Least Square Estimation of Parameters for Linear Regression

  • T. Daniya, M. Geetha, B. Santhosh Kumar, R. Cristin

Abstract

Regression analysis acts as a statistical tool for examining the relationship between dependent and one or more independent variables which can be widely used for prediction and forecasting. Regression is a supervised learning technique in which the output variable is a real or continuous value. For an unknown value ‘X’, the machine will predict the output ‘Y’ by its experience. Some applications are economics, management, life and biological science engineering, Social Science etc. In this paper, the parameters used in linear regression techniques (both simple linear  and multiple linear models) are derived and estimated using Least Square Parameter Estimation model.

Published
2020-04-17
How to Cite
T. Daniya, M. Geetha, B. Santhosh Kumar, R. Cristin. (2020). Least Square Estimation of Parameters for Linear Regression. International Journal of Control and Automation, 13(02), 447 - 452. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/9898
Section
Articles