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emis_user_manual_data_warehouse [2022/02/17 15:27] – [Flows] ghacheyemis_user_manual_data_warehouse [2023/12/13 20:37] – [Teachers] ghachey
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 <note tip>The table warehouse.EdLevelERDistrict is essentially the same but with the added ability to disaggregate by districts.</note> <note tip>The table warehouse.EdLevelERDistrict is essentially the same but with the added ability to disaggregate by districts.</note>
 +
 +Details about the columns names in this table are included below:
 +
 +  * **SurveyYEar**: school year of survey to collect data
 +  * **ClassLevel**: the class level or grade
 +  * **YearOfEd**: the official year of education of the class level
 +  * **OfficialAge**: the official age of the class level
 +  * **enrolM**: males enrolled
 +  * **enrolF**: females enrolled
 +  * **enrol**: total enrolled
 +  * **repM**: male repeaters
 +  * **repF**: female repeaters
 +  * **rep**: total repeaters
 +  * **psaM**: male pre-school attenders
 +  * **psaF**: female pre-school attenders
 +  * **psa**: total pre-school attenders
 +  * **intakeM**: males intakes (new enrollments without repeaters)
 +  * **intakeF**: females intakes (new enrollments without repeaters)
 +  * **intake**: total intakes (new enrollments without repeaters)
 +  * **nEnrolM**: males enrolled of official age
 +  * **nEnrolF**: females enrolled of official age
 +  * **nEnrol**: total enrolled of official age
 +  * **nIntakeM**: males intakes of official age
 +  * **nIntakeF**: females intakes of official age
 +  * **nIntake**: total intakes of official age
 +  * **nRepM**: male repeaters of official age
 +  * **nRepF**: female repeaters of official age
 +  * **nRep**: total repeaters of official age
 +  * **popM**: male population as per most recent population projection in use
 +  * **popF**: female population as per most recent population projection in use
 +  * **pop**: total population as per most recent population projection in use
  
 The workbook below shows some sample analysis. This workbook does a bit more than simple pivot tables and charts on the data but also computes new data using formulas. The workbook below shows some sample analysis. This workbook does a bit more than simple pivot tables and charts on the data but also computes new data using formulas.
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 === Tables/Views warehouse.TeacherJob* === === Tables/Views warehouse.TeacherJob* ===
  
-Tables containing data about the number of staff. They come in various disaggregations:+Tables containing data about the number of staff and their job title, staff type (i.e. teaching and non-teaching) and teacher type (i.e. regular, special ed.). They come in various disaggregations:
  
   * warehouse.TeacherJobTable   * warehouse.TeacherJobTable
emis_user_manual_data_warehouse.txt · Last modified: 2023/12/13 20:52 by ghachey