I will share an experimental data set that tracks more than five variables per case. My priority is to uncover how these variables relate to one another, so I need a thorough correlation study—Pearson where assumptions hold, and an alternative (e.g., Spearman) when they do not—alongside the classic descriptive stats: mean, standard deviation, and any other summary measures you believe add insight. You are free to work in the environment you prefer—Python (pandas, SciPy, seaborn), R (tidyverse, ggplot2), SPSS, Stata, or any other tool that lets you produce reproducible results. Visual clarity matters, so please include readable tables and at least one visual overview such as a correlation heat-map or scatter-matrix. Deliverables • A short, well-structured report (PDF, HTML, or Jupyter Notebook) that walks through the methods, assumptions checked, findings, and practical interpretation. • Annotated code or syntax so I can replicate every step. • A note on any preprocessing decisions (missing values, outliers, normalization) so the analytical path is transparent. If you can meet those points, I look forward to seeing how you expose the hidden patterns in my data.