In　this　article,　a　naive　empirical　likelihood　ratio　is　constructed　for　a　non-parametric　regression　model　with　clustered　data,　by　combining　the　empirical　likelihood　method　and　local　polynomial　fitting.　The　maximum　empirical　likelihood　estimates　for　the　regression　functions　and　their　derivatives　are　obtained.　The　asymptotic　distributions　for　the　proposed　ratio　and　estimators　are　established.　A　bias-corrected　empirical　likelihood　approach　to　inference　for　the　parameters　of　interest　is　developed,　and　the　residual-adjusted　empirical　log-likelihood　ratio　is　shown　to　be　asymptotically　chi-squared.　These　results　can　be　used　to　construct　a　class　of　approximate　pointwise　confidence　intervals　and　simultaneous　bands　for　the　regression　functions　and　their　derivatives.　Owing　to　our　bias　correction　for　the　empirical　likelihood　ratio,　the　accuracy　of　the　obtained　confidence　region　is　not　only　improved,　but　also　a　data-driven　algorithm　can　be　used　for　selecting　an　optimal　bandwidth　to　estimate　the　regression　functions　and　their　derivatives.　A　simulation　study　is　conducted　to　compare　the　empirical　likelihood　method　with　the　normal　approximation-based　method　in　terms　of　coverage　accuracies　and　average　widths　of　the　confidence　intervals/bands.　An　application　of　this　method　is　illustrated　using　a　real　data　set.