. In this thesis first the logistic regression model will be introduced, and then robust estimators are introduced to estimate the coefficients of this model. We are interested in testing that concern the parameter of a logistic regression model. A robust Wald-type test based on a weighted Bianco and Yohai estimator as implemented by Croux and Haesbroeck is proposed. The asymptotic distribution of the test statistic is study to get a further insight on the stability of the p-value. A Monte Carlo study is performed to investigate the stability of both, the level and power of the test. Finally, we illustrate the performance of the proposed test on a real data set In the binomial regression model we assume that the response variable Y has a Bernoulli distribution such that P( ) Where F is a trictly increasing cumulative distribution function, X is the vector of explanatory variables and is the unknown regression parameter. When F(t) = We have the logistic regression model, which is the model we will consider . However, our result can be extended to other link functions. It is well known that the maximum likelihood estimator (MLE) of can be severely affected by outlying observations . Croux et al, discuss the breakdown behavior of the MLE in the logistic regression model and show that the MLE breakdown to zero when severe outlier are added to a data set . In the last few decades , a lot of work has been done in order to obtain robust estimates of the parameter in this model and also in the more general framework of generalize linear models. Among others , we can mention the proposals given by Pregibon, Stefanski et al, Kunsch et al, Morgenthaler , Carroll and Pederson, Christmann and Bianco and Yohai and more recently Croux and Haesbroeck and Bondell. We are interested in testing parametric hypotheses about the regression parameter of the logistic regression model . Robust testing in this setting has received much less attention than robust estimation. Testing procedures based on ltr"