Gross Domestic Product (GDP) refers to the value of all goods and services (finished) in monetary terms that are produced within the borders of a particular country during a given period. This research paper seeks to determine the effects that imports and exports have on the value of a country's finished goods and services. Exports are known to increase a country's Gross Domestic Product since the exported products bring money directly into the exporting country. Imports, on the other hand, leads to money being spent on the imported products which consequently results in money leaving the importing nation's economy leading to a reduction in her Gross Domestic Product. Though that may be the case, this paper seeks to uncover the actual effects that exports and imports pose to African countries' Gross Domestic Product as a whole. The fifty-four African countries, therefore, form the required population under study from which a given sample of countries would be obtained. The Gross Domestic Product is then determined for each of the countries that are members of the population being studied.
Method of Selecting the Sample
With the desired population having being determined, stratified random sampling is then conducted. The nations forming the population are divided into strata/ groups possessing similar characteristics after which samples are selected from each stratum proportional to the size of the strata. The random samples selected from every group forms the required sample to be used for hypothesis testing.
Procedure of Conducting the Research
With the required sample having being obtained, associate with each nation the Gross Domestic Product with its respective amount of exports for a specific period, preferably annually to. With the data for the GDP and amount of export from each country in the sample, an ANOVA analysis is then performed on the simple linear regression model formed using software such as Excel or Stata. Manually, simple linear regression can be carried out by first computing the sample mean for the Gross Domestic Product of the nations forming the sample, and the sample means for the amount of products exported from the sample nations. The sum of squares due to error (SSE), regression sum of squares (SSR) and the total sum of squares (SST) are then computed after which an ANOVA analysis is done.
The ANOVA analysis involves determining the degrees of freedom for the error sum of squares and regression sum of squares after which the mean error sum of squares and mean regression sum of squares are obtained by dividing the error sum of squares and the regression sum of squares by their respective degrees of freedom. The F statistic is then obtained by dividing the mean regression sum of squares with the mean error sum of squares using the appropriate level of significance which is always taken to be 95% unless specified. The critical value for the F statistic is then determined from statistical tables or software after which a cross comparison is performed to determine, first if the model is significant and secondly, the effect that the explanatory variable (amount of export) has on the response variable (Gross Domestic Product). A computed F value that is greater than the critical F value implies that the simple linear regression model developed is statistically significant while a computed F value less than the critical F value implies that the model formed is not statistically significant.
If a simple linear model formed from the relationship between the amounts of products exported from the African countries and the Gross Domestic Product of the nations is statistically significant, it means that the number of goods exported influences the amount of the Gross Domestic Product. The effect could either be negative or positive or be termed as strong or weak depending on how much the exports impact the GDP. On the other hand, a model that is not statistically significant implies that there is no relationship between the number of exports and the Gross Domestic Product value and hence exports do not affect the GDP at all.
The expected result of this research paper would be the presence of a strong relationship between exports and the Gross Domestic Product value of the sample nations under study. A positive relationship in which an increase in the number of goods exported by a unit causes the GDP to increase by a given margin is found to exist. Most of the African nations that export products to other countries through trade agreements have experienced a significant increase in their GDP values as the amount of money in circulation in their economy are greatly increased. Through exportation, industries in the exporting nations have experienced immense growth leading to more production which directly influences their GDP. Positive net exports, therefore, serve to increase a nation's Gross Domestic Product while negative exports reduce the GDP for a particular country.
Importance of Statistics in Daily Lives
Most of the information about the phenomena happening in the world daily has a mathematical basis that usually has some connection to statistics. Through statistical studies, medical research has been made easier and more efficient, political campaigns have been heightened, and quality checks in manufacturing industries have been effectively improved.
Medical research without statistical application has been found to be quite ineffective. This is because it is only through statistics that rate of effectiveness of certain drugs manufactured to cure some diseases can be determined. Lack of proper statistical studies in the medical research field poses a serious implication on the health of patients as correct prescriptions cannot be easily administered to them.
In politics, politicians and the general public depend on opinion polls to predict likely winners of elections. These polls are done statistically helping the politicians to know the intensity of campaigns to be done and in which regions while the public gets to know who their likely leaders will be. The media also uses the polls, which are based on statistics, to compile reports on voting patterns in a particular country or region. Without statistics, it would not have been possible for the polls to be conducted which would consequently lead to politicians and all the other stakeholders who depend entirely on the polls for political predictions to be misled.
The manufacturing industries largely depend on statistical procedures to conduct quality checks on the products that they manufacture. A sample of products is subjected to a number of quality check procedures to determine if they are of the required quality. If the sample products pass the checks, then a conclusion is drawn concerning the quality of the batch produced. These procedures for obtaining samples and drawing conclusions are purely statistical. Statistics is thus evidently, a very key area in our daily lives as it finds applications in almost every area of daily activities.
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