FACTOID # 2: Wisconsin has more metal fabricators per capita than any other state.
 
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Correlations > Labor Statistics > Electrical equipment, appliance, & component manufacturing > Number of employees

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Showing latest available data.

Correlations between Labor > Electrical equipment, appliance, & component manufacturing > Number of employees ...

Variable Strength
...and  Industry > Electrical equipment, appliance, & component manufacturing > Total value of shipments 83% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Electrical equipment and appliance manufacturing 80% [plot | correlate | graph]
...and  Industry > Electrical equipment, appliance, & component manufacturing > Value added 80% [plot | correlate | graph]
...and  Industry > Electrical equipment, appliance, & component manufacturing > Total cost of materials 78% [plot | correlate | graph]
...and  Industry > Electrical equipment, appliance, & component manufacturing > Total capital expenditures 78% [plot | correlate | graph]
...and  Industry > Electrical equipment, appliance, & component manufacturing > Annual payroll 77% [plot | correlate | graph]
...and  Economy > GSP > Real GSP > Electrical equipment and appliance manufacturing 75% [plot | correlate | graph]
...and  Economy > GSP > Nominal GSP > Electrical equipment and appliance manufacturing 75% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Plastics and rubber products manufacturing 71% [plot | correlate | graph]
...and  Labor > Paper manufacturing > Production workers (average per year) 70% [plot | correlate | graph]
...and  Labor > Paper manufacturing > Number of employees 69% [plot | correlate | graph]
...and  Industry > Plastics & rubber products manufacturing > Value added 69% [plot | correlate | graph]
...and  Industry > Plastics & rubber products manufacturing > Total value of shipments 68% [plot | correlate | graph]
...and  Economy > GSP > Real GSP > Machinery manufacturing 68% [plot | correlate | graph]
...and  Economy > GSP > Nominal GSP > Machinery manufacturing 68% [plot | correlate | graph]
...and  Industry > Fabricated metal product manufacturing > Total capital expenditures 67% [plot | correlate | graph]
...and  Industry > Fabricated metal product manufacturing > Value added 66% [plot | correlate | graph]
...and  Economy > GSP > Real GSP > Plastics and rubber products manufacturing 66% [plot | correlate | graph]
...and  Economy > GSP > Nominal GSP > Plastics and rubber products manufacturing 66% [plot | correlate | graph]
...and  Industry > Plastics & rubber products manufacturing > Annual payroll 66% [plot | correlate | graph]
...and  Industry > Fabricated metal product manufacturing > Total value of shipments 66% [plot | correlate | graph]
...and  Economy > GSP > Gross Operating Surplus > Fabricated metal product manufacturing 66% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Machinery manufacturing 65% [plot | correlate | graph]
...and  Economy > GSP > Real GSP > Fabricated metal product manufacturing 65% [plot | correlate | graph]
...and  Economy > GSP > Nominal GSP > Fabricated metal product manufacturing 65% [plot | correlate | graph]
...and  Industry > Plastics & rubber products manufacturing > Total cost of materials 65% [plot | correlate | graph]
...and  Industry > Fabricated metal product manufacturing > Production workers wages 65% [plot | correlate | graph]
...and  Industry > Fabricated metal product manufacturing > Total cost of materials 64% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Fabricated metal product manufacturing 63% [plot | correlate | graph]
...and  Labor > Total Manufacturing > Production workers hours 63% [plot | correlate | graph]
...and  Industry > Fabricated metal product manufacturing > Annual payroll 63% [plot | correlate | graph]
...and  Industry > Machinery manufacturing > Total value of shipments 63% [plot | correlate | graph]
...and  Labor > Electrical equipment, appliance, & component manufacturing > Production workers hours 62% [plot | correlate | graph]
...and  Industry > Machinery manufacturing > Value added 62% [plot | correlate | graph]
...and  Industry > Machinery manufacturing > Total cost of materials 62% [plot | correlate | graph]
...and  Industry > Total Manufacturing > Production workers wages 62% [plot | correlate | graph]
...and  Labor > Plastics & rubber products manufacturing > Production workers (average per year) 62% [plot | correlate | graph]
...and  Labor > Fabricated metal product manufacturing > Production workers (average per year) 62% [plot | correlate | graph]
...and  Labor > Fabricated metal product manufacturing > Number of employees 62% [plot | correlate | graph]
...and  Industry > Plastics & rubber products manufacturing > Total capital expenditures 61% [plot | correlate | graph]
...and  Labor > Machinery manufacturing > Production workers hours 59% [plot | correlate | graph]
...and  Industry > Machinery manufacturing > Annual payroll 59% [plot | correlate | graph]
...and  Industry > Total Manufacturing > Value added 59% [plot | correlate | graph]
...and  Industry > Electrical equipment, appliance, & component manufacturing > Production workers wages 58% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Paper manufacturing 58% [plot | correlate | graph]
...and  Labor > Total Manufacturing > Number of employees 57% [plot | correlate | graph]
...and  Industry > Machinery manufacturing > Production workers wages 57% [plot | correlate | graph]
...and  Industry > Food manufacturing > Value added 57% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Truck transportation 57% [plot | correlate | graph]
...and  Economy > GSP > Compensation of Employees > Printing and related support activities 56% [plot | correlate | graph]
Average: 66%

About Correlations:

A correlation is a statistical measure of similarity between at least two given sets of data. StateMaster's correlations compare two variables from our database and reveal statistical relationships between them. The percentages you see represent the strength (or likelihood) that a change in the topic variable is matched by a change in the listed variables below it. But remember: These correlations do not imply causation, that is, one does not necessarily cause the other. Also, not all variables contain all states, rather subsets of states matched together.

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NOTES:

  • Outliers have been removed only where they are outside 3 standard deviations of the mean.
  • Only variable pairs where at least 15 states match for each have been considered.
  • Strength is given by the correlation coefficient (R squared). It is the fraction of variation in Y that can be attributed to the variation in X. 100% signifies a perfect fit (R squared of 1). The top 50 such stats are displayed



DEFINITION: This item includes all full-time and part-time employees on the payrolls of operating manufacturing establishments during any part of the pay period that included the 12th of the months specified on the report form. Included are employees on paid sick leave, paid holidays, and paid vacations; not included are proprietors and partners of unincorporated businesses.

SOURCE: Annual Survey of Manufactures, 2004

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