statistics-math

Analyzes datasets for trends, variability, and distribution using Python.

Updated Aug 27, 2026

Search Tags:#python #data analysis #hypothesis testing #statistics #regression #linear algebra

Ready to run

npx skills add https://github.com/tDalile/dotfiles --skill statistics-math-tdalile

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

Descriptive Statistics: Compute mean, median, standard deviation, and inter‑quartile range for numeric data.
Hypothesis Testing: Perform t‑tests, chi‑square tests, and A/B testing with effect‑size reporting.
Probability Distributions: Generate and query normal, binomial, and Poisson distributions.
Linear Algebra Operations: Matrix multiplication, inversion, eigen‑decomposition, SVD, and cosine similarity.
Regression Modeling: Build OLS, Ridge, and Lasso models, evaluate with R², RMSE, and visualize results.

Quick Start

Ask the skill to analyze my data and provide a summary of statistical insights.

Frequently Asked Questions about statistics-math

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform hypothesis testing and calculate effect size in Python?▼

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.

What Python libraries do I need for regression modeling and descriptive statistics?▼

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.

Can I run linear algebra operations like SVD and eigen-decomposition on my dataset?▼

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.

What's the best way to build OLS, Ridge, and Lasso regression models for data analysis?▼

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.

How do I generate and query probability distributions like normal and Poisson?▼

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.