Although, lexically the term ‘regression’ means ‘going back’, or ‘stepping down, the regression analysis is a statistical tool of measuring the average relationship between any two, or more closely related (positively, or negatively) variables in terms of the original units of their data.
It is advantageously used by the statistician in estimating the unknown values of a dependent variable say Y from the known values of an independent variable say X. This technique is extensively used as a formidable instrument in almost all the sciences viz., Natural science, Physical science, and Social science. Particularly, in the fields of business and economics that come under the social science, this technique is invariably used for studying the relationship between two, or more related variables viz., price and Demand, Demand and Supply, Production and Consumption, Expenditure on Advertisement and Volume of Sales, Cost, Volume and Profit etc.
The technique of regression analysis introduced as above has been defined variously by various authors. Some of the important and meaningful definitions are reproduced here, as under:
- In the words of Sir Francis Galton, the regression analysis is defined as “the law of regression that tells heavily against the full hereditary transmission of any gift.. the more bountifully the parent is gifted by nature, the more rate will be his good fortune if he begets a son who is richly endowed as himself, and still more so if he has a son who is endowed yet more largely.”
- According to Taro Yamane, “one of the most frequently use techniques in economics and business research to find to a relation between two or more variables that are related causally, is regression analysis.”
- In the words of Ya Lun Chou, “Regression analysis attempts to establish the nature of the relationship between variables that is to study the functional relationship between the variables and thereby providers mechanism for prediction or forecasting”.