Publish-Ready Stats Modeling Paper

I am in the final stretch of an applied-mathematics paper centred on inferential statistical analysis carried out in Python, and I want to push it over the line to journal-ready quality. The dataset is already cleaned and the preliminary models are coded; what I need now is a skilled collaborator who can tighten both the analytics and the manuscript itself.

Here’s what the assignment looks like from my side:

• Review and, where necessary, refine the existing inferential tests and mathematical modelling logic in the Python notebook (NumPy, SciPy, pandas, statsmodels are already in use).
• Generate publication-grade visualisations and summary tables that meet typical journal standards.
• Suggest and implement any additional, defensible statistical checks that strengthen the results section.
• Edit and format the manuscript (LaTeX or Word—your choice) so that it aligns with target-journal guidelines, including citation style, figure placement, and concise mathematical exposition.
• Deliver a clean, reproducible Python script/notebook alongside the final manuscript and a short, clear methodology note so reviewers can follow each step without ambiguity.

If you are comfortable with inferential statistics, applied mathematical framing, and Python-based data analytics—and can turn the above into a polished, publish-ready package—I’d love to work together.