Linear Regression Model Minimizing Prediction Error Using SSE and OLSE a Comparison

Regression analysis is a form of predictive modeling technique which investigates the relationship between a dependent or target and independent variable or predictor. This technique is used for forecasting, time series modeling and finding the causal effect relationship between the variables. Regression analysis is an important tool for modeling and analyzing data. Regression analysis is used to fit a curve / line to the data points, in such a manner that the differences between the distances of data points from the curve or line is minimized. Regression analysis estimates the relationship between two or more variables. There are multiple benefits of using regression analysis. It indicates the significant relationships between dependent variable and independent variable. It indicates the strength of impact of multiple independent variables on a dependent variable. In this paper we proposed apply Ordinary Least Square approach in linear regression model to minimize error in prediction. our object is to find a line so that all object are best fit around the predicted line.