## Most strongly related columns I computed Pearson correlations across all 62,496 rows for the five numeric measures plus average time on page. That gives 15 pairs (step 1 query). Only two pairs are strongly correlated, and two more are moderate. **Strong relationships** - **BOUNCE RATE (%) and EXIT RATE (%): r = 0.79.** This is the strongest pair. Pages where visitors leave after one page also tend to be pages where visitors leave from, which fits the pattern of single-page visits being counted in both. - **PAGE VIEWS and ENTRANCES: r = 0.79.** Pages that get more views are also more often where sessions start. This suggests traffic is driven mainly by landing pages rather than by internal navigation. **Moderate relationships** - **UNIQUE VIEWS and ENTRANCES: r = 0.38.** - **PAGE VIEWS and UNIQUE VIEWS: r = 0.33.** This is lower than I would expect for two volume metrics that measure similar things. Pearson correlation is sensitive to a few very large pages, so the heavy-tailed volumes in this data likely weaken the link. I did not test that, and a rank (Spearman) correlation would check it. **Essentially unrelated** - All other pairs have |r| below 0.08, which is effectively zero. - **AVERAGE TIME ON PAGE (SECONDS)** is nearly independent of everything else. Its largest correlation is -0.07 with exit rate and -0.05 with bounce rate. Time on page does not predict bounce or exit here, nor does it track traffic volume. - Bounce and exit rates are uncorrelated with the volume measures (page views, unique views, entrances), all with |r| ≤ 0.012. High-traffic pages are not systematically better or worse at retaining visitors. ## What this means for the decisions - Treat bounce and exit as one signal. Fixing high-exit pages will overlap heavily with fixing high-bounce pages. - To find pages worth optimizing as entry points, look at page views and entrances together, since they move together. - Because rates are independent of volume, a page's bounce or exit problem has to be judged on its own rate, not inferred from how popular it is. These are linear correlations on page-level rows, so they show association, not causation. They were computed across all websites and years pooled together, so they may differ within a single site or year.