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statistical-analysis

Apply hypothesis tests, regression, and forecasting to BI data with SQL and Python.

Updated Feb 24, 2026
Search Tags:#forecasting#correlation#outliers#statistical-analysis#a-b-testing#bi-analytics
One-click install
npx skills add https://github.com/modus-bi/dtp-bi-dashboards --skill statistical-analysis-modus-bi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/modus-bi/dtp-bi-dashboards/tree/main/skills/statistical-analysis
Command: npx skills add https://github.com/modus-bi/dtp-bi-dashboards --skill statistical-analysis-modus-bi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Statistical Analysis Expert helps BI teams assess significance, run A/B tests, identify correlations and outliers, and build forecasting models directly from data.

Core Features & Use Cases

  • Rigorous hypothesis testing and effect size estimation for BI decisions.
  • Correlation and regression analyses to quantify relationships between metrics.
  • A/B testing guidance with practical sample-size checks and practical significance interpretation.
  • Forecasting and time-series decomposition using accessible SQL/Python examples.

Quick Start

Provide a dataset and ask for a complete statistical analysis plan including test choices, model specs, and a business-ready interpretation.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I run A/B testing and calculate sample size for BI dashboards?▼

A/B testing for BI dashboards involves checking sample sizes and interpreting practical significance. This skill provides A/B testing guidance with sample-size checks and business-ready interpretations for your data.

What statistical tests should I use to find correlations and outliers in my data?▼

To find correlations and outliers, use hypothesis tests like t-test, ANOVA, and chi-square. This skill enforces proper statistical assumptions for correlation, regression, and outlier detection directly on your data.

How do I build forecasting models and decompose time-series using SQL or Python?▼

Forecasting and time-series decomposition can be built using accessible SQL or Python examples. This skill applies time-series decomposition methods to determine forecasted outcomes from your data.

Does this statistical analysis approach support confidence intervals and effect size estimation?▼

Yes, this statistical analysis approach supports confidence intervals and rigorous effect size estimation. It enforces proper statistical assumptions to help BI teams assess significance and quantify metric relationships.

How do I interpret practical significance versus statistical significance in reporting?▼

Practical significance in reporting interprets the real-world business impact of your test results. This skill provides practical significance interpretations alongside rigorous hypothesis testing for dashboards and reports.

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