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.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Statsmodels is a comprehensive Python library for rigorous statistical modeling, enabling deep inference, diagnostics, and advanced econometric analysis. Use it for tasks like linear regression (OLS), generalized linear models (GLM), time series forecasting (ARIMA), and hypothesis testing, providing detailed results for academic and scientific reporting.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables generating scalable and structure-preserving embeddings for visualization (2D/3D), clustering preprocessing, and feature engineering. It supports supervised and semi-supervised workflows, allowing users to guide the embedding process with labeled data while maintaining local and global data structure integrity.

This tool provides forward-looking cost projection by calculating the average daily burn rate from a specified measurement window. It linearly extrapolates future spending across multiple time horizons (7d, 30d, 90d, 365d) and alerts users to the projected date of budget exhaustion. It is essential for proactive financial planning, SRE cost monitoring, and setting CI/CD build cost gates.

This tool performs deep, multi-touch attribution analysis to calculate comprehensive Return on Investment (ROI), Return on Ad Spend (ROAS), and Customer Acquisition Cost (CAC) across all marketing channels. It supports various attribution models (e.g., linear, time-decay) and projects long-term value using LTV, providing data for budget optimization, executive reporting, and identifying efficiency gaps.

This guide provides comprehensive standards for designing, quantifying, and narrating scientific figures and tables for high-impact publications. It details best practices for maintaining image integrity (e.g., uncropped blots, linear adjustments), ensuring all data points are quantified across replicates, and structuring the visual evidence to advance a clear, mechanistic argument step-by-step.

Provides rigorous guidance and structure for statistical analysis in Field Crops Research (FCR). Focuses on implementing linear mixed models that respect complex experimental designs (e.g., split-plot, block, environment). Essential for proper G×E analysis, robust means separation, and accurate evaluation of crop models, ensuring results are methodologically sound and actionable.

A comprehensive guide for conducting rigorous, publication-quality statistical analyses required by top-tier management journals. It covers advanced techniques including Structural Equation Modeling (SEM)/CFA, Hierarchical Linear Modeling (HLM), complex regression, and meta-analysis, ensuring reports include necessary effect sizes, confidence intervals, and robustness checks to meet demanding academic standards.

A comprehensive guide for conducting advanced quantitative research using Structural Equation Modeling (SEM) on questionnaire data. It details rigorous procedures for testing complex causal relationships, including mediation, moderation, and moderated mediation, alongside handling nested data via Hierarchical Linear Modeling (HLM). Essential for academic papers requiring robust inference.

Statsmodels is Python's premier library for rigorous statistical modeling. It provides comprehensive tools for estimation, inference, and diagnostics across a wide range of statistical methods. Use this skill for advanced analysis, including linear regression (OLS, WLS), generalized linear models (Logistic, Poisson), and complex time series forecasting (ARIMA). Ideal for econometric studies and producing publication-ready results.