Location Audit Discrepancy Rate Percent Calculator
Preserve confirmed versus unresolved location audit discrepancy bounds with actual pooled record denominators; unlocated items are not automatically compliant or stolen.
Description
Confirmed discrepancies and unlocated records are disjoint audit outcomes.
Confirmed discrepancies and unlocated records are disjoint audit outcomes. Unknown locations provide possible-discrepancy bounds rather than confirmed failures.
Preserve confirmed versus unresolved location audit discrepancy bounds with actual pooled record denominators; unlocated items are not automatically compliant or stolen.
When to use Location Audit Discrepancy Rate Percent Calculator
Use this tool to preserve confirmed versus unresolved location audit discrepancy bounds with actual pooled record denominators; unlocated items are not automatically compliant or stolen.
Prepare the collection, exposure, scheduling or financial records required for this calculation. Keep row order and the declared units consistent.
How to use Location Audit Discrepancy Rate Percent Calculator and understand its results
- Prepare the collection, exposure, scheduling or financial records required for this calculation. Keep row order and the declared units consistent.
- Enter auditedRecordCounts, locationDiscrepancyCounts, unlocatedRecordCounts using the field table and calculation rules, then run the tool.
- Read confirmedDiscrepancyPercent, possibleDiscrepancyUpperPercent, unlocatedCoveragePercent. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| auditedRecordCounts | Audited record counts | list of number | Required |
| locationDiscrepancyCounts | Location discrepancy counts | list of number | Required |
| unlocatedRecordCounts | Unlocated record counts | list of number | Required |
Align nonnegative integer counts with confirmed plus unlocated no greater than audited and positive pooled audited total. Return pooled confirmed, possible upper and unlocated percentages.1
Paired lists must be nonempty and have equal lengths.
lower percent = 100 × confirmed/audited; upper = 100 × (confirmed + unlocated)/audited Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| confirmedDiscrepancyPercent | Confirmed discrepancy percent | number |
| possibleDiscrepancyUpperPercent | Possible discrepancy upper percent | number |
| unlocatedCoveragePercent | Unlocated coverage percent | number |
{
"input": {
"auditedRecordCounts": [
10,
30
],
"locationDiscrepancyCounts": [
1,
6
],
"unlocatedRecordCounts": [
2,
3
]
},
"output": {
"confirmedDiscrepancyPercent": 17.5,
"possibleDiscrepancyUpperPercent": 30,
"unlocatedCoveragePercent": 12.5
}
}Limitations and assumptions
Unlocated items are not automatically compliant or stolen.
Numbers and computed results must be finite; overflow produces an error. Numeric lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Audit Sampling Square Root N Sample Size Calculator: Expose caller square-root audit heuristic plus minimum/population cap; no statistical confidence, representativeness or error-detection assurance. For a complementary task, use Duplicate Accession Conflict Detector: Detect exact strict accession identity conflicts across source rows without silently normalizing malformed identifiers; no conflict resolution or ownership decision.
References
Similar or alternative tools
- Audit Sampling Square Root N Sample Size Calculator
Expose caller square-root audit heuristic plus minimum/population cap; no statistical confidence, representativeness or error-detection assurance.
- Duplicate Accession Conflict Detector
Detect exact strict accession identity conflicts across source rows without silently normalizing malformed identifiers; no conflict resolution or ownership decision.