Quantitative Business Analysis

1 Quantitative Business Analysis In the article by Rodney Ramcharan, the author discusses the use of regression analysis in economics to distinguish between correlation and causality (Ramcharan, n.d). The article explains how economists use regression analysis to identify the relationship between variables and how it helps understand causation. The author presents an example of trying to determine the returns on education (dependent variable) based on the level of education (independent variable) and intelligence quotient (IQ) test results (another independent variable). The regression equations are as follows:

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2 Simple Regression Y = 5.40 + 1.06 EDU In this equation, Y represents hourly wages (dependent variable), and EDU represents the level of education (independent variable). The coefficient of 1.06 indicates that an additional year of education is associated with a $1.06 increase in hourly wages.

Multiple Regression Y = 5.40 + 0.83 EDU + 0.001 IQ In this equation, Y is hourly wages (dependent variable), EDU is the level of education (independent variable), and IQ is the intelligence quotient (another independent variable). The coefficient of 0.83 for EDU indicates that when IQ is also considered, the impact of education on wages becomes 27% smaller than the simple regression without considering IQ. Additional Summary The regression analysis helps economists understand the relationship between variables, such as the impact of education and IQ on hourly wages. Including multiple variables in the regression allows one to highlight that IQ influences the correlation between education and wages. The positive relationship between education and wages is attenuated when accounting for the influence of IQ. This demonstrates the importance of considering additional variables to estimate causal relationships better. In the example, the independent variables are education and intelligence quotient (IQ). The impact/relevance of the independent variable "education" is evident in its positive coefficient in both regression equations. The coefficient indicates that an additional year of education is associated with an increase in hourly wages. The article provides regression equations to estimate the effect of education and IQ on hourly wages. However, the

3 article does not explicitly calculate the linear correlation coefficient (r) to quantify the strength of the linear relationship between the variables.

4 Reference Ramcharan, R. (n.d.). Regressions: An economist obsession. International Monetary Fund. https://www.imf.org/external/pubs/ft/fandd/basics/regress.htm