What are the myths associated with linear regression, data transformations? I have been encountering many assumptions associated with linear regression (especially ordinary least squares regression) which are untrue or unnecessary. For example:

*

*independent variables must have a Gaussian distribution

*outliers are the points either above or below the upper or lower whiskers correspondingly (employing Boxplot terminology)

*and that the sole purpose of transformations is to bring a distribution close to normal in order to suit the model.

I would like to know what are the myths that are generally taken for facts/assumptions about linear regression, especially concerning associated nonlinear transformations and distributional assumptions.  How did such myths come about?

Myth
A linear regression model can only model linear relationships between the outcome $y$ and the explanatory variables.
Fact
Despite the name, linear regression models can easily accomodate nonlinear relationships using polynomials, fractional polynomials, splines and other methods. The term "linear" in linear regression pertains to the fact that the model is linear in the parameters $\beta_0, \beta_1, \ldots$. For an in-depth explanation about the term "linear" with regards to models, I highly recommend this post.
