Decir que el salario medio de una población es 25 000 euros anuales no implica que la mayoría cobre esa cantidad. Distinguir entre media, mediana y moda es importante para interpretar los datos que aparecen en las noticias. » Luis José Rodríguez Muniz | theconversation.com
Netflix: A Culture of Learning » netflixtechblog.com | netflixtechblog.com
Important results of a quantitative analysis of The Office TV show. See how the dataset was created, cleaned and calculated. Inspired by Tim Marcin » blog.wolfram.com | blog.wolfram.com
Este domingo se celebrarán en Rusia elecciones locales. Es fácil anticipar lo que ocurrirá. La oposición denunciará, una vez más, la existencia de fraude electoral y divulgará multitud de videos incriminatorios. Sin embargo, la comisión electoral ignorará las protestas y… » Manuel Bagues | nadaesgratis.es
Even in times of a growing market of specialized NoSQL databases, the relevance of traditional RDBMS doesn’t decline. Especially when it comes to the calculation of aggregates based on complex data sets that can not be processed as a batch like Map&Reduce. MySQL is already bringing in a handful of aggregate functions that can be useful for a statistical analysis. The best known of this type are certainly: COUNT(x), SUM(x), AVG(x), MIN(x), MAX(x), STD(x) In addition, there are a number of statistical evaluations which are also worthwhile - if not even more interesting and meaningful, but with MySQL only producible with greater efforts. What about the different averages? The harmonic average, a weighted average or the geomean? What is in the course of this with the aggregate product? How do we determine the mode, the median? The covariance? In the following article I want to go to the bottom of these questions and develop a list of standard formulas for a statistical evaluation. Presumably the article is meant more for beginners. In addition, a few new features have been poured into my infusion UDF, which simplifies some of the calculations. You can check out the source of the UDF on Github: ...