Homocedasticity of the error : Error has the same variance, is additive, and does not depend of any specific value

Independence of errors

Non-linearity of X and Y in the considered interval

Error proportional to the value of the dependent variable

Conditional dependence of errors

The values are random variables

The values are not random variables. They are constants

Linearity of X and Y in the considered interval

The indicator adjusted uses to be far from value

A low value of  indicates a poor relation between X and Y or a non-linear relation

A high value of may indicate a good linear relation between X and Y

The indicator takes values between 0 and 1

In practise it is used the indicator adjusted , which is the value  ajusted to the degrees of freedom. It is slightly lower than

The indicator  takes values beween -1 and 1

A low value of may indicate a good linear relation between X and Y

A high value of  indicates a poor relation between X and Y or a non-linear relation

Is a process to be avoided because it can only generate errors and difficults the understanding of problems

May help to the fulfilment of some premisses that may not be fulfilled without the application of logarithms. Some examples are linearity between X and Y, and normality and/or homocedasticity of errorsors