Method

Data from several sources were joined together into a merged dataset. We used data in 2017 to build the model. The main outcome is the suicide rate for each state, candidate predictors are gun, alcohol, temperature, precipitation, marijuana, education, GDP and gender for each state. We used stepwise approach to select a model.

Predictor Description
suicide The number of suicide deaths per 100k population
gun Averaged guns number per capita
alcohol Alcohol consumption per capita (gallons of ethanol)
temperature Average temperature (F)
precipitation Average precipitation (inches)
marijuana The proportion of people using marijuana (%)
education Educational attainment - bachelor’s degree or higher (%)
gdp GDP per capita (dollars)
gender Male (%)

Results

Scatter Plot

Correlation plot

Selected model of interest

term estimate p.value
(Intercept) -143.9870 0.0000
gun 0.1220 0.0007
temperature -0.0955 0.0502
marijuana 0.2485 0.0038
education -0.2343 0.0085
gender 3.5589 0.0000
gdp -0.0002 0.0004
r.squared adj.r.squared AIC BIC
0.8354 0.8124 220.9604 236.2566

Visualize model coefficients

  • The “alcohol” and “precipitation” were removed from the model.
  • The fitted equation is “suicide = -143.99 + 0.12gun - 0.10temperature + 0.25marijuana - 0.23education + 3.56gender - 0.0002gdp”.
  • Adjusted R-square is 0.8124, which means these variables can explain a large proportion of variance in the suicide rate.
  • According to the results, the suicide rate is higher in states where there is a higher gun ownership rate, higher marijuana usage, higher ratio of males to females, lower temperature and lower educational attainment.
  • Holding other variables constant, for one unit increase in gun ownership(per 1,000), marijuana use in adults(%), or for one unit decrease in proportion of male (%), GDP per capita (dollars), the suicide rate(per 100k population) will increase by 0.13, 0.27, 3.11 respectively, or decrease by 0.22, 0.0002 on average.