Which Python libraries are used with this skill?

The skill provides implementation patterns primarily using pandas, scipy.stats, scikit-learn, and matplotlib.

How does it help with A/B testing?

It includes code for calculating control and treatment conversion rates, determining lift, and calculating p-values to check for statistical significance.

Can I use this for data cleaning?

While primarily focused on analysis, the skill's inclusion of anomaly detection and correlation analysis helps identify data quality issues before modeling.

What statistical methods does this skill support?

It provides frameworks for A/B testing, chi-square contingency tests, linear regression, correlation analysis, and general hypothesis testing.

Does this skill help with predictive modeling?

Yes, it covers regression analysis which allows you to define relationships between variables and predict future outcomes based on historical data.

Statistical Analysis

Name: Statistical Analysis
Author: spjoshis

byspjoshis

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Ciencia de Datos y ML

Applies rigorous statistical methods and hypothesis testing to derive data-driven insights and validate experimental results.

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The Statistical Analysis skill equips Claude with specialized expertise for evaluating data through advanced mathematical frameworks like A/B testing, linear regression, and significance testing. It is designed for developers and data scientists who need to move beyond raw data to find actionable insights, validate experimental hypotheses, and identify statistically significant trends. By following structured patterns for Python-based analysis and adhering to core statistical best practices, this skill ensures that every conclusion is mathematically sound and scientifically valid.

Características Principales

1 GitHub stars
Automated A/B test significance and lift calculations
Implementation patterns for linear and multi-variable regression
Standardized hypothesis testing workflows and p-value interpretation
Guidance on checking assumptions like normality and independence
Correlation analysis and statistical anomaly detection

Casos de Uso

Analyzing A/B test results to determine the impact of new product features
Validating experimental research through formal significance testing
Building predictive models for business forecasting and trend analysis

Acerca de

Características Principales

1 GitHub stars
Automated A/B test significance and lift calculations
Implementation patterns for linear and multi-variable regression
Standardized hypothesis testing workflows and p-value interpretation
Guidance on checking assumptions like normality and independence
Correlation analysis and statistical anomaly detection

Casos de Uso

Analyzing A/B test results to determine the impact of new product features
Validating experimental research through formal significance testing
Building predictive models for business forecasting and trend analysis