Income level is a significant determinant of the home environment of students, school that students attend, as well as the leisure activities and choice of entertainment. Imperatively, a student’s score in S.A.T tests is partly influenced by the IQ of the student, as well as the degree of exposure to learning opportunities. Students from families with lots of disposable income demonstrate higher scores than students from low income families. Essentially, students from high income families manage high score in tests; as their parents can afford tuition fees for their kids. Further, such kids are frequently exposed to educating and learning opportunities (Miles & Shevlin, 2000).
On the contrary, kids from low income parents live in environments that are not favorable for learning. Most low income students miss lessons and learning opportunities due to absenteeism. Therefore, the positive correlation between performance and level of income of the parents is absolutely true (Correlation and Regression Analysis, 2012). It is imperative to note that, test scores in students is influenced by multiple factors; thus correlation is not sufficient to describe the cause causes the result to performance.#2Correlation is a measure of the extent or degree to which a couple of variables is related. On the same note, correlation describes the linear association between two variables as x and y. On the contrary, regression describes the exact association (linear), which exist between a couple of variables as x and y (Miles & Shevlin, 2000). Both regression and correlation are similar in that the two are concerned with the examination of the association between a couple of variables, as well as whether an alteration in one variable results, in alteration in the second variable. Correlation is preferred in qualitative intellectual inquiries, especially in social sciences while regression is mostly utilized in quantitative studies; as correlation is suited in the handling descriptive data (Correlation and Regression Analysis, 2012). Criterion variables facilitate the description of the dependent variables in different statistical contexts. On the same note, predictor variables are vital in a regression equation; as they facilitate the anticipation and prediction of the value of other variables.
Correlation and Regression Analysis. (2012). S.l.: Sage Pubns Ltd.
Miles, J., & Shevlin, M. (2000). Applying regression & correlation: A guide for students and researchers. London: SAGE.