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ZanderRuss
ZanderRussCOMMUNITY
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Website𝕏@mathew_fullerGitHub

statistical-analysis

Conduct t-tests, ANOVA, chi-square, regression, and Bayesian analyses with APA-style reporting.

18|2|Updated Jan 10, 2026
Search Tags:#research#data analysis#hypothesis testing#statistics#regression#bayesian analysis
One-click install
npx skills add https://github.com/ZanderRuss/obsidian-claude --skill statistical-analysis-zanderruss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/ZanderRuss/obsidian-claude/tree/main/.claude/skills/statistical-analysis
Command: npx skills add https://github.com/ZanderRuss/obsidian-claude --skill statistical-analysis-zanderruss

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, matplotlib, seaborn, statsmodels, pingouin, pymc, arviz, sklearn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to perform complex statistical analyses, from hypothesis testing to regression and Bayesian modeling, ensuring rigorous and accurate data interpretation for research.

Core Features & Use Cases

  • Hypothesis Testing: Conduct t-tests, ANOVA, chi-square tests with assumption checks.
  • Regression & Correlation: Perform linear, logistic regression, and correlation analyses with diagnostics.
  • Bayesian Statistics: Utilize Bayesian methods for flexible inference and direct probability statements.
  • Reporting: Generate APA-style reports with effect sizes and confidence intervals.
  • Use Case: A researcher needs to determine if a new teaching method significantly improves student test scores compared to the traditional method, while also checking for normality and homogeneity of variance assumptions.

Quick Start

Use the statistical-analysis skill to perform an independent samples t-test on 'group_a_scores.csv' and 'group_b_scores.csv'.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I run hypothesis testing with assumption checks for academic research?▼

Hypothesis testing requires verifying normality and homogeneity of variance assumptions before applying t-tests, ANOVA, or chi-square tests to ensure valid academic research conclusions.

Can I perform Bayesian analysis and regression using pandas and statsmodels?▼

Yes, Bayesian analysis and regression can be performed using pandas for data manipulation and statsmodels alongside pymc for flexible statistical inference and direct probability statements.

What is the best way to generate APA-style reports with effect sizes from statistical analysis?▼

Generating APA-style reports involves calculating effect sizes, confidence intervals, and power analysis after completing statistical analysis to provide rigorous academic data interpretation.

Does this statistical analysis toolkit support power analysis and correlation diagnostics?▼

Yes, the statistical analysis toolkit supports power analysis, correlation diagnostics, and assumption checking to validate research data interpretation across various hypothesis testing scenarios.

How do I compare teaching methods using independent samples t-test on CSV data?▼

Comparing teaching methods via independent samples t-test involves loading CSV score data, checking variance assumptions, and calculating effect sizes to determine significant differences between groups.

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