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statistics-math

Perform descriptive statistics, hypothesis tests, and regression analysis with Python libraries.

5|1|Updated Nov 18, 2025
Search Tags:#probability#statistics#regression#data-analysis#linear-algebra#machine-learning#hypothesis-testing
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill statistics-math
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistics-math
Source: https://github.com/pluginagentmarketplace/custom-plugin-data-engineer/tree/main/skills/statistics-math
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill statistics-math

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill provides mathematical foundations and practical tools to perform statistics, probability, linear algebra, and data analysis tasks within data science workflows.

Core Features & Use Cases

  • Descriptive Statistics: Compute mean, median, std, and IQR on datasets.
  • Hypothesis Testing & Inference: Conduct t-tests, chi-square tests, and A/B experiments.
  • Regression & ML Foundations: Build and evaluate linear models, regularized variants, and interpret results.
  • Use Case: Given a dataset, quickly assess distributions, test hypotheses, and build a simple predictive model.

Quick Start

Use statistics-math to perform core statistical analyses on your data. For example, request descriptive statistics, hypothesis testing, or regression analysis, and the skill will execute the computations using numpy, scipy, pandas, and scikit-learn.

Frequently Asked Questions about statistics-math

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

FAQPage Schema
How do I run hypothesis testing and A/B experiments using Python?▼

To run hypothesis testing and A/B experiments in Python, you can use this skill to conduct t-tests and chi-square tests. It executes these computations using scipy, pandas, and numpy to rigorously evaluate statistical significance on your datasets.

What is the best way to compute descriptive statistics like IQR and standard deviation on a dataset?▼

Computing descriptive statistics like IQR and standard deviation is handled by calculating the mean, median, and distribution spread. This skill performs these data analysis tasks directly using pandas and numpy to profile your dataset.

Can I build and evaluate regression models with scikit-learn and statsmodels?▼

Yes, you can build and evaluate regression models with scikit-learn and statsmodels. This skill supports constructing linear models and regularized variants, allowing you to interpret results and establish machine learning foundations.

Do I need to install numpy and scipy to perform linear algebra and probability tasks?▼

Yes, you need to install numpy and scipy to perform linear algebra and probability tasks. The skill requires Python with these libraries, alongside pandas, statsmodels, and scikit-learn, to execute its rigorous statistical analysis workflows.

Why use statsmodels instead of other data analytics tools for interpreting linear models?▼

Using statsmodels for interpreting linear models provides detailed statistical summaries compared to basic data analytics tools. This skill leverages it alongside scikit-learn to build regularized variants and rigorously evaluate model results.

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