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?

@Dave's answers are excellent.  Here are some more myths.

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*The original scale/transformation for Y is one you should use in the model.

*The central limit theorem means you don't have to worry about any of this if N is moderately large.

*Trying different transformations for Y does not distort standard errors, p-values, or confidence interval widths.

