An introductory section shows the behavior of quantile regressions in datasets with different characteristics, adding more details to the discussion developed in Section 2.2. Section 3.2, using simulated data, shows the empirical distribution of the quantile regression estimator in case of independent and identically distributed (i.i.d.) errors, in case of non-identically distributed (i.ni.d.) and in case of dependent errors (ni.i.d.). The chapter then analyzes only the case of i.i.d. errors, while the other two cases are deferred to Chapter 5. Section 3.3 considers a small size real dataset and a very simple linear regression model where wages depend on education, to compare OLS and quantile regression estimates when the errors are i.i.d.. Then the simple linear regression model is extended to comprise more than one explanatory variable, and elements like age, gender and type of work, dependent or independent, full time or part time, are included. The tests considered in Section 3.4 allow to verify hypotheses on more than one coefficient at the time, in order to evaluate the validity of the selected explanatory variables. In the final specification, considering a very small dataset, wages turn out to depend upon age and degree of education.