stat-modeling-tools

Perform hypothesis testing and regression analysis on scientific data by automating workflows with Python, utilizing numpy, pandas, scipy, and statsmodels to fit OLS, logistic, or Poisson models and generate reproducible summaries.

Yes, you can use statsmodels to export reproducible statistical summaries for manuscripts by generating machine-readable CSV and JSON outputs containing coefficient tables, inference results, and model diagnostics.

The best way to compare two experimental groups using a t-test on tabular data is to run an independent t-test on your CSV file, reporting effect sizes and p-values, then save the output to JSON and CSV formats.

Yes, this statistical modeling workflow supports continuous, binary, and count data by applying appropriate regression models such as OLS, logistic, and Poisson regression to compute inference and diagnostics.

Yes, you need pandas and scipy installed alongside numpy and statsmodels to handle core statistical computations, data manipulation, and modeling workflows for scientific research.

Generate machine-readable outputs for statistical tests and regression models by automating the analysis workflow to export coefficient tables and inference results directly into CSV and JSON files.