My university background is physics, which is an empirical science. All measurements or derived quantities must be quoted with an error estimate. This should ideally include both a statistical and a systematic error. If you take a look at the paper from my thesis, you will find this table:
In the board game Ex Libris one draws cards with book shelves and has to arrange them in alphabetic order. The catch is that one can only rarely rearrange the cards that one has put down onto the table. Also one gets extra points for the largest rectangular section. There can at most be three rows of shelves. Therefore one should try to have a third of the alphabet in each row. But where should one separate?
I recently noticed by accident that the default standard deviation implementations in R and NumPy (Python) do not give the same results. In R we have this:
In 2011 I was on vacation with friends, we played a lot of Risk. Somehow we ended up having fights of hundreds of armies against each other. Since with every dice rolling you can only eliminate up to three armies, you need a lot of rounds until a battle is settled. While my friends were occupied in a battle of 200 against 150, I used my freshly acquired Python skills to write a program to do the dice rolling, risk-auto-dice.
I have seen a few referendums in the past:
There are multiple ways of structuring data. Two sensible formats are the long and the wide data format. In this article I want to do the same problem in two different ways and show the relative merits.
I played around with the weapons from MechWarrior Online in R. The following is created from an Rmarkdown document.
Gerade noch einen schönen Artikel zum Thema Fahrradhelm gelesen. Dort wurde gesagt, dass ein Helm viel nützt.
In statistics, there is the standard deviation and the standard error. Their respective estimators are
There are a couple functions that promise to fit an arbitrary curve with error weighting. Most of those methods then give error estimates for the fit parameters.