Secondary Quantitative Data Analysis

I have an existing, well-structured quantitative dataset and I need an experienced statistician to turn it into clear, actionable insights. Your main task will be to clean the data if necessary, choose and justify the right analytical techniques, run the analyses, and explain the findings in plain language supported by tables and visualisations.

The dataset is already collected, so there is no survey or experimental design work involved—this is purely about extracting value from secondary data. I am comfortable with you using R, Python (pandas / statsmodels), Stata, or SPSS; just let me know which environment you prefer so I can reproduce the results later.

Deliverables:
• A brief outline of the analytical plan before you begin
• Reproducible code or syntax files with comments
• A concise report (Word, PDF, or Markdown) summarising methods, key statistics, graphics, and interpretations

Acceptance criteria:
• All code runs end-to-end on my machine without modification
• Statistical assumptions for each test or model are checked and reported
• Conclusions are tied directly to the output, avoiding over-interpretation

If you have handled similar secondary datasets and can start soon, I’d like to hear how you would approach this project and your estimated turnaround time.