Assumptions in Linear Regression you might not know.

At first, Linear Regression is a method of modelling the best linear relationship between the independent variables and dependent variables. Linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The predictor variables are seen as fixed values and can be any complex function like polynomial, trigonometric, etc. But the coefficients will be strictly linear with the predictor variable. This assumption is used for implementing the Polynomial regression, which uses linear regression to fit the response variable as an arbitrary polynomial function of a predictor variable which also makes the linear relationship with the coefficients.