Ing. Químico, fui profesor es mi vocación, pensador sistémico, ávido lector y creo en aprender toda la vida y confío en que Dios supo dotarnos. Lo lograremos!
Discovery of a 14-protein biomarker that predicts lung cancer 5.6 years before it is diagnosed, even in non-smokers, and an anti-inflammatory medicine that prevents its progression. And, challenging dogma, the proteins are not coming from cancerous cells!
¿Cómo así que el Mundial MÁS COSTOSO de la historia NO tiene contratada traducción para la segunda lengua más hablada EN EL MUNDO, más aún siendo lengua nativa de uno de sus países anfitriones (México) y el segundo idioma más hablado en Estados Unidos?
I don't think anybody really grasps how desperate this situation is.
University professors are now saying they are unable to teach history because reading long books and passages is how a person learns history. College kids are incapable of reading more than a few pages.
Some classes don't assign any reading at all now, only lectures.
There is an assumption among the people managing this decline that reading is just a way of receiving information. It isn't. Proper reading is how we build the mental muscle to synthesize ideas and evaluate them.
If the catastrophic decline in reading and literacy is not addressed now, we risk losing everything.
Western civilization cannot survive the death of reading because it was built by people with the kind of cognitive depth that a culture of deep reading brings:
Complex reasoning, extended internal dialogue, the capacity to hold opposing ideas in tension. Our systems and institutions are complex, and they require well ordered minds to maintain them.
Reading forms minds, and the West was built by the richest minds in history.
Kernel Smoothing Regression is a flexible technique used to model complex, non-linear relationships in data. Unlike linear regression, which assumes a straight-line relationship, kernel smoothing adapts to the underlying patterns, making it ideal for data that doesn't fit simple models.
Opportunities:
✔️ Captures Complex Patterns: It identifies intricate relationships in data, providing a more accurate fit for non-linear trends.
✔️ Adaptable: Adjusts to data variations without being overly restricted by assumptions, making it useful for exploratory analysis.
✔️ Handles Noise Effectively: Smooths out random variations, which can help reveal the true signal in noisy data.
Challenges:
❌ Computationally Intensive: Requires significant computational power, especially with large data sets, which might slow down analysis.
❌ Sensitive to Parameters: The choice of kernel and bandwidth can significantly impact results. A poor choice may lead to overfitting or underfitting.
❌ Less Interpretability: Compared to simpler models like linear regression, the results of kernel smoothing can be harder to interpret and explain.
To handle Kernel Smoothing Regression in practice:
🔹 R: Use the ggplot2 package for visualization and the geom_smooth() function with method = "loess" to apply kernel smoothing.
🔹 Python: Use the seaborn library for visualization, specifically the sns.lmplot() function with the lowess=True parameter to perform kernel smoothing.
The visualization above shows the difference between linear regression (dashed red line) and kernel smoothing (solid green line). The kernel smoothing line adjusts to the data's natural curvature, providing a better fit for non-linear relationships.
If you're interested in learning more about Kernel Smoothing Regression and other statistical methods, check out my online course on Statistical Methods in R! More information: https://t.co/7YQCRDKSPO
#rstudioglobal #DataVisualization #DataViz #database #DataAnalytics #DataScientist #ggplot2 #RStats #Rpackage #DataAnalytics #tidyverse
A plumber knows more about plumbing than you.
A pilot knows more about flying than you.
A scientist usually knows more about science than you.
That doesn’t make them automatically right.
But it does mean the burden of proof is on the person claiming thousands of experts got it wrong.
Science isn’t a democracy.
It’s not decided by likes, vibes, or confidence.
It’s decided by evidence.
And evidence doesn’t care who wins the argument.
Science is not trustworthy because a scientist wears a lab coat, has a PhD, or holds a prestigious position.
Science is trustworthy because claims are transparent, testable, and supported by evidence.
El examen de catalán de la Selectividad está hecho con más mala leche que una feria del jamón ibérico delante de una mezquita. Curiosamente, el de lengua española parece un cuaderno de "colorea y pinta". Luego os dirán que las PAU demuestran que el nivel de catalán es peor.
A mi no me preocupa que Elon se haga trillonario con sus empresas que avanzan la humanidad y generan decenas de miles de empleos y ahora de empleados millonarios.
A mi me preocupa que los líderes del PSOE se hagan millonarios robando de tus impuestos y los míos.
Es decir, Aranza Hernández pasó más tiempo presa con los hermanos Rodríguez al mando, la Ley de Amnistía activa y la Comisión para la Convivencia en marcha, que con Maduro.
¿Quiénes fueron sus carceleros? ¿Cuántas instituciones se coordinaron por meses para mantenerla detenida?