The document explains how to simulate a linear regression model in R, covering the math, data-generating process for binary and continuous predictors, and visualization. It shows how to generate data, fit the model, and interpret results, noting that increasing sample size improves estimate accuracy.
The post explains how collinearity between predictors affects regression results, using simulated data to compare two models and showing that adding a highly correlated predictor can reduce the significance of existing predictors and worsen model selection metrics.
An A/A test of LLM evaluation noise using Llama 3.1 8B Instruct shows small but measurable variability across identical runs; fixed tasks and predefined rules are needed to distinguish real regressions from noise, with industry notes on model versioning and infrastructure affecting results.
A reanalysis disputes earlier claims that lifestyle data predict culturally specific obesity risk between Indian and Japanese students; proper train/test evaluation shows BMI alone drives obesity prediction, and other lifestyle features add little predictive value. The study emphasizes the importance of held-out data and correct feature-importance measures.
The document explains the difference between regression and classification in supervised learning, with examples and guidance on choosing targets, evaluating errors, and recognizing how the same data can serve different tasks. It also notes that numerical outputs can underpin classification when thresholds are applied, and references related tutorials on regression and churn detection.
This study tests 13 LLMs on 45,964 Form 10-Q events to see if model outputs align with market results, using multiple information views. It finds modest gains from adding fundamental ranks, limited benefit from analyst summaries, variable performance by model type, and mixed success for LLM forecasts versus analysts.
The paper presents a method that uses feature-invariant scaling laws and statistical inference to estimate the full-scale effects of recommendation-system changes from small experiments. Tests across three model families and more than 40 changes found that the method can identify useful changes while reducing compute needs.
The document describes calculating the Artificial Analysis Progress Index v1 via item response theory to compare model performance across benchmarks and over time, by estimating task difficulty and model capability from results, noting how older and newer models are tested on different benchmarks and how comparisons use the top-scoring models and a common scale.
Apple’s hardware performance appears strong despite memory issues; the piece questions the impact of software storytelling. It promotes Stratechery’s subscription-based analysis, podcasts, and various delivery options, outlining pricing, discounts, and invoicing details.
A regression-based analysis finds that a model's benchmark Intelligence Index does not predict how much developers actually run it; multiple statistical tests show no significant relationship, with usage influenced by price, latency, and other factors. The study uses OpenRouter traffic as the usage measure and cautions that results apply only to models already receiving significant traffic, not the entire market.