning strategy.

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## Pseudo-Randomness: Creating Fake Noise

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!
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## How to be Successful! The Role of Risk-taking: A Simulation Study

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!
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## R Coding Challenge: How many Lockers are Open?

The German news magazine DER SPIEGEL has a regular puzzle section in its online version, called “Rätsel der Woche” (“Riddle of the Week”). Some of those puzzles are quite interesting but I am often too lazy to solve them analytically.

So I often kill two birds with one stone: having fun solving the puzzle with R and creating some new teaching material for my R classes! This is what we will do with one of those more interesting riddles, which is quite hard to solve analytically but relatively easy to solve with R, so read on!
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## ELIZA Chatbot in R: Build Yourself a Shrink

More and more companies use chatbots for engaging with their customers. Often the underlying technology is not too sophisticated, yet many people are stunned at how human-like those bots can appear. The earliest example of this was an early natural language processing (NLP) computer program called Eliza created 1966 at the MIT Artificial Intelligence Laboratory by Professor Joseph Weizenbaum.

Eliza was supposed to simulate a psychotherapist and was mainly created as a method to show the superficiality of communication between man and machine. Weizenbaum was surprised by the number of individuals who attributed human-like feelings to the computer program, including his own secretary!

If you want to build a simple Eliza-like chatbot yourself with R read on!
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## Why Gradient Descent Works (and How To Animate 3D-Functions in R)

The workhorse of Machine Learning is Gradient Descent. If you want to understand how and why it works and, along the way, want to learn how to plot and animate 3D-functions in R read on!
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