TY - JOUR
AU - T. Daniya, M. Geetha, B. Santhosh Kumar, R. Cristin,
PY - 2020/04/17
Y2 - 2022/09/24
TI - Least Square Estimation of Parameters for Linear Regression
JF - International Journal of Control and Automation
JA - IJCA
VL - 13
IS - 02
SE - Articles
DO -
UR - http://sersc.org/journals/index.php/IJCA/article/view/9898
SP - 447 - 452
AB - 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.
ER -