Analyzes datasets for trends, variability, and distribution using Python.
npx skills add https://github.com/tDalile/dotfiles --skill statistics-math-tdalile
Please help me install this Agent Skill. Skill: statistics-math Source: https://github.com/tDalile/dotfiles/tree/main/agents/skills/statistics-math Command: npx skills add https://github.com/tDalile/dotfiles --skill statistics-math-tdalile
It eliminates the need for manual calculations and ad‑hoc scripts by providing ready‑to‑use statistical analysis, hypothesis testing, and linear‑algebra tools for data‑driven projects.
Ask the skill to analyze my data and provide a summary of statistical insights.
High-intent search queries and answers about installing and using this skill.
Hypothesis testing in Python is performed using t-tests, chi-square tests, and A/B testing with effect-size reporting. This skill automates these calculations using libraries like SciPy and statsmodels to evaluate statistical significance directly from your dataset.
Regression modeling and descriptive statistics require Python libraries such as NumPy, SciPy, pandas, scikit-learn, and statsmodels. These dependencies enable you to compute central tendencies, build OLS models, and evaluate results with R² and RMSE.
Yes, linear algebra operations including matrix multiplication, inversion, eigen-decomposition, SVD, and cosine similarity are fully supported. The skill applies these techniques using NumPy to analyze numerical properties and relationships within your data.
The best way to build OLS, Ridge, and Lasso regression models is by using scikit-learn and statsmodels. This skill streamlines the process, allowing you to train models, evaluate performance with RMSE, and visualize the regression results.
Probability distributions such as normal, binomial, and Poisson are generated and queried using SciPy. This skill provides ready-to-use tools to model distribution characteristics, eliminating the need for manual calculations or ad-hoc scripts.
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