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Global Puma Sales correlates with...
Variable | Correlation | Years | Has img? |
Netflix's stock price (NFLX) | r=0.98 | 17yrs | No |
Alphabet's stock price (GOOGL) | r=0.98 | 17yrs | No |
NIKE, Inc.'s stock price (NKE) | r=0.97 | 17yrs | No |
Sales of LP/Vinyl Albums | r=0.97 | 17yrs | No |
Nasdaq's stock price (NDAQ) | r=0.97 | 17yrs | No |
Eli Lilly and Company's stock price (LLY) | r=0.97 | 17yrs | No |
Nuclear power generation in China | r=0.96 | 16yrs | No |
The number of interior designers in New York | r=0.95 | 17yrs | No |
Annual US household spending on beef | r=0.94 | 17yrs | No |
American cheese consumption | r=0.94 | 16yrs | Yes! |
The number of RV service technicians in Idaho | r=0.92 | 17yrs | Yes! |
Tesla's stock price (TSLA) | r=0.9 | 12yrs | No |
The number of fast food cooks in Kentucky | r=0.86 | 17yrs | No |
Global Puma Sales also correlates with...
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You caught me! While it would be intuitive to sort only by "correlation," I have a big, weird database. If I sort only by correlation, often all the top results are from some one or two very large datasets (like the weather or labor statistics), and it overwhelms the page.
I can't show you *all* the correlations, because my database would get too large and this page would take a very long time to load. Instead I opt to show you a subset, and I sort them by a magic system score. It starts with the correlation, but penalizes variables that repeat from the same dataset. (It also gives a bonus to variables I happen to find interesting.)