Comments (3)
if the estimate is 0, then MOE should be 0
so the variables used as inputs to this calculation should follow this rule
and then the MOE should match
from db-acs.
similar case in economic table
for variable Pv150t174, it seems like when calculating MOE, population ignored all M value that are equal to 16.
e.g. for geoid 1000700
m_pop: 57
m_mine: 75
data:
[{'B17024_034M': 16.0,
'B17024_112M': 16.0,
'B17024_021M': 16.0,
'B17024_060M': 57.0,
'B17024_099M': 16.0,
'B17024_008M': 16.0,
'B17024_073M': 16.0,
'B17024_086M': 16.0,
'B17024_125M': 16.0,
'B17024_047M': 16.0}]
sum([i**2 for i in info[0].values() if i != 16])**0.5
==> 57
sum([i**2 for i in info[0].values()])**0.5
==> 75
from db-acs.
Addressed
from db-acs.
Related Issues (20)
- Find total variables and make p 100 percent HOT 2
- Estimation that involves subtraction
- CvEm16pl2 --> percentage estimate set to 100 for all NTAs
- Social columns wildly off HOT 4
- calculation errors on population's end? HOT 1
- percentage estimate set to 100 HOT 2
- demo - c,e,m calculated correctly, but not p and z (wrong total?) HOT 2
- New total lookup function HOT 1
- 2018 update HOT 1
- 2018 update feedback HOT 1
- MOEs for zero-estimate data set to null HOT 1
- Change intermediate table naming -- low priority
- Check that all instances of collapsing variables is summing independent variables
- Y2006-2010 row count doesn't match Y2014-2018 HOT 5
- Don't inflate 2006-2010 ACS values because consuming app should do this
- 2014-2018 new issues HOT 9
- Catch controlled variables
- Adding CD level Aggregation for Community Profiles purpose
- python package for calculating pff
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from db-acs.