Regression analysis can be considered to be a statistical tool. To Regress means to Deviate; hence, an analysis of regression in any data set simply means measuring how certain data points defer from the line and how the deviation can be minimised.

This tool helps one to examine the relationships between two or more variables. This tool chiefly helps individuals and companies to envisage change in a dependent variable based on change in the independent variables. This is also referred to as line fitting as a regression analysis equation can successfully be employed to fit a curve or line to specific data points. This fit is in a way such that variations in the data points are reduced to the minimum. Associations made in a regression analysis, on the other hand, are merely associative and any cause-effect is merely subjective.

To calculate the best fitting function, or the one that is least deviating, we can use a number of approaches. One of the approaches is Sum of Squares, which is a mathematical method. The linear least squares method and the non-linear least squares method use this approach. There are some basic assumptions of regression analysis, which include that the sample should be representative of the population for which analysis is being done, and that the error should be a random variable.

Significance of Regression

Regression models help in developing an objective plan. Further, regression analysis also assists one to forecast change and prepare the plans accordingly for a company. Both linear and non linear techniques of regression analysis are used effectively to make predictions and take correct business decisions.

There are so many ways in which regression analysis can be useful for business, and so many different ways in which it can be applied, that you an expert to guide you. Send a query to our team of consultants, or simply place an order for assistance with regression analysis. You will get a mentor who will teach you all the methods.