Top variables that correlate with Popularity of the first name Nestor

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Data source: US Social Security Administration
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Popularity of the first name Nestor correlates with...

Variable	Correlation	Years	Sys. Score
Gasoline pumped in Latvia	r=0.95	31yrs	418
Gasoline pumped in Finland	r=0.92	43yrs	396
Gasoline pumped in Spain	r=0.91	43yrs	394
Lloyds Banking Group's stock price (LYG)	r=0.9	21yrs	392
The number of receptionists in Delaware	r=0.96	20yrs	392
The number of broadcast technicians in Puerto Rico	r=0.97	20yrs	392
The number of switchboard operators in Arizona	r=0.96	20yrs	390
The marriage rate in Wyoming	r=0.95	23yrs	382
Physical album shipment volume in the United States	r=0.94	24yrs	381
Robberies in South Carolina	r=0.93	38yrs	381
Milk consumption	r=0.95	32yrs	380
The marriage rate in Idaho	r=0.94	23yrs	380
Motor vehicle thefts in Georgia	r=0.92	38yrs	380
Robberies in North Carolina	r=0.92	38yrs	380
The divorce rate in Alabama	r=0.92	23yrs	378
U.S. intercountry adoptions	r=0.91	23yrs	376
Google searches for 'learn spanish'	r=0.93	19yrs	376
Cottage cheese consumption	r=0.93	32yrs	375
Google searches for 'shook'	r=0.92	19yrs	374
xkcd comics published about romance	r=0.91	16yrs	374
US milk fat used to produce fluid beverage milk	r=0.9	22yrs	373
Google searches for 'snoop dog'	r=0.9	19yrs	372
The number of conveyor operators in Arkansas	r=0.96	20yrs	360
AIG's stock price (AIG)	r=0.89	21yrs	360
Citigroup's stock price (C)	r=0.88	21yrs	359
GMO use in cotton	r=0.91	23yrs	358
Global Per Capita Rice Consumption	r=0.93	20yrs	356
US household spending on floor coverings	r=0.91	23yrs	356
US household spending on clothing	r=0.91	23yrs	356
US household spending on clothin for women	r=0.91	23yrs	356
US household spending on postage and stationery	r=0.9	23yrs	354
Petroluem consumption in Germany	r=0.9	32yrs	352
The number of CEOs in New Hampshire	r=0.95	20yrs	350
The divorce rate in North Carolina	r=0.92	23yrs	348
Motor vehicle thefts in Maryland	r=0.91	38yrs	348
US birth rates of triplets or more	r=0.89	20yrs	347
Air pollution in Blacksburg, Virginia	r=0.9	33yrs	347
GMO use in corn grown in Texas	r=0.94	18yrs	341
The number of file clerks in New Mexico	r=0.96	20yrs	340
GMO use in cotton in Texas	r=0.92	23yrs	340
Robberies in Virginia	r=0.91	38yrs	338
The marriage rate in Nevada	r=0.92	23yrs	337
The number of painting, coating, and decorating workers in New York	r=0.96	20yrs	330
HSBC Holdings' stock price (HSBC)	r=0.87	21yrs	328
The divorce rate in Kentucky	r=0.91	23yrs	327
Motor vehicle thefts in Indiana	r=0.9	38yrs	326
US average milk-fat content of cottage cheese	r=0.94	22yrs	326
The number of boiler operators in Ohio	r=0.96	20yrs	320
Gasoline pumped in Switzerland	r=0.87	43yrs	318
The divorce rate in Wyoming	r=0.91	23yrs	317
Robberies in Kansas	r=0.9	38yrs	316
Barclays' stock price (BCS)	r=0.86	21yrs	316
Portion of all US dairy skim-solids allocated to the production of fluid beverage milk	r=0.93	22yrs	314
Google searches for 'oprah winfrey'	r=0.9	19yrs	312
The number of typists in Pennsylvania	r=0.96	20yrs	310
The marriage rate in Arkansas	r=0.91	23yrs	306
Portion of all US dairy skim-solids allocated to the production of cottage cheese	r=0.93	22yrs	304
Orange's stock price (ORAN)	r=0.85	21yrs	304
The number of brickmasons in South Carolina	r=0.96	20yrs	300
Gasoline pumped in Germany	r=0.89	32yrs	300
The marriage rate in Alabama	r=0.9	23yrs	294
Portion of all US dairy skim-solids allocated to the production of frozen dairy products	r=0.92	22yrs	293
The number of prepress technicians and workers in Texas	r=0.96	20yrs	290
Arson in Idaho	r=0.9	22yrs	290
Kerosene used in Montserrat	r=0.86	35yrs	288
The marriage rate in South Dakota	r=0.9	23yrs	284
US average milk-fat content of frozen dairy products	r=0.92	22yrs	283
The number of prepress technicians and workers in Virginia	r=0.96	20yrs	280
Arson in Iowa	r=0.9	22yrs	280
The marriage rate in Virginia	r=0.9	23yrs	274
Petroluem consumption in Uzbekistan	r=0.86	30yrs	274
US average milk-fat content of milk fat and skim solids byproduct fluid beverage milk	r=0.91	22yrs	272
The number of compensation and benefits managers in District of Columbia	r=0.96	19yrs	269
Motor vehicle thefts	r=0.85	38yrs	269
Liquefied petroleum gas used in Greenland	r=0.89	22yrs	260
The number of postal service machine operators in District of Columbia	r=0.96	19yrs	259
The number of compensation and benefits managers in Idaho	r=0.96	19yrs	249
Burglaries in North Carolina	r=0.9	38yrs	246
The number of archivists in Illinois	r=0.96	19yrs	239
Arson in Missouri	r=0.89	38yrs	235
The divorce rate in Mississippi	r=0.89	23yrs	234
The number of compensation and benefits managers in Tennessee	r=0.96	19yrs	229
Motor vehicle thefts in Virginia	r=0.89	38yrs	225
The marriage rate in Alaska	r=0.89	23yrs	222
The number of data entry keyers in District of Columbia	r=0.95	20yrs	218
Motor vehicle thefts in Florida	r=0.89	38yrs	215
The marriage rate in Arizona	r=0.89	23yrs	212
The number of file clerks in Massachusetts	r=0.95	20yrs	208
Robberies in Pennsylvania	r=0.89	38yrs	205
The marriage rate in Kentucky	r=0.89	23yrs	202
The number of file clerks in Mississippi	r=0.95	20yrs	198
Motor vehicle thefts in Ohio	r=0.88	38yrs	194
The marriage rate in Louisiana	r=0.89	23yrs	192
Robberies in Maryland	r=0.88	38yrs	184
The divorce rate in Nevada	r=0.88	23yrs	182
Robberies in Arkansas	r=0.88	38yrs	174
The divorce rate in Oregon	r=0.88	23yrs	172
The marriage rate in Kansas	r=0.88	23yrs	161
Robberies in Missouri	r=0.87	38yrs	152
The divorce rate in Arkansas	r=0.87	23yrs	151
Violent crime rates	r=0.85	38yrs	94

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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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