The　purpose　of　this　article　is　to　use　an　empirical　likelihood　method　to　study　the　construction　of　confidence　intervals　and　regions　for　the　parameters　of　interest　in　linear　regression　models　with　missing　response　data.　A　class　of　empirical　likelihood　ratios　for　the　parameters　of　interest　are　defined　such　that　any　of　our　class　of　ratios　is　asymptotically　chi-squared.　Our　approach　is　to　directly　calibrate　the　empirical　log-likelihood　ratio,　and　does　not　need　multiplication　by　an　adjustment　fact　or　for　the　original　ratio.　Also,　a　class　of　estimators　for　the　parameters　of　interest　is　constructed,　and　the　asymptotic　distributions　of　the　proposed　estimators　are　obtained.　Our　results　can　be　used　directly　to　construct　confidence　intervals　and　regions　for　the　parameters　of　interest.　A　simulation　study　indicates　that　the　proposed　methods　are　comparable　in　terms　of　coverage　probabilities　and　average　lengths/areas　of　confidence　intervals/regions.　An　example　of　a　real　data　set　is　used　for　illustrating　our　methods.　(c)　2008　Elsevier　Inc.　All　rights　reseved.