  In the context of the usual calibration model, we consider the case in which
the independent variable is unobservable, but a pre-fixed value on its
surrogate is available. Thus, considering controlled variables and assuming
that the measurement errors have equal variances we propose a new calibration
model. Likelihood based methodology is used to estimate the model parameters
and the Fisher information matrix is used to construct a confidence interval
for the unknown value of the regressor variable. A simulation study is carried
out to asses the effect of the measurement error on the estimation of the
parameter of interest. This new approach is illustrated with an example.
