Linear Model: linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression.
A simple linear regression equation is y=mx+b, whereas m is the slope/gradient of the polynomial of the line aka y( predict coefficient) and b is the intercept of the line (bias coefficient).
Root Mean Square Error is the Standard Deviation of residuals, which are a measure of how far data points are from the regression. Or in simple terms how concentrated the data points are around the best fit line.
R-Squared score varies between 0 to 100%.
