An Analysis of Car Crash Fatalities

An Analysis of Car Crash Fatalities: Introduction

The objective of this paper is to construct a quantitative, multiple regression model to predict fatality rates of car crashes in the U.S. (dependent variable) in terms of a number of variables that are thought to play a role in explaining the variation of the dependent variable. The original list of predictors to be used are fatalityrate (Number of fatalities per million of traffic miles), year, fips (State ID Code), vmt (Millions of traffic miles per year), sb_useage (Seat belt usage rate), speed65 (Binary variable for 65 mile per hour speed limit), speed70 (Binary variable for 70 or higher mile per hour speed limit), drinkage21 (Binary variable for age 21 drinking age), ba08 (Binary variable for blood alcohol limit ≤ .08%), income (Per capita income), age (mean age), primary (Binary variable for primary enforcement of seat belt laws), secondary (Binary variable for secondary enforcement of seat belt laws), and t (Number of years since 1983).

Literature Reference

The dataset used for the analysis comes from a balanced panel from 50 U.S. States (along with the District of Columbia), corresponding to years 1983 through 1997. The data comes from a study by Professor Liran Einav and Alma Cohen named The Effects of Mandatory Seat Belt Laws on Driving Behavior and Traffic Fatalities ".

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