Difference between Greek and Latin letters in OLS Currently I am learning about the OLS method. In the literature I see different notations for the to be explained variable $y_i$. The different notations have to do with the letters used. In one notation Greek letters are used:
$y_i=\alpha+\beta x_i+\varepsilon_i$
In another notation Latin letters are used.
$y_i=a+b x_i+e_i$
In the literature I found the following:

When we analyse empirical data we do not know ‘true’ values of $\alpha$ and
$\beta$, but we can compute estimates a and b from the observed data.

So from this statement I understand that if we have a data set, we can only obtain $a$, $b$ which is an estimation for the true parameter. However, it is still not clear to me which notations are used in which context.
It is extremely helpful if somebody could point out the differences between the notations and in which context each notation is applicable.

Using Greek letters for the true parameters and Latin letters for the empirical ones (estimated, computed from the sample) is a rather common convention. E.g., if sampling from a normal distribution, one would often use $\mu,\sigma$ for the true values of mean and standard deviation, and $m,s$ for the ones calculated from sample $\{x_1,...,x_n\}$. Thus, $\mu,\sigma$ are numbers, whereas $m,s$ are themselves random variables.
It is however advisable to always check beforehand what notation is used in a course or a book, as there is no general rule. E.g., using letters with hats for estimated parameters is also rather common (although one might also wish to distinguish between an empirical mean, $m$, and an estimate of the true mean, $\hat{\mu}$.)
