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This article studies the estimation and statistical inference problems of semi-functional partially linear regression models when the covariates in the linear part are measured with additive error.To obtain the estimation of the parametric component,a corrected profile least-squares based estimation procedure is developed.Asymptotic properties of the proposed estimators are established under some mild assumptions.To test hypothesis on the parametric part,the authors propose a novel test statistic based on the difference between the corrected residual sums of squares under the null and alternative hypotheses,and show that its limiting distribution is a weighted sum of independent standard x21 Finally,the authors illustrate the finite sample performance of the methods with some simulation studies and a real data application.