statistical-analysis

Analyze data distributions, trends, outliers, and hypothesis tests using Python.

Updated Apr 3, 2026

Search Tags:#data analysis #hypothesis testing #trend analysis #statistical analysis #outlier detection

Ready to run

npx skills add https://github.com/jmolz/m0lz.02 --skill statistical-analysis-jmolz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/jmolz/m0lz.02/tree/main/.agents/skills/statistical-analysis
Command: npx skills add https://github.com/jmolz/m0lz.02 --skill statistical-analysis-jmolz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of applying statistical methods, making it accessible for users to analyze distributions, test for significance, detect anomalies, compute correlations, and interpret results.

Core Features & Use Cases

Descriptive Statistics: Calculate mean, median, mode, standard deviation, and more.
Trend Analysis: Identify patterns and trends in data with moving averages and period-over-period comparisons.
Outlier Detection: Detect and handle outliers using statistical methods like Z-score and IQR.
Hypothesis Testing: Conduct hypothesis tests to determine the significance of observed differences.
Use Case: A data analyst can use this Skill to analyze sales data, identify trends, detect outliers, and test hypotheses about customer behavior.

Quick Start

Use the statistical-analysis skill to calculate the mean and standard deviation of the sales data in the 'sales_data.csv' file.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema

How do I perform statistical analysis on a CSV file in Python?▼

To perform statistical analysis on a CSV file, you need Python knowledge to execute the provided scripts. The skill processes CSV data to calculate descriptive statistics, identify trends, and interpret results.

What is the best way to detect outliers in data distributions?▼

Detect outliers in data distributions using Z-score and IQR statistical methods. These techniques identify anomalies by measuring data point deviations from established central tendencies.

Can I use Python for hypothesis testing and trend analysis?▼

Yes, Python supports hypothesis testing and trend analysis through statistical methods. You can identify patterns using moving averages and test the significance of observed differences in your data.

Do I need prior statistical knowledge to analyze data distributions and test hypotheses?▼

Yes, statistical concepts knowledge is required to analyze data distributions and test hypotheses. Python proficiency is also necessary to execute the analysis scripts and interpret the statistical results.

What statistical methods are used for trend analysis?▼

Trend analysis uses moving averages and period-over-period comparisons as statistical methods. These techniques identify patterns and trends within data to help interpret longitudinal changes.

When should I use Z-score vs IQR for outlier detection?▼

Choose Z-score for normally distributed data and IQR for skewed distributions during outlier detection. Both statistical methods detect anomalies, but selection depends on your specific data distribution characteristics.