We　consider　the　estimation　and　hypothesis　testing　problems　for　the　partial　linear　regression　models　when　some　variables　are　distorted　with　errors　by　some　unknown　functions　of　commonly　observable　confounding　variable.　The　proposed　estimation　procedure　is　designed　to　accommodate　undistorted　as　well　as　distorted　variables.　To　test　a　hypothesis　on　the　parametric　components,　a　restricted　least　squares　estimator　is　proposed　under　the　null　hypothesis.　Asymptotic　properties　for　the　estimators　are　established.　A　test　statistic　based　on　the　difference　between　the　residual　sums　of　squares　under　the　null　and　alternative　hypotheses　is　proposed,　and　we　also　obtain　the　asymptotic　properties　of　the　test　statistic.　A　wild　bootstrap　procedure　is　proposed　to　calculate　critical　values.　Simulation　studies　are　conducted　to　demonstrate　the　performance　of　the　proposed　procedure,　and　a　real　example　is　analyzed　for　an　illustration.