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The simple linear model $$Y_i = \alpha + \beta \, x_i + \epsilon_i \qquadi=1,2, \ldots,N \geq 2$$ is considered, where the $x_i$'s are given constantsand $\epsilon_1, \epsilon_2 , \ldots, \epsilon_N$ are iid with continuousdistribution function $F$. An estimator of $\beta$ is proposed, based on Gini'srank association coefficient $G(\underline y;b)$ and defined as $\tilde{\beta}= \frac 12 \, \left\{ \sup (b: G(\underline y;b) >0) + \right. $ $ \left. \inf(b: G(\underline y;b) <0) \right\}.$ The properties of $\tilde{\beta}$ and ofthe related confidence interval are studied. Some comparisons are given, interms of asymptotic relative efficiency, with other estimators of $\beta$including that obtained with the method of least squares.

Estimation of the regression slope by means of Gini's cograduation index