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.

From my understanding, there is not much difference in terms of the Greek letters or Latin letters to represent the 'true' value of parameters in OLS. If you're referring to the estimates of these parameters, $\hat{\beta}$ is used more frequently than $b$.
In general, Greek letters are more often used as it's convenient to treat $\alpha$ as $\beta_0$ so that the expression can be written as $Y = \beta^T X$ in multivariate regression. In the world of machine learning, the expression is often written as $Y = w^T X$.
