The figure shows density plots of the 10,000 bootstrapped linear model results where the number of drivers who are involved in fatal accidents (labeled: num_drivers and centered to minimum value) and the region of the United States (4 values; Midwest, Northeast, West, and South) were used as attributes to predict / explain variation in the average price of auto insurance (in US dollars). The R command for the linear model was:

lm(insurance_premiums ~ I(num_drivers - min(num_drivers)) + region)

Is there evidence of a relationship in the population between the number of drivers involved in a fatal accident and the average price of insurance premiums and which direction is the relationship? (i.e., is there evidence the relationship is 0 or a different value?)

Options:

Yes, the bootstrapped results show that most of the slopes are positive.

Yes, the bootstrapped results show that most of the slopes are negative.

No, the bootstrapped results show that 0 is a strong plausible value.

(Intercept) num drivers regionNortheast 0.006 . 0.08 0.005 0.06 0.004 0.004 - 0.003 0.04 0.002 - 0.002 0.02 0.001 0.000 - 0.00 0.000 600 800 1000 -30 -20 -10 0 10 0 200 400 density regionSouth regionWest 0.006 - 0.0075 0.004 - 0.0050 0.002 - 0.0025 0.000 - 0.0000 0 100 200 300 400 -100 0 100 200 estimate