Today, I am going to be writing about linear regression and its use in business decision but first, I would discuss what linear regression is and areas business could employ it in decision making. To understand linear regression, I would paint a little scenario. A man owns a construction company and he want to find a relationship between revenue and quality of labor. The depended variable in this scenario is the revenue and the independent variable is the quality of labor. Simple linear regression could be use in establishing this relationship but in a situation where there are multiple independent variables, logistics regression would be more appropriate in establishing the relationship. Regression therefore can be used to establish relationship between an independent variable and depended variable and how each of these variables could impact the dependent variables.
We are going to consider few types of regression analysis, thus;
Predictive Analytics: this analysis make use of historical data to determine patterns, trends and also to use in predicting a possible future trend. This analysis could help business make not only predicting the future but also help in forecasting impact on immediate revenue. For example, you can forecast the number of customers who will purchase a service and use that data to estimate the amount of workforce needed to run that service. Insurance companies make use of regression analysis to estimate credit health of policy holders and a possible number of claims in a given time period. Predictive analytics could help business in reducing cost, number of tools needed, provide faster result, improve operation efficiency, detect fraud, risk management and optimization of marketing campaign.
Decision Making: Linear and logistic regression, provides a more accurate analysis which can then be used to test hypotheses of situations prior to sending it to production. Businesses generates a whole load of data from their operation and they are now making use of data analytics to make evidence-driven decisions rather than decisions based on intuition.
Operational Efficiency: Regression could also be use in optimizing business processes. Business leaders could use regression to understand the impact of organizational structure on employee’s performance. In this case, performance would be the depended variable while organizational structures are the independent variables.
Identification of Errors in Judgement: This analysis is very important for business leaders. The example below: executives managing a store may think that adding after hours shopping will increase profit. Regression analysis, however, analyzes all the variables revolving around this action and may conclude that to support the increase in operating expenses due to longer working hours (such as additional employee labor charges) will decrease profit significantly. Regression analysis provides quantitative support for decisions and prevents mistakes, product of intuitiveness.
New Knowledge: Because of the volume of data businesses generate, this could provide invaluable new insights to the company. Nevertheless, these data is invaluable without appropriate analysis. Regression analysis can find a relationship between several variables by uncovering patterns that were not taken into account
Data-driven decision eliminates the need to guess, and shields companies from making gut decisions thereby improving business performance by focusing on areas that have more impacts on the organizational performance.
