Introduction
Growth is a big issue in the development of a nation. It is a measure of how a country can possibly sustain itself and make advances in terms of the development project. Growth of any given county can be attributed to some factors such as the resources that are available internally and the labor force that both in physical and technological that is needed to convert these resources to meaningful products.
Theory
The growth of a nation is caused by several factors, among them include: education, changes in the labor force, inflow of the foreign investments and the structures of the price. The growth is measured by the Gross Domestic Product (GDP). This is the total monetary value of all the goods produced within a county over a given period of time, take for example, if the GDP is 4%, It means that the economy of a country has grown by 4% compared to the previous year. The growth of a nation can be the potentiality of a country to meet the needs of the people. It also has some positive effects on the employment level and the national income; this may in turn improve the living standard of the people in the country.
Therefore for the nation to grow it must improve on the following sectors that affect growth: the human resources such as skills, education and training of the citizens. the natural resources such as water, landscape and wildlife, the capital formation such as land, machinery, communication and transportation, the technological development and finally the political and social factors such as traditions, customs, beliefs and values which might affect the economy growth.
Data section
Summary
The above summary table gives the summary in terms of minimum, first quartile, median, mean, third quartile and the, maximum value. The summary is of the four variables namely lgp2, mse2, fse2 and fhe2.
Correlation
The table above shows the correlation between the variables lgp2, mse2, fse2 and fhe2. The correlations are all positive; this means that all the variables are directly related. This is to say that an increase in one variable leads to an increase in the other and vice versa. All the correlations are above 0.5; this is to say that the linear relationship between the variables is very strong. The highest correlation is between the variables mse2 and fse2. This is the strongest linear relationship among the four variables, while the lowest is between lgdp2 and fhe2.
Bi-variate plots
The above are the bi-variate plots among the four variables. The plots show that there is positive linear relationship among all the variables involved.
Model section
The dependent variable is the lgdp2 while the independent variables are mse2, fse2 and fhe2.
Therefore the model is formulated as follows
lgdp2=β0+β1mse2+β2fse2+β3fhe2
Hypothesis
Based on the regression model, the hypothesis is formulated as follows
null hypothesis: βi=0, this is to mean that the model is not significant
alternative hypothesis: βi≠0, this is to mean that the model is significant
The control variables are mse2, fse2 and fhe2. They are the explanatory variables that explain the response variable lgdp2.
Result
The summary of the model is therefore obtained by the R-formula
Model<-lm (lgdp2~mse2+fse2+fhe2)
Model
Summary (model)
Coefficients:
Residual standard error: 0.6796 on 157 degrees of freedom
Multiple R-squared: 0.5023, Adjusted R-squared: 0.4928
F-statistic: 52.82 on 3 and 157 DF, p-value: < 2.2e-16
lgdp2=7.1329+0.3049mse2+0.4377fse2+0.6597fhe2
However, looking at the individual factors of the regression model, only the intercept and the fse2 variables are significant. Therefore they are the most important variables in the regression model; the others can be excluded from the model.
Conclusion
It is conclusive to say that there exists a significant relationship between the gross domestic product of a nation and the following factors: mse2, fse2 and fhe2. Therefore, for a nation to improve on its gross domestic product, it must improve on each of these single factors. However, much improvement should be made on fse2 since it is the most significant explanatory variable of the model.
R-CODES
#calling the data
data<-read.csv("dataa.csv",header=T)
data
summary(data)
lgdp2<-data$lgdp2
mse2<-data$mse2
fse2<-data$fse2
fhe2<-data$fhe2
#correlation plot
cor(lgdp2,mse2)
cor(lgdp2,fse2)
cor(lgdp2,fhe2)
cor(mse2,fse2)
cor(mse2,fhe2)
cor(fse2,fhe2)
#bivariate plot
plot(lgdp2,mse2)
plot(lgdp2,fse2)
plot(lgdp2,fhe2)
plot(mse2,fse2)
plot(mse2,fhe2)
plot(fse2,fhe2)
#regression
model<-lm(lgdp2~mse2+fse2+fhe2)
model
summary(model)
References
Chatterjee, S., & Hadi, A. S. (2012). Regression Analysis by Example. Hoboken: Wiley.
