Related Experiment Video
Updated: May 10, 2026

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
Published on: January 29, 2018
Correlates of tanning facility densities in North Carolina
Julianne Treme1, Samuel K Allen
1Department of Economics and Finance, University of North Carolina at Wilmington, Wilmington, North Carolina, USA.
Background:
The indoor tanning industry is currently receiving increased attention from policymakers, but this industry has not been well researched. Our study examines economic, demographic, and climate-related variables to better understand variations among North Carolina counties in terms of the number of tanning beds and booths per capita during a recent 3-year period.
Methods:
This study used regression analysis to estimate the magnitude and statistical significance of correlations between the density of tanning beds and other relevant variables from 2007 through 2009.
Results:
The number of indoor tanning beds per capita in a county is positively correlated with the county's unemployment rate and with the proportion of the county's population that consists of white females 18-49 years of age; there is also a weakly positive correlation with the number of days per year of hot weather in the county. All else being equal, tanning beds are marginally more common in counties with higher rates of unemployment, with a greater number of days when the temperature exeeds 90 degrees Fahrenheit, and with residents who are more likely to engage in risky behaviors (as measured by the gonorrhea infection rate and the percentage of the population who smoke cigarettes).
Limitations:
The data span a 3-year period (2007-2009) during which economic conditions were depressed.
Conclusion:
Economic, demographic, geographic, and climate-related factors should be considered when policies that affect the tanning industry in North Carolina are being developed and implemented.
Related Concept Videos
Correlation and Causation
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Calculating and Interpreting the Linear Correlation Coefficient
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Correlations
Scatter Plot
