I have completed a qualitative survey study in Python and now need a fresh set of expert eyes on one critical stage: data interpretation. The dataset has already been cleaned, coded, and themed; where I need help is checking whether my statistical analysis truly backs the insights I claim. Here’s the current setup: • Framework in use: Statistical analysis applied to previously coded qualitative responses. • Techniques employed: multivariate analysis using pandas, NumPy, and scikit-learn. • All scripts and Jupyter notebooks are well-commented and version-controlled on GitHub. What I need from you: 1. Review my multivariate outputs (factor analyses, clusters, regressions) and confirm they align with the qualitative coding structure. 2. Spot any misinterpretations or over-extensions in the narrative I draw from those statistics. 3. Suggest concrete adjustments—whether it’s a different model, an additional diagnostic test, or clearer wording—for a more defensible results section. 4. Deliver a concise audit report (bullet points or short memo) and inline comments in the notebook so changes are easy to follow. You’ll succeed quickly if you’re comfortable moving between qualitative concepts and quantitative validation, and can articulate feedback in plain language. I aim to integrate your recommendations immediately, so responsiveness and clarity are key.