Top variables that correlate with Petroluem consumption in United Kingdom

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Data source: Energy Information Administration
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Petroluem consumption in United Kingdom correlates with...

Variable	Correlation	Years	Sys. Score
The number of janitors and cleaners in Wisconsin	r=0.98	20yrs	393
The number of cashiers in New York	r=0.96	20yrs	390
The number of librarians in Michigan	r=0.97	16yrs	388
Robberies in Alabama	r=0.91	38yrs	378
US average milk-fat content of cottage cheese	r=0.91	22yrs	372
The number of salespeople in Ohio	r=0.95	20yrs	358
The divorce rate in Maryland	r=0.92	23yrs	358
Sherbet consumption	r=0.86	32yrs	354
The marriage rate in Hawaii	r=0.9	23yrs	354
Total value of all $1 bills printed	r=0.85	33yrs	351
Robberies in South Carolina	r=0.89	38yrs	345
Robberies in North Carolina	r=0.88	38yrs	344
US household spending on postage and stationery	r=0.87	23yrs	340
US household spending on clothin for women	r=0.87	23yrs	340
Google searches for 'shook'	r=0.88	19yrs	338
US household spending on clothing	r=0.86	23yrs	338
US production of cottage cheese	r=0.85	22yrs	336
The marriage rate in Oregon	r=0.89	23yrs	322
How geeky SciShow Space YouTube video titles are	r=0.91	9yrs	318
The number of travel agents in New York	r=0.94	20yrs	317
Robberies in Arizona	r=0.86	38yrs	310
China's Rare Earth Element Export Volume	r=0.9	11yrs	309
The number of heat treating equipment setters, operators, and tenders, metal and plastic in New York	r=0.94	20yrs	307
US dairy skim solids used to produce cottage cheese	r=0.85	22yrs	304
Robberies in Pennsylvania	r=0.86	38yrs	300
Intercountry adoptions	r=0.87	15yrs	298
US Motorcycle Manufacturing Industry Revenue	r=0.89	12yrs	298
The number of salespeople in Connecticut	r=0.93	20yrs	296
The marriage rate in Alaska	r=0.88	23yrs	291
The number of surgens in Alabama	r=0.95	16yrs	284
The marriage rate in Louisiana	r=0.88	23yrs	281
The number of bellhops in California	r=0.91	20yrs	274
The divorce rate in Texas	r=0.87	23yrs	271
The number of cooks, short order in Michigan	r=0.92	20yrs	264
The marriage rate in Florida	r=0.87	23yrs	260
The number of salespeople in Michigan	r=0.92	20yrs	254
Popularity of the first name Tania	r=0.82	48yrs	250
Popularity of the first name Marlon	r=0.82	48yrs	250
Popularity of the first name Aric	r=0.82	48yrs	250
The marriage rate in Massachusetts	r=0.87	23yrs	250
NASA's budget as a percentage of the total US Federal Budget	r=0.76	48yrs	249
The number of millwrights in Montana	r=0.92	20yrs	244
US Employment Rate	r=0.84	33yrs	240
The marriage rate in Virginia	r=0.86	23yrs	238
The number of salespeople in New Jersey	r=0.92	20yrs	234
Emmy Awards Ceremony viewership	r=0.78	36yrs	234
U.S. intercountry adoptions	r=0.85	23yrs	232
US household spending on clothing for men	r=0.84	23yrs	230
The divorce rate in Oregon	r=0.85	23yrs	228
Global Per Capita Rice Consumption	r=0.85	20yrs	228
Google searches for 'panama canal'	r=0.84	19yrs	228
GMO use in cotton	r=0.8	23yrs	227
The number of procurement clerks in New York	r=0.92	20yrs	224
Cigarette Smoking Rate for US adults	r=0.81	21yrs	224
Physical album shipment volume in the United States	r=0.79	24yrs	224
The distance between Neptune and the Sun	r=0.62	48yrs	224
US household spending on mortgage interest and charges	r=0.8	23yrs	224
The marriage rate in Nebraska	r=0.85	23yrs	216
The number of travel agents in Pennsylvania	r=0.92	20yrs	214
Popularity of the first name Tyron	r=0.77	48yrs	213
Milk consumption	r=0.85	32yrs	210
The number of waiters and waitresses in Rhode Island	r=0.92	20yrs	204
Popularity of the first name Shelton	r=0.76	48yrs	202
The number of computer programmers in Tennessee	r=0.91	20yrs	194
Ice cream consumption	r=0.82	32yrs	194
Popularity of the first name Maria	r=0.74	48yrs	188
The number of travel agents in Wisconsin	r=0.92	20yrs	184
Robberies in Tennessee	r=0.84	38yrs	182
Cottage cheese consumption	r=0.8	32yrs	180
Popularity of the first name Kelvin	r=0.74	48yrs	178
The number of library assistants in Georgia	r=0.91	20yrs	172
Robberies in Virginia	r=0.84	38yrs	172
Popularity of the first name Kenya	r=0.74	48yrs	168
The number of heat treating equipment setters, operators, and tenders, metal and plastic in Massachusetts	r=0.91	20yrs	162
Burglaries in North Carolina	r=0.83	38yrs	161
Popularity of the first name Viridiana	r=0.77	43yrs	158
US average milk-fat content of milk fat and skim solids byproduct fluid beverage milk	r=0.82	22yrs	153
Motor vehicle thefts in Georgia	r=0.82	38yrs	150
Popularity of the first name Rosario	r=0.74	48yrs	148
Portion of all US dairy skim-solids allocated to the production of frozen dairy products	r=0.81	22yrs	142
Motor vehicle thefts in Maryland	r=0.82	38yrs	140
Popularity of the first name Ruben	r=0.73	48yrs	137
Robberies in Ohio	r=0.82	38yrs	130
Popularity of the first name Cindy	r=0.73	48yrs	127
Robberies in Indiana	r=0.81	38yrs	118
Popularity of the first name Brenda	r=0.72	48yrs	116
Robberies in Mississippi	r=0.81	38yrs	108
Popularity of the first name Manuel	r=0.72	48yrs	106
Motor vehicle thefts	r=0.67	38yrs	97
Popularity of the first name Roberto	r=0.72	48yrs	96
Robberies in the US	r=0.67	38yrs	87
Popularity of the first name Marlene	r=0.72	48yrs	86
Popularity of the first name Rolando	r=0.72	48yrs	76
Robberies in Nevada	r=0.8	38yrs	76
Popularity of the first name Brook	r=0.72	48yrs	66
Robberies in Arkansas	r=0.8	38yrs	66
Popularity of the first name Lourdes	r=0.72	48yrs	56
Arson in Missouri	r=0.79	38yrs	55
Burglaries in Mississippi	r=0.79	38yrs	45
Popularity of the first name Raul	r=0.71	48yrs	44
Robberies in Delaware	r=0.79	38yrs	35

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^{What is Sys. Score?} 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.)


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