statistical-analysis

Automating statistical analysis for academic research runs hypothesis tests, checks assumptions, calculates effect sizes, and generates ready-to-publish reports. You run included scripts on your dataset to produce full diagnostics and figures automatically.

Generating APA-style reports requires combining test results, effect sizes, and power analyses into a standardized format. This workflow automates those calculations and applies APA-reporting templates to produce ready-to-publish outputs from your raw experimental or observational data.

Yes, the statistical analysis workflow supports Bayesian methods and nonparametric alternatives alongside t-tests, ANOVA, regression, and correlations, applying automated diagnostics and effect size calculations across all test types.

Yes, you can use pandas and scipy datasets for regression and correlation analysis. The workflow processes dataframes to run comprehensive tests, apply automated assumption checks, and output formatted figures using matplotlib and seaborn.

Statistical tests available for experimental and observational data include t-tests, ANOVA, regression, correlations, nonparametric alternatives, and Bayesian methods, all featuring automated effect size calculations and power analyses.

Automated assumption checks validate model prerequisites before running ANOVA or regression, ensuring diagnostic integrity. They automatically verify data distribution and variance criteria to maintain reproducible, ready-to-publish statistical reporting.