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Ben Ortega Torres
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Ben Ortega Torres
@BenTorres33
“Dread it. Run from it. Destiny arrives all the same”. -Thanos IG: @bentorres33
Santa Rosa, Misiones, Paraguay Katılım Nisan 2016
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Michael B. Jordan with his In-N-Out burger and his Oscar.
See the full winners list: bit.ly/OscarWins26

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El Vori Vori ya no solo es solo la Mejor Sopa, ahora es el Mejor Plato del Mundo del 2025 !!! 🇵🇾🇵🇾🇵🇾🇵🇾🇵🇾

TasteAtlas@TasteAtlas
@TasteAtlas Awards: 100 Best Rated Dishes in 2025: tasteatlas.com/best/dishes. Huge surprise at the top!
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Best Dish in the World for 2026: Vori Vori (Paraguay 🇵🇾)
Full ranking: tasteatlas.com/best
Vori vori, the iconic Paraguayan chicken soup with cheese-filled cornmeal dumplings, has been crowned the #1 Best Dish in the World 2025/26 on TasteAtlas.
It achieved the highest average rating of 4.64/5 based on 453,720 valid user ratings for 11,781 dishes - counted only from users who have actually tried the dish. The ranking is powered by algorithms that rigorously filter out bots and nationalist voting brigades, ensuring pure democratic math: no jury, no magic.
Rooted in Guaraní culinary tradition, vori vori stands out as one of the few soups in the region that uses dense, cheese-enriched cornmeal balls instead of noodles or vegetables.
Video by guidopenayopy
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Collinearity and multicollinearity are often mistaken for the same issue, but they have distinct impacts on regression analysis. Understanding their differences is key to building stable and interpretable models.
Collinearity occurs when two predictor variables are highly correlated, making it difficult to determine their individual contributions to the response variable. This can lead to unstable coefficient estimates and reduced model reliability.
Multicollinearity extends this issue to three or more predictors. Even if two variables are not directly correlated, they may still exhibit multicollinearity if they share relationships with a third variable. This can inflate standard errors, distort p-values, and make it harder to interpret the model.
The image below shows a pair plot suggesting multicollinearity, displaying scatter plots and correlation values among predictor variables. Strong correlations (e.g., X1 with X2 and X3) indicate possible multicollinearity, which should be further analyzed using statistical tests. The plot was created using the GGally package in R.
🔹 In R, vif() from the car package computes the Variance Inflation Factor (VIF) to detect multicollinearity, while cor() identifies pairwise collinearity.
🔹 In Python, variance_inflation_factor() from statsmodels.stats.outliers_influence quantifies multicollinearity, and numpy.corrcoef() checks for pairwise correlations.
Looking to learn more? Check out my online course on Statistical Methods in R!
More info: statisticsglobe.com/online-course-…
#RStats #Python #statisticians #R4DS #statisticsclass

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