José R. Berrendero

M.C Escher's grave bears one of his own works, "Freedom". 😲

En matemáticas, el contexto oscurece la estructura. En estadística, el contexto proporciona el sentido. Esta diferencia tiene profundas implicaciones en la docencia. (Cobb y Moore, "Mathematics, Statistics and Teaching", Amer. Math. Monthly, 1997)

Quantile regression is a valuable tool for analyzing the relationship between variables, especially when data is not evenly distributed or has outliers. Unlike traditional linear regression, which focuses only on the mean, quantile regression allows us to predict different points across the distribution of the target variable. Challenges: ❌ Compared to linear regression, quantile regression requires more computational power and can be harder to interpret for non-experts. ❌ Larger sample sizes might be needed to achieve stable and reliable quantile estimates, especially for extreme percentiles. ❌ The model's results might be less intuitive if you are accustomed to traditional regression techniques, which could limit ease of communication. Advantages: ✔️ Quantile regression helps to explore trends at various quantiles, offering a more detailed picture of your data. ✔️ This method is highly effective for non-normal data, particularly when there are outliers or heavy tails. ✔️ It is ideal for situations where extreme values or various percentiles are as important as the central trend. How to handle quantile regression in practice: 🔹 R: Use the quantreg package to apply quantile regression. The rq() function allows you to specify the quantiles you're interested in. 🔹 Python: In Python, statsmodels provides quantile regression with the QuantReg() function to analyze different percentiles of your data. The attached visualization is based on a Wikipedia image (link: en.wikipedia.org/wiki/Quantile_…) and illustrates quantile regression lines at various percentiles, showing how predicted values differ across the distribution. To explain this topic in further detail, I collaborated with Micha Gengenbach to create a comprehensive tutorial: statisticsglobe.com/quantile-regre… Curious to learn more about statistics and R programming? Join my online course, "Statistical Methods in R." For more information, visit this link: statisticsglobe.com/online-course-… #Rpackage #datastructure #database

Suficiente música, Dorianne Laux zendalibros.com/6-poemas-de-do…

📢 Publicado nuevo artículo #MUYSEIO "La cara menos neutral de la IA: cuando sus sistemas muestran sesgos" 👥 Itziar Irigoien Garbizu, Naiara Aginako Bengoa, Olatz Arbelaitz Gallego y Ana Zelaia Jauregi Descúbrelo aquí: 🔗fundacionmuyinteresante.org/la-cara-menos-…

Bring You Down The Lemon Twigs, 2026 (Se dice que no innovan. Con esta apabullante colección de canciones pueden seguir no innovando todo el tiempo que quieran.) youtu.be/MHtlfZVibTo?si…

Una breve historia de la IA open.substack.com/pub/digitaldat…

Posiblemente, la imagen más bella de #Madrid se la debemos al fotógrafo portugués Horácio Novais, quien captó esta postal de la Gran Vía en 1954. #2deMayo

BASIC launched on this day in 1964.

Está ya disponible para su libre descarga mi manual de Filosofía de la Ciencia, que gracias a la amabilidad de algunos colegas ha sido recomendado en varias universidades. Me gustaría que al estar ahora en abierto pueda ser útil para más personas. monografias.uma.es/index.php/muma…

📘 Plan para el puente: Comprender la Inteligencia Artificial, de Daniel Peña. Monografía en abierto, clara y divulgativa sobre los fundamentos estadísticos de la Inteligencia Artificial. Muy recomendable 📖 🔗 funcas.es/wp-content/upl…

Se cumplen cincuenta años de la publicación de uno de los libros de historia más influyentes de nuestro tiempo. El queso y los gusanos sigue siendo un referente, una forma de hacer, un lugar seguro al que volver en tiempos de miedo, duda e incertidumbre. La pregunta es inmediata:

How to differentiate the ReLU function? Let me count the ways. johndcook.com/blog/2026/04/3…

Basically

Hace tiempo en el blog (2018): El Grand Slam de las revistas estadísticas caminosaleatorios.wordpress.com/2018/08/16/el-…

Math. Liber Abaci (Book of Calculation) is the work that essentially brought modern numerals to Europe. Published in 1202, Leonardo of Pisa (Fibonacci) learned the "Hindu-Arabic" system (0–9) while traveling in North Africa and the Middle East as a merchant's son. In the book, he demonstrated its superiority over Roman numerals for commerce, calculation, and science (arguing it made arithmetic accessible rather than an elite skill reserved for specialists using abacuses). One cool detail: the book opens with the famous "rabbit breeding problem" (how many pairs of rabbits will be produced in a year from a single pair, assuming they mature in one month and produce another pair every month thereafter). This generates the sequence 1, 1, 2, 3, 5, 8, 13, 21... (now called the Fibonacci sequence) which appears throughout nature (spiral patterns in pinecones, sunflowers, etc.). Leonardo didn't claim to discover the sequence (it was known earlier in India), but his popularization of it in a practical context helped embed it in Western mathematics. This book helped shift Europe from clunky Roman numerals to the efficient decimal system we still use today. Image: mathigon.org/timeline/liber…

@gilbellosta Entre Excel, Word y PowerPoint, Excel es el único no dañino

(2/2) Esta es la url: economist.com/business/2024/…

(1/2) “Why Microsoft Excel won’t die”

@_amayamendiko @GardeJulian Me ha parecido un artículo vacío de contenido. En ningún momento aclara hacia donde se deben encaminar esos cambios tan necesarios. Da la sensación de que falta contexto para entender la propuesta.

La cuestión no es si hay que cambiar, sino si vamos a hacer los cambios necesarios a tiempo. Interesante reflexión de @GardeJulian 👇

Una reflexión sobre el rigor en matemáticas (leído en Needham, Visual complex analysis)