Regression analysis is a method that is used in statistics to find out relationships among variables. It provides a way of quantifying the effect of changes in one or more independent variables (Fox, 2017). Regression analysis is used widely in the field of econometrics which deals with the use of statistics as well as mathematical techniques to the analysis of economic data. Other uses are also found in sociology, biology, finance, pharmacology, psychology, and engineering, among others. This article will, however, focus on the application of regression analysis in economics.
It is vital to understand some of the types of regression analysis. The two main types of regressions are linear and logistic regressions. Linear regression is one of the methods that is widely used. In this type, the dependent variable is usually continuous while the independent variable may be either continuous or discrete and the regression line’s nature is linear. Linear regression strikes a relationship between the dependent variable (Y) and with either one or more independent variables (X) using a line referred to as a best fit straight line or a regression line. The equation that represents it is Y = a + b * X + e, whereby a is an intercept, b is the slope of the line whereas e is the error term (Pindyck & Rubinfeld, 2015). The equation can also be used to speculate the target variable value from given predictor variables. On the other hand, logistic regression is used to investigate the probability of events, that is, either event success or event failure. Logistic regressions are popularly used in cases where the dependent variable is binary (True/False, Yes/No, 0/1). Logistic regression handles multiple types of relationships because it uses a non-linear transformation to the speculated odds ratio. Additionally, it needs large sizes of samples because maximum likelihood estimates are not as powerful at low samples as the ordinary least square.
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The main application of regression analysis in economics is forecasting. By using the present trends, economists can have an insight of what is prone to occur in the future. The primary step of conducting a regression analysis involves the formulation of a hypothesis (Fox, 2017). For instance, it may be alleged that the practice of people saving their money in banks may reduce the flow of cash in the economy. The hypothesis may state as; saving money in banks does not have an impact on the flow of cash in the economy. The central bank may then instruct other banks to stop lending money to their customers and even sell bonds to the public. Observers can then find out whether the hypothesis is null or alternative. A null hypothesis indicates that there is no significant difference among the variables being observed whereas an alternative hypothesis indicates that there is a significant statistical relationship between two variables that are being observed.
Economists analyze and keep records of different aspects of the economy so that they can advise the relevant institutions on the best possible steps to take. Economists are mainly concerned with consumer trends and habits. Through regression analysis, economists can establish seasons where particular products are mostly consumed, the type of market that specific products appeal to, the spending patterns of different classes of people, among others (Pindyck & Rubinfeld, 2015). Using this information, they can advise various sectors involved in the production.
Conclusively, regression analysis plays a vital role in guiding business people on how to align their products strategically with consumer needs. Many fields of the economy such as insurance companies, financial institutions, government organizations, among others seek the services of economists to help them analyze data. Economists mainly use regression tools to analyze the data.
References
Fox, J. (2017). Applied regression analysis, linear models, and related methods . New York, NY: Sage Publications, Inc.
Pindyck, R. S., & Rubinfeld, D. L. (2015). Econometric models and economic forecasts (Vol. 4). Boston: Irwin/McGraw-Hill.
