research-statistics

To perform an independent samples t-test with APA reporting, you provide two data series to the analysis workflow. The system executes the test using statsmodels and automatically formats the statistical results into an APA-compliant output.

Yes, you can run Bayesian hierarchical models using pymc. The system integrates MCMC sampling and provides arviz for diagnostics, allowing you to perform complex Bayesian inference on your research datasets.

The best way to conduct survival analysis is by using the integrated suite which supports both Cox proportional hazards and Random Survival Forests (RSF). It leverages scikit-survival to process time-to-event data accurately.

You check statistical assumptions before running ANOVA by following the guided workflow. The system provides step-by-step assumption checking to ensure your data meets requirements before proceeding with frequentist tests.

Yes, the statistical analysis suite supports time series and regression. It includes comprehensive frequentist methods powered by statsmodels and scipy, covering everything from basic regressions to complex time series forecasting.

You need pandas and numpy for academic data analysis because they serve as the foundational dependencies for data manipulation. The system requires these libraries to structure your datasets before applying advanced statistical tests.