I plugged your queries into my database and I ended up with 4 NYC's, 4 Atlanta's, and 2 LA's.
I was able to reproduce the issue in the test DB but on a smaller scale by adding some data into the data tables:
Result:
LocationName RepType AvgOfNewA1 AvgOfNewC AvgOfNewH AvgOfNewD1 AvgOfNewE1 NewC NewW NewH Atlanta Type1 0 0 0 0 0 0 0 0 Atlanta Type1 323 0 0 116 27.5 0 0 0 Atlanta Type2 22 21 132 23 23 21 878 132 LA Type1 23 45 123 43 34 45 544 123 LA Type2 34 0 0 546 56 0 0 0 NYC Type1 5 200 6545 4 4 200 545 6545 NYC Type2 100 300 231 2 2 300 321 231
tblAgents:
ID AgentName RepType OfficeID SupervisorID 380 Mike Smith 1 1 1 381 John Doe 2 1 1 382 Tom Jones 1 2 1 383 Adam Reynolds 2 2 1 385 Paul Hodgkins 1 3 1 386 Barry Thomas 2 3 1 387 Bernie Patel 1 2 1
tblEntries
ID AgentID TimePeriod A1 B1 C1 D1 E1 28 380 Jan 2015 5 10 4 4 4 30 381 Jan 2015 100 200 2 2 2 31 382 Jan 2015 100 200 1 1 1 32 383 Jan 2015 22 23 32 23 23 33 385 Jan 2015 23 34 656 43 34 34 386 Jan 2015 34 4563 56 546 56 35 387 Jan 2015 546 156 51 231 54
tblEfficiency
ID AgentID TimePeriod C W H 4598 380 Jan 2015 200 545 6545 4599 381 Jan 2015 300 321 231 4601 383 Jan 2015 21 878 132 4603 385 Jan 2015 45 544 123