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statistical-analysis

Analyze numeric data with descriptive statistics, trend analysis, and outlier detection.

Updated Jan 23, 2026
Search Tags:#statistics#data-analysis#hypothesis-testing#trend-analysis#descriptive-statistics#business-intelligence#outlier-detection
One-click install
npx skills add https://github.com/qytay-palo/gen-e2-data-analysis-MOH --skill statistical-analysis-qytay-palo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/qytay-palo/gen-e2-data-analysis-MOH/tree/main/.github/prompts/data-plugin/skills/statistical-analysis
Command: npx skills add https://github.com/qytay-palo/gen-e2-data-analysis-MOH --skill statistical-analysis-qytay-palo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a structured approach to applying descriptive statistics, trend analysis, outlier detection, and hypothesis testing to analyze distributions, assess significance, detect anomalies, and interpret results accurately.

Core Features & Use Cases

  • Descriptive statistics: mean, median, mode, std, IQR, and percentiles to summarize distributions.
  • Trend analysis and forecasting: identify directions, compute moving averages, and compare periods (WoW, MoM, YoY).
  • Outlier and anomaly detection: apply Z-score, IQR, and percentile-based approaches with guidance on interpretation and data context.
  • Hypothesis testing basics: framing null/alternative hypotheses, selecting tests, and interpreting p-values with practical significance.

Quick Start

Input your numeric dataset and request a descriptive summary with trend analysis and outlier checks.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I calculate descriptive statistics and find outliers in my numeric dataset?▼

To calculate descriptive statistics and find outliers, input your numeric dataset to receive summaries like mean, median, and standard deviation alongside Z-score and IQR-based anomaly detection.

What is the best way to perform trend analysis and compare business metrics over time?▼

Trend analysis for business metrics involves computing moving averages and comparing periods like WoW, MoM, or YoY to identify directions and inform decisions based on your numeric data.

Can I use hypothesis testing to check for statistical significance in my data?▼

Yes, you can frame null and alternative hypotheses, select appropriate tests, and interpret p-values to check for statistical significance and practical relevance in your data distributions.

Does outlier detection work with skewed distributions and non-normal data?▼

Outlier detection works with skewed distributions by applying percentile-based approaches and IQR calculations, providing cautious interpretation guidance based on your specific data context.

Why use IQR and percentiles instead of just mean and standard deviation for anomaly detection?▼

Using IQR and percentiles instead of mean and standard deviation prevents anomalies from being skewed by extreme values, offering robust outlier detection for non-normal distributions.

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