Background of the Study
A significant number of South Koreans are killed annually due to the road accident menace. The roads highways and superhighways are no longer just a means of necessitating movement of goods and people within the nation but are also a major source of loss of lives. The nation records alarming rates of road accidents that have been greatly attributed to human error. Reckless driving has been identified as a major cause of road accidents. Among the key perpetrators of this vice are drunken drivers, those drivers who do not abide by the traffic laws and sometimes failure of vehicle breaks. Some vehicles are also not roadworthy and end up causing accidents.
Statement of the Problem
The problem of road accidents is not a new trend. There have been numerous cases of road accidents in South Korea for a long time. Various mechanisms have been put in place to curb this situation but these have proven futile. This is because a lot of lives continue to be lost and injuries sustained due to road accidents. However, an issue of major concern is the frequency and fatality of road accidents over recent years.
This situation can be attributed to the increased number of cars that run on South Korean roads, highways and superhighways. The increased number of cars is associated with an increase in individual’s income and thus the nation’s Gross Domestic Product (GDP).This research paper set out to study if indeed there is a relationship existing between the GDP of the nation and the number of car crash.
Justification of the Study
This study was conducted due to serious concerns about the increasing number of road accident-related deaths and injuries. It will help in providing solutions towards reducing the menace.
This study set out to determine whether there exists a relationship between there exists a relationship between changes in income and the number of car crash incidents. In other words, they study tested whether an increase in income leads to an increase in car crash incidents and vice versa. The null hypothesis tested was whether an increase in income leads to an increase in the number of car crash incidents in the country.
The study involved the collection of information on a number of variables. These included the Gross Domestic Product (GDP), the number of registered cars, number of road traffic accidents and Years. The years covered were from the year 2000 up to the year 2010. This data was collected personally. The data was analyzed and the calculated values are interpreted below.
Data Analysis and Interpretation
The data collected was analyzed in two ways: simple regression analysis and multiple regression analysis. The simple regression analysis for the study involved testing the impact of registered vehicle on individual’s income (GDP). This meant that number of registered vehicles was used as the explanatory variable while the GDP was used as the dependent variable.
The multiple regression analysis involved understanding the impact of number of registered cars and road traffic accidents on the GDP of the country. Own data was collected for the period between 2000 and 2010 on all the variables: GDP, year, number of traffic road accidents, number of registered cars.
Simple Regression Analysis
Simple regression was carried out on GDP (endogenous variable) and registered vehicle (exogenous variable).
Multiple R: this is the coefficient of correlation. It was found to be 0.984624. This implies that there is a strong positive correlation between GDP and the number of registered cars.
R square: it was calculated as 0.969485. This is the coefficient of determination otherwise known as the goodness of fit. This figure implied that the number of registered vehicles accounts for 96.95% of all changes experienced in GDP. The 3.05% that is unexplained are accounted for by other independent variables.
Adjusted R square: it was calculated as 0.966095. It is the coefficient of determination adjusted to degrees of freedom. It implies that 96.61% of all changes in GDP the number of registered are accounted for by the number of registered cars.
p-value: A researcher will often "reject the null hypothesis" when the p-value turns out to be less than a certain significance level, often 0.05 (Stigler 2008).
p-value for intercept was calculated as 0.000042336. It is significant at 0.05 significant level. p-value for GDP= 3.97E-08 is also significant at 0.05.
Multiple regression analysis
The multiple regressions that were conducted were based on the number of car crash (dependent variable) and Year & GDP (independent variables).
Multiple R for multiple regression analysis was 0.738342.
This indicated that there was a strong positive relationship between the exogenous variables (Year & GDP), and the number of car crash.
R square calculated was equal to 0.545149
According to the results of the multiple regressions, 54.5149% of all changes in number of car crashes can be explained by the GDP of South Korea for the years between 2000-2010.
Adjusted R square equated 0.431436
If all other factors are held constant, 43.1436% of the changes in number of car crashes over the years are explained by the GDP when adjusted for degrees of freedom.
t-statistic for year= 0.868502
The null hypothesis was rejected. The model was not significant in this case.
t-statistic for GDP= -1.19926
The tested hypothesis revealed that the null hypothesis rejected. The income of individuals was not significant in the model.
p-value intercept=0.416096, p-value for year =0.410416 and p-value for GDP=0.264738
The p-value was used to test the level of significance in the multiple regression model. The model was significant because all the three p-values calculated were lower than 0.05.
In summation, the results of the analysis revealed that indeed there is a relationship between the income of the individuals and the number of car crash incidents in the nation. Results of the simple regression analysis provided evidence that there is a strong positive relationship between the GDP of the nation and the number of car crash incidents recorded in the country. The number of car crash incidents depends on the number of registered cars. The multiple regression results however showed that the relationship between GDP and number of registered cars, number of car crash and the years.