This talk includes two testing problems of regression functions with responses missing at random. One problem is minimum distance model checking. The proposed lack-of-fit tests are based on a class of minimum integrated square distances between a kernel type estimator of a regression function and the parametric regression function being fitted. These tests are shown to be consistent against a large class of fixed alternatives. The corresponding test statistics are shown to have asymptotic normal distributions under the null hypothesis.