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:

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*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: You should always standardize (or somehow "normalize") variables for the purpose of fitting regression models.
Usually not: software will either do this automatically (under the hood, as it were) or uses algorithms that accommodate huge ranges of values among the variables without losing numerical precision.
When the order of magnitude of one explanatory variable is more than about eight times greater than of another variable, though, then watch out: even preliminary standardization can run into trouble.  ("Eight" orders of magnitude is the square root of double precision, which is about 15.6 orders of magnitude.) The commonest example is when a date is used along with other variables, because some dates are represented as the number of seconds elapsed since approximately 1970, which is on the order of $10^9$ seconds.
