Highway crash data analysis. Researchers at Montana State University have written a tutorial on an empirical method for analyzing before and after highway crash data (Montana Department of Transportation, Research Report, May 2004). The initial step in the methodology is to develop a safety per formance function (SPF)—a mathematical model that estimates the probability of occurrence of a crash for a given segment of roadway. Using data on over 100 segments of roadway, the researchers fit the model E1y2 = b0 + b1 x1 + b2 x2, where y = number of crashes per three years, x1 = roadway length (miles), and x2 = average annual daily traffic 1number of vehicles2 =

AADT. The results are shown in the following tables.

a. Give the least squares prediction equation for the interstate highway model.

b. Give practical interpretations of the b estimates you made in part a.

c. Refer to part

a. Find a 95% confidence interval for b1 and interpret the result.

d. Refer to part

a. Find a 95% confidence interval for b2 and interpret the result.

e. Repeat parts a–d for the non-interstate-highway model.

Transcribed Image Text:

Interstate Highways Parameter Standard Variable Estimate Error t-Value Intercept 1.81231 .50568 3.58 Length (x1) .10875 .03166 3.44 AADT (x2) .00017 .00003 5.19 Non-Interstate Highways Parameter Standard Variable Estimate Error t-Value Intercept 1.20785 .28075 4.30 Length (x1) .06343 .01809 3.51 AADT (x2) .00056 .00012 4.86