Regression Analysis is a statistical tool used to calculate and comment on the relationship between two or more variables. Regression analysis allows the company to understand mutual connections between factors that affect their general performance. In regression, variables are separated into dependent and independent, which can be used by business analysts in multiple ways. Independent variables are not affected by other variables in the data set, while the dependent ones are. In business context, this distinction allows the firms to distribute their resources in the most pragmatic way.
Organizations require better judgments and an understanding of the consequences of those actions in order to function smoothly and efficiently. Organizations gather and analyze data on sales, investments, expenditures, and other factors in order to enhance their performance. Regression analysis aids organizations in making sense of data, which is subsequently utilized to obtain insights into their operations. Regression analysis is used by business analysts and data professionals to make strategic business choices.
As specified in an example post, regression analysis might benefit a variety of commercial organizations regardless of industries they participate it. Small-scale businesses, such as boutiques, may utilize its potential for systematization to ensure a smooth transition between various types of operations. Additionally, since the regression analysis is conducted through an appropriate statistical tool, the observations based on it are by their nature credible and may be presented to stakeholders.
The goal of regression analysis is to transform gathered data into useful information. Organizations are using data-driven decision making, which removes old-school approaches such as guessing or assuming a hypothesis and, as a result, enhances work performance (Xue & Zhang, 2019). This analysis aids the organization’s management unit in its day-to-day operations. With so much data available, it’s possible to analyze and comprehend it in order to get useful insights and perform more efficiently. An example of this relationship would be the regular dynamic observed in an ice cream truck throughout the summer. The more people buy ice cream the more they buy iced drinks: an observation that through regression is revealed to be correlated to the summer weather.
Reference
Xue, W., & Zhang, L. (2019). Revisiting the asymmetric effects of bank credit on the business cycle: A panel quantile regression approach. The Journal of Economic Asymmetries, 20. Web.