Over the years, people have used mathematics in many ways that could be unimaginable 100 years ago. Scientists, artists, and other professionals have proven that most concepts and ideas from different fields of study are highly dependent on mathematics in one way or the other. As a result, we have seen the relationship between mathematics and economics strengthen; researchers concerned with distribution, production, and consumption of resources utilize both basic mathematics and complex mathematics to explain numerous economic phenomenon. Over the years, economists have worked around the clock to identify and report the magnitude with which economic variables influence development and growth in a country. Fortunately, the application of regression and linear relations (mathematical concepts) have shone some light on most empirical studies by providing economists with the linear regression concept which represents the relationship between economic growth and other dependent factors, such as inflation, interest rate, and income.
My interest is in identifying and learning how mathematics has changed economics – the study of resource allocation. According to Renshaw (2016), most economic theories follow a statistical or mathematical model, thereby forming the basis of the relationship between mathematics and economics. In their study, Nawaz, Rafiq, Mehmood, Abdullah, and Hussain (2014) provide evidence that the use of statistical and mathematical models are important for economic research. The main goal of this study was to identify the factors that influence economic growth of a country with reference to Pakistan. Based on the literature review section of the report, Nawaz et al. (2014) provide an analysis of the factors that they believe can influence economic growth in Pakistan. Mathematically, the forces that contribute to a single outcome of economic growth are known as independent variables. By definition, an independent variable is controlled in a scientific experiment to identify its effect on the dependent variable. Therefore, the study uses five independent variables as the basis of the research: inflation rate, FDI (Foreign Direct Investment), exchange rate, interest rate, and literacy rate. These variables are used to test the dependent variable named economic growth.
According to Nawaz et al. (2014), finding the relationship between independent variables and the dependent variable would require a combination of statistical and mathematical models. Fortunately, the regression line concept in mathematics can help to create a viable relationship between dependent and independent variables. Furthermore, this mathematical concept employs the collinearity test that quantitates the extent of the effect of each independent variable on economic growth. A regression test was then used to identify coefficients of variation that represents the relationship between the selected variables. According to Renshaw (2016), the linear regression line is of the form:
Y = aX1 + bX2 + cX3 + … +nXi …. (Equation 1)
The equation has three major entries, namely the dependent variable (Y), independent variables (Xi), and the standard coefficients (n) for each independent variable. Now, with the help of the Statistical Package for the Social Sciences, Nawaz et al. (2014) performed the regression test that focused on calculating and assigning standardized coefficients to each independent variable. The results were as follows:
Y = -0.225 (INF) + 1.438 (EXC) + 0.072 (INT) + 0.064 (LIT) + 0.583 (FDI)
INF = inflation rate
EXC = exchange rate
INT = interest rate
LIT = literacy rate
FDI = foreign direct investment
Clearly, the use of regression analysis can help economists to determine the extent to which various variables affect economic growth or the GDP. From the regression line, EXC, INT, LIT, and FDI have positive effects on economic growth because of the positive coefficients. That is, an increase in these variables will result in an increase in the GDP. However, a negative coefficient on the INF variable is an indication of a negative relationship between the inflation rate and the GDP (Nawaz et al., 2014).