Linear Least Square Regression is a model to predict a sample’s scalar response based on its features. Its prediction function’s output is linear to samples:
\[y=\beta _{0}+\beta _{1}x_{1}+\beta _{2}x_{2}+...+\beta _{n}x_{n}\]
\[y= \theta ^{T}\cdot x + \beta _{0}\]