With the advent of revolutionary technologies like machine learning, artificial intelligence, predictive analysis, artificial neural network, etc., companies are striving to be more data-driven and analytical in their approach and decision making. One such mathematical operation that can help make data-driven decisions by analyzing the interaction between the given variables is regression analysis.
In mathematical language, regression analysis can be regarded as a statistical technique utilized to find the relation between two or more variables. In essence, this would help us to identify the independent variables which are not affected by other variables and also the dependent variables whose output depends upon the functionality of the independent variables. Hence by establishing a mathematical relationship in terms of co-variance, co-relation, standard deviation, etc., we can determine the nature and, therefore, the impact of independent variables in the dependent variables.
Regression analysis is thus used to build a mathematical model and make a prediction as to the impact on the whole environment when one of the variables is varied to given boundary conditions.
Regressions analysis can be classified under two heads based on independent variables. The two different types of regression analysis are as follows:
- Simple regression: When there is a single independent and dependent variable, then the regression equations so formed are said to be in the form of simple regression.
- Multiple regression: On the contrary, if there are numerous independent variables in the system i.e., many variables are having. On the other hand, when many independent variables influence the final outputs set of the dependent variables, the system is called multiple regression.
From predicting the outcome to determining efficiency to forecasting the nature of variables, the company can use regression analysis to establish a plethora of objectives. The different applications of regression analysis in business are discussed as below:
Predictive analytics is the most essential and prominent use of regression analysis. Regression can help establish a discreet and mathematically defined relationship between variables. Hence regression models are used to predict the change in final output if the independent variable is varied under various conditions.
The effect on demand for a product when the price changes the quantity demanded of a product. If the costs of substitutes are adjusted, the impact of discounts on the amount required, etc., are some examples where regression analysis is used to predict the outcome when one of the variables is changed.
Businesses needed to continuously upgrade and drive efficiency in workflow management, resource utilization, designing data have driven and objective standard operating procedures, etc. Since regression analysis helps determine the correct importance of each variable, businesses can use regression analysis to weed out inefficiencies in the right workflow process.
This would lead to better utilization of resources and streamlining the production mechanisms. Once the process is streamlined, it would ensure that monitoring becomes more tranquil and leads to increased productivity.
Any business works in an environment of uncertainty and risk. In such a situation, there are chances of error, be it strategic, operational, financial, etc. Successful companies learn from the mistakes and analyze the past data to prepare plans. Such analysis not helps to correct errors but also builds new capabilities in the long run. Regression analysis is based on processes that can help businesses in achieving this task. Regression analysis can give both causal and non-causal results, and it helps in examining the time-bound variables. This would help in delving deeper into past actions and hence develop a holistic and comprehensive strategy for the future.
Often, a project fails not due to lack of planning or execution but due to external factors like lack of appropriate technology, human elements, consumer behavior, which are beyond a firm control. Such failures can turn out to be a great success story under certain circumstances. Hence companies should ideally not abandon such ideas and instead wait for the right time. Businesses could use regression analysis to identify such purposes. Using regression analysis, companies could correct their past mistakes and use the learning to grow exponentially in the future.
Business today drives for automating the processes as much as possible. Automation not only reduces risks posed by human intervention but also reduces cost. In many cases, automation leads to time-saving, thus leading to an increase in production. Regression analysis can be used by businesses to drive automation in a phased and sequential manner since regression analysis helps establish a mathematical relationship and gives data-driven relationships among variables it can use to interlink and automate various processes.
With interdependency and interrelations, several components of the workflow can be automated in one go. Hence the task of automation can be made more objective and data-driven using regression analysis. This would also cut down on the mundane task and ensure that the company's wherewithal is used only for productive and innovative purposes.
Plans drive business. However, given the rapid pace of data creating, it is nearly impossible for a company to filter the data in a time-bound manner and then develop a hypothesis. This leads to businesses having a lack of insights and resorting to extrapolation to make decisions. For most companies, it is not the availability of data, which is the problem; somewhat, it cannot analyze the data, which creates issues.
Businesses can use regression analysis to surf through a vast magnitude of data and hence ensure that there are no loose ends when it comes to developing conjectures and hypotheses. This would ensure that key insights are not missed and that the data is monetized in the required manner.
In a nutshell, regression analysis is a potent mathematical tool that businesses can use for multiple purposes. Hence to gain competitive advantage and stay ahead of the curve, companies should use regression analysis to meet business objectives.
Sep 21, 2020