Sponsored Post Learn from the experts: Create a successful blog with our brand new courseThe WordPress.com Blog

WordPress.com is excited to announce our newest offering: a course just for beginning bloggers where you’ll learn everything you need to know about blogging from the most trusted experts in the industry. We have helped millions of blogs get up and running, we know what works, and we want you to to know everything we know. This course provides all the fundamental skills and inspiration you need to get your blog started, an interactive community forum, and content updated annually.

Throwing The Dice

A common problem to solve for various university courses are gambling games. In these games we are usually interested in the probability that we actually win. One of these game consists in throwing dice and the higher number wins, now a more complicated and from a statistical point of view more interesting game is where each player is throwing its die twice and the person which has the overall higher sum wins. In the following I am going through an example to calculate the winning probability step by step and in the end I attach an excel file were the game can be simulated using various kind of dice.

Continue reading Throwing The Dice

Proof of Unbiasedness of Sample Variance Estimator

Proof of Unbiasness of Sample Variance Estimator

(As I received some remarks about the unnecessary length of this proof, I provide shorter version here)

In different application of statistics or econometrics but also in many other examples it is necessary to estimate the variance of a sample. The estimator of the variance, see equation (1) is normally common knowledge and most people simple apply it without any further concern. The question which arose for me was why do we actually divide by n-1 and not simply by n? In the following lines we are going to see the proof that the sample variance estimator is indeed unbiased.

Continue reading Proof of Unbiasedness of Sample Variance Estimator



What exactly is happening when we linearize a model? Well, the answer is simple, we basically approximate non-linear equations with linear once. In context of macroeconomics we may have models which are non-linear. Thus in order to solve them there is need to put them in a linear form. In the following we are going to see how to log-linearizing a model by the means of a (very) simple example. Continue reading Log-Linearizing

“In God we trust; all others must bring data.” W. Edwards Deming