Skill UI

Enables regression modeling via the regression-analysis-tool plugin by analyzing datasets, choosing linear or polynomial fits, validating models, and reporting metrics so users can predict outcomes or explore variable relationships.

Provides automated regression analysis guidance, best practices, code snippets, and validation for SQL, BI, visualization, and statistical workflows in data analytics projects.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

Scikit-learn compatible Python toolkit for time series machine learning: classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search and distance metrics for univariate and multivariate temporal data.

Provides guided statistical analysis for research data, covering test selection, assumption checking, effect sizes, and APA reporting. Supports frequentist and Bayesian methods including t-tests, ANOVA, regression, and correlation. Ideal for comparing groups, testing hypotheses, and analyzing experimental or survey data to produce robust, reviewer-ready results.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

Perform a priori power analysis to determine required sample sizes for studies. Supports closed-form calculations for t-tests, ANOVA, and regression, plus Monte Carlo simulations for complex models like mixed models and survival analysis. Helps justify sample sizes for grants and protocols by calculating minimum detectable effects and power curves.

This skill helps diagnose performance bottlenecks, slow jobs, and expensive queries across various data platforms (Airflow, dbt, Databricks). It uses a tiered investigation approach, starting with broad discovery to pinpoint problematic jobs or queries, bridging to affected tables, and finally drilling down to identify root causes, latency trends, and change-related regressions.

Generates comprehensive quality intelligence reports by analyzing historical evaluation scores. This tool tracks content performance over time, identifies improving or declining content types, detects significant quality regressions against baselines, and provides prioritized, actionable recommendations for improving brand quality across various content dimensions.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

This guide details how to construct a sophisticated and rigorous robustness suite for top-tier academic econometrics journals (e.g., AEJ: Applied). It moves beyond simple regression tables, providing targeted checks against common methodological threats such as omitted variables, specification search, functional form choices, and incorrect statistical inference. The goal is to systematically demonstrate that the main point estimate is stable, reliable, and not an artifact of the research design.