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?

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?

I have been encountering many a fact and underlying assumptions that are associated with linear regression, and data transformation which are actually not true (at least as far as my knowhow is concerned). For example, independent variables must have a Gaussian distribution, outliers are the points either above or below the upper or lower whiskers correspondingly (employing Box plot terminology), and that the sole purpose of transformations are to bring a distribution close to normal distribution in order to suit the model. I want to know what are a common set of myths that are generally taken for facts/assumptions about the purpose of transformations, linear regression, and different kinds of distributions. Also, how did such myths come about?

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?

I have been encountering many a fact and underlying assumptions that are associated with linear regression, and data transformation which are actually not true (at least as far as my knowhow is concerned). For example, independent variables must have a gaussian distribution, outliers are the points either above or below the upper or lower whiskers correspondingly (employing Box plot terminology), and that the sole purpose of transformations are to bring a distribution close to normal distribution in order to suit the model. I want to know what are a common set of myths that are generally taken for facts/assumptions about the purpose of transformations, linear regression, and different kinds of distributions. Also, how did such myths come about?

I have been encountering many a fact and underlying assumptions that are associated with linear regression, and data transformation which are actually not true (at least as far as my knowhow is concerned). For example, independent variables must have a Gaussian distribution, outliers are the points either above or below the upper or lower whiskers correspondingly (employing Box plot terminology), and that the sole purpose of transformations are to bring a distribution close to normal distribution in order to suit the model. I want to know what are a common set of myths that are generally taken for facts/assumptions about the purpose of transformations, linear regression, and different kinds of distributions. Also, how did such myths come about?