Statistics for Business and Economics, Student Value Edition (13th Edition)

13th Edition

ISBN: 9780134456997

Author: James T. McClave, P. George Benson, Terry Sincich

Publisher: PEARSON

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Textbook Question

Chapter 12, Problem 12.148ACB

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 Performance Function (SPF)—a mathematical model that estimates crash occurrence for a given roadway segment. Using data collected for over 100 roadway segments, the researchers fit the model, E (y) = β₀ + β₀x₁+ β₀x₂, where y = number of crashes per 3 years, x₁ = roadway length (miles), and x₂ = AADT = average annual daily traffic (number of vehicles). The results are shown in the following tables.

Interstate Highways

Chapter 12, Problem 12.148ACB, Highway crash data analysis. Researchers at Montana State University have written a tutorial on an , example 1

Noninterstate Highways

Chapter 12, Problem 12.148ACB, Highway crash data analysis. Researchers at Montana State University have written a tutorial on an , example 2

a. Give the least squares prediction equation for the interstate highway model.
b. Give practical interpretations of the β estimates, part a.
c. Refer to part a. Find a 99% confidence interval for β₁ and interpret the result.
d. Refer to part a. Find a 99% confidence interval for β₂ and interpret the result.
e. Repeat parts a-d for the noninterstate highway model.
f. Write a first-order model for E(y) as a function of x₁ and x₂ that allows the slopes to differ depending on whether the roadway segment is Interstate or non-interstate. [Hint: Create a dummy variable for Interstate/non-interstate.]

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Chapter 12, Problem 12.147ACB

Chapter 12, Problem 12.149ACB

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Chapter 12 Solutions

Statistics for Business and Economics, Student Value Edition (13th Edition)

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Solution Summary: The author explains the least square prediction equation for the interstate highway model: stackrely=1.81231+(0.10875)x_1+

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