Correlation Analysis
Explore statistical relationships between Wyoming's economic and demographic indicators using Pearson correlation coefficients.
Understanding Correlation Analysis
Correlation analysis measures the strength and direction of relationships between two variables. A correlation coefficient (Pearson's r) ranges from -1 to +1:
- +1.0: Perfect positive correlation (as one increases, the other increases proportionally)
- 0.0: No correlation (variables are independent)
- -1.0: Perfect negative correlation (as one increases, the other decreases proportionally)
Common Correlations
GDP per Capita vs Median Income
How closely does economic output track household income?
View AnalysisEmployment Ratio vs Labor Force Participation
Relationship between employment and labor force participation.
View AnalysisAvailable Metrics by Level
State Level
- • Unemployment Rate
- • Population Change %
- • GDP per Capita
- • Median Household Income
- • Employment-Population Ratio
- • Labor Force Participation Rate
- • State Population
- • Total Employment
County Level
- • Unemployment Rate
- • Population Change %
- • GDP per Capita
- • Median Household Income
- • Employment-Population Ratio
- • County Population
- • Employment
🏭 Industry Level
- • Sector Employment
- • Average Wage
- • Year-over-Year Growth %
- • Wage Growth %
- • Jobs per 1,000 People
- • Sector Employment %
API Usage
Use the correlation API to analyze relationships between any two metrics:
GET /api/comprehensive/correlations?metric1=unemployment_rate&metric2=population_change_pct&startYear=2000Parameters:
metric1(required) - First metric to analyzemetric2(required) - Second metric to analyzelevel(optional) - Analysis level: state, county, industry (default: state)startYear(optional) - Starting year for analysisendYear(optional) - Ending year for analysiscounty(optional) - Specific county name (if level=county)industry(optional) - Specific industry sector (if level=industry)
Important Note
Correlation does not imply causation. A strong correlation between two variables indicates they tend to move together, but it doesn't prove that one causes the other. Other factors (confounding variables) may influence both metrics, or the relationship may be coincidental.