Can you predict the # of drunk driving fatalities over a span of 4 years?

The relationship between the # of drunk driving fatalities in 2006 and 2009 is very strong, positive, and linear with an r^2 value of .99 which is very close to 1. The high r^2 value provides evidence to support that you can use the linear regression model to accurately predict the number of drunk driving fatalities that will be seen in 2009 after a span of 4 years.

High Residual

The residual plot illustrates how far away each of the values on the graph is from the expected value (the value on the line). (Notice that the residual dot on the residual plot that is the furthest away from the line is also the dot that is the furthest away from the line on the linear regression model.)

The high residual dot on the residual plot suggests that the # of drunk driving fatalities that actually occurred for this particular state in 2009 was higher than we expected it would be after the 4 year span based on the linear regression model. So, based on the linear regression model, for a 2006 value of 415 drunk driving fatalities, we would expect the number of drunk driving fatalities in 2009 to be lower than 377. Therefore, this state did not lower their fatalities as much as we expected they might based on the model.

Low Residual

The low residual plot suggests that the actual # of drunk driving fatalities that in 2009 for this particular state was lower than we would have expected it to be after the 4 year span based on the linear regression model. So, based on the linear regression model, for a 2006 value of 439 drunk driving fatalities, we would expect the number of drunk driving fatalities for 2009 to be higher than 313. Therefore, this particular state is doing an exception job at bringing down the number of drunk driving fatalities each year compared to other states.

## Can you predict the # of drunk driving fatalities over a span of 4 years?

The relationship between the # of drunk driving fatalities in 2006 and 2009 is very strong, positive, and linear with an r^2 value of .99 which is very close to 1. The high r^2 value provides evidence to support that you can use the linear regression model to accurately predict the number of drunk driving fatalities that will be seen in 2009 after a span of 4 years.

High ResidualThe residual plot illustrates how far away each of the values on the graph is from the expected value (the value on the line). (Notice that the residual dot on the residual plot that is the furthest away from the line is also the dot that is the furthest away from the line on the linear regression model.)

The

high residualdot on the residual plot suggests that the # of drunk driving fatalities that actually occurred for this particular state in 2009 was higher than we expected it would be after the 4 year span based on the linear regression model. So, based on the linear regression model, for a 2006 value of 415 drunk driving fatalities, we would expect the number of drunk driving fatalities in 2009 to be lower than 377. Therefore, this state did not lower their fatalities as much as we expected they might based on the model.Low ResidualThe

low residualplot suggests that the actual # of drunk driving fatalities that in 2009 for this particular state was lower than we would have expected it to be after the 4 year span based on the linear regression model. So, based on the linear regression model, for a 2006 value of 439 drunk driving fatalities, we would expect the number of drunk driving fatalities for 2009 to be higher than 313. Therefore, this particular state is doing an exception job at bringing down the number of drunk driving fatalities each year compared to other states.