Chatterjee’s rank correlation coefficient (CCC) is a rank correlation coefficient that estimates a population quantity while remaining asymptotically normal under independence. However, it has been proven to be rate suboptimal compared to other recognized rank correlations. CCC has been demonstrated to adapt to the data’s complex structure, making it a potentially useful tool. This thesis applies the jackknife empirical likelihood (JEL) and its variation to estimate confidence intervals for CCC. It compares coverage probability and interval length for JEL and normal approximation (NA), bootstrap, and adjusted jackknife empirical likelihood (AJEL) methods. Simulation studies are carried out to assess the suggested estimators’ performance. Simulation results showed that the AJEL methods having the widest confidence intervals in most cases perform better than the NA methods in terms of coverage rate of the confidence interval. Also, bootstrap showed inconsistent coverage probability. The performances of the proposed methods are tested using real data analysis.