Regression analysis can be regarded as one of the pillars of predictive analysis, renowned for its capacity to develop the link between variables and make a data-driven forecast. While compared to other forms of prediction models such as time series analysis, clustering, and neural networks, regressive analysis aims at the modeling of linear relationships as well as non-linear relationships that exist in order to make accurate predictions. Due to these characteristics, it is an indispensable instrument in many fields of study, particularly in engineering, where it is used from calculating mechanical stress to forecasting outcomes of chemical reactions.
Multiple reasons make regression analysis a central feature of the practice of predictive modeling. It provides:
Regression analysis provides a convenient tool to model relations between variables, making it an irreplaceable tool in various branches of engineering and other sciences. Their main functionality entails the use of information about past occurrences in order to make prognostications, often in both a linear and non-linear fashion. In engineering practice, regression analysis has a broad variety of applications including the following:
Beyond engineering, regression analysis supports diverse fields such as economics, healthcare, and social sciences. It aids in predicting market trends, analyzing patient outcomes, and understanding behavioral patterns. Its flexibility in handling different types of data and its ability to provide actionable insights make it a foundational tool in both research and practical applications across industries. Some of the popular regression algorithms are depicted in Figure 1.
Besides engineering, regression analysis falls under the application of qualitative and quantitative methods in economics, healthcare, among other social sciences. It helps in forecasting trends in the market, evaluating the results of the patient, and comprehending the patterns of conduct. Due to its versatility and capacity to generate insights from all forms of data, it is considered a core tool in all research and operational fields. Among the general regression algorithms in the classification shown in Figure 1 below, the most widely used include the following.
Figure 1
Popular Regression Analysis Methods
(Courtesy: https://towardsdatascience.com/7-of-the-most-commonly-used-regression-algorithms-and-how-to-choose-the-right-one-fc3c8890f9e3)
