Einleitung und Fragestellung In den letzten Jahren1 konnten in diversen Ländern der Aufstieg populistischer Phänomene beobachtet werden: der Aufstieg der AfD in Deutschland, die seit ihrer Gründung binnen 5 Jahren in sämtlichen deutschen Landtagen, dem Bundestag und dem europäischen Parlament vertreten ist; der Wahlsieg Donald J. Trumps in den USA; der Aufstieg der Lega in Italien und viele weitere Beispiele. Ein Verlaufsmuster, das bei jedem einzelnen dieser Aufstiege in der Öffentlichkeit auftritt ist das folgende Ping-Pong-Spiel zwischen Medien und Umfragewerten: Je größer die gesellschaftliche Bekanntheit, desto größer das Medienecho, desto größer die Bekanntheit, etc.
Great news: a scientific article I have co-authored has been accepted for publication and can now be found online here or via the DOI 10.1016/j.spa.2020.01.011. Yes, my list of publications has been amended 1. This article has been through quite a lengthy review process, and was the main motivation for another one of my blog posts. This post dates to September 2018, yet I only started working on these simulations in the framework of the second round of peer review.
A Standard Problem: Determining Sample Size Recently, I was tasked with a straightforward question: "In an A/B test setting, how many samples do I have to collect in order to obtain significant results?" As ususal in statistics, the answer is not quite as straightforward as the question, and it depends quite a bit on the framework. In this case, the A/B test was supposed to test whether the effect of a treatment on the success rate p had the assumed size e.
The Setting: Avoiding 4 Weeks of Runtime Recently, I was faced with a problem: I had written a rather complex simulation of a discrete time queueing network, and I needed to let this simulation run with some repetitions of the entire simulation, for some varying different parameter values, with many observations (i.e. ~ 2.000.000 observation). The goal was to verify that a new estimating procedure for such queueing networks provides sensible results.
In a previous post I have shown you how to setup an AWS instance running the newest RStudio, R, Python, Julia and so forth, where the configuration of the instance can be freely chosen. However, there is quite a lot of possibilities of instance configurations out there: There are different instance classes (General Purpose, Compute Optimized, RAM Optimized, … ) and different instance sizes within these classes. For General Purpose, or t2, there are, e.
Assume you want to start to write R code (a very good decision, in my opinion) and you want to be able to write and test code whereever you are. Wouldn’t it be awesome if one could set up an environment that can be used for R coding independent of any device? Where all you need is a decent browser, a working internet connection and you’re good to go? Obviously, that is the case.