Linear Regression. Mathematics & Economics Research Paper. (Research Paper Sample)

Economics Linear Regression
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Economics Linear Regression
Purpose
The study purposed to examine the relationship between education and earnings. Focus is on examining the impact that the education has on wages a person obtains once employed after many years of study. The researcher looks at the time spent under education in years in relation to the wages that one obtains from their employment as an avenue to assess the impact of education. In this analysis, education is presented in terms of years as an independent variable while the dependent variable consists of the employment considered as earnings per hour.
Background
In a study conducted by Bakan and Buyukbese (2013, p.18) in a British company, they reveal that there exists a relationship between the two without indication of the direction that the relationship took. However, the introduction of an element such as the length of time taken by one to obtain and education that qualifies them for this job was another attribute. Juster (1975, p.71) conducted a study in which education, experience and the income one earns were related. In such an environment, education is viewed as the yardstick with which income is determined as it reflects stock of human skills that the person has. Basing on this study, the relationship between education and income is one that needs investigation to reveal the level of impact one spends accumulating a stock of human skills has on their income in employment.
Method
The methodology suited for this study is a quantitative approach. Under quantitative research, the numerical abilities of the data are explored with development of statistical models to help explain the relationship between the variables (Lewis, McGrath & Seidel, 2011, p.17). The intentions of the researcher in examining the relationship between education and earnings based on a numerical ability of the data with the participants’ contributions converted into numerical classifications. The presentation of the results is done through tables that explain the relationship between the variables and a linear regression equation based on the basic equation:
Wages=a+βEducation
Where Wages represents the dependent variable, a for the intercept, β represents the slope of the line for the two variables while the Education is for the independent variable. In addition to the above, the hypothesis below is subjected to testing using the regression analysis that reveals a p value examined on the basis of a 0.05 alpha and 95% confidence interval.
Null Hypothesis: There is no statistically significant relationship between the independent variable education and earnings of the participants.
Alternative Hypothesis: There is a statistically significant relationship between education and earnings.