Öz
Binary regression has many medical applications. In applying the tech-
nique, the tradition is to assume the risk factor $X$ as a non-stochastic
variable. In most situations, however, $X$ is stochastic. In this study,
we discuss the case when $X$ is stochastic in nature, which is more re-
alistic from a practical point of view than $X$ being non-stochastic. We
show that our solutions are much more precise than those obtained by
treating $X$ as non-stochastic when, in fact, it is stochastic.