Simple Linear Regression

Abstract

Mullple linear regression (MLR) is a method used to model the linear relaonship between a dependent variable (target) and one or more independent variables (predictors). MLR is based on ordinary least squares (OLS), the model is fit such that the sum-of-squares of differences of observed and predicted values is minimized. The MLR model is based on several assumpons (e.g., errors are normally distributed with zero mean and constant variance). Provided the assumpons are sasfied, the regression esmators are opmal in the sense that they are unbiased, efficient, and consistent. Unbiased means that the expected value of the esmator is equal to the true value of the parameter. Efficient means that the esmator has a smaller variance than any other esmator. Consistent means that the bias and variance of the esmator approach zero as the sample size approaches infinity. How good is the model? R 2 also called as coefficient of determinaaon summarizes the explanatory power of the regression model and is computed from the sums-of-squares terms.