Review Engagement Analytical Inquiry Standard Procedures Lookup
Compare review-engagement analytical expectations with results and flag only differences greater than their supplied tolerances for follow-up inquiry.
Description
Review-engagement analytics compare observed results with explicit expectations and tolerances.
Review-engagement analytics compare observed results with explicit expectations and tolerances. An exception prompts inquiry rather than an automatic adjustment.1
The returned values include Absolute differences, Analytical exceptions, Exception indices, Follow-up required. The How section below explains the calculation, input rules and interpretation.
When to use Review Engagement Analytical Inquiry Standard Procedures Lookup
Use Review Engagement Analytical Inquiry Standard Procedures Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. No expectation development, explanation of exceptions or review assurance is produced.
Prepare source-backed observations and the applicable engagement policy before entering facts. Use consistent monetary units and periods. Supply all boolean evidence flags explicitly, and treat a successful screen as a reason to continue professional review.
How to use Review Engagement Analytical Inquiry Standard Procedures Lookup and understand its results
- Prepare source-backed observations and the applicable engagement policy before entering facts. Use consistent monetary units and periods. Supply all boolean evidence flags explicitly, and treat a successful screen as a reason to continue professional review.
- Enter expectations, results, tolerances using the field table and calculation rules, then run the tool.
- Read absoluteDifferences, exceptionCount, exceptionIndices, followUpRequired. 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 |
|---|---|---|---|
| expectations | Expected analytical values. | list of number | Required |
| results | Observed analytical values in the same order. | list of number | Required |
| tolerances | Nonnegative absolute tolerance for each expectation. | list of number | Required |
Require aligned nonempty finite signed expectation/result lists and finite nonnegative tolerances. Return finite absolute differences, zero-based exception indexes/count and follow-up flag. Equality is accepted.
difference_i=abs(result_i−expected_i); exception=difference_i>tolerance_i Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| absoluteDifferences | Absolute differences | list of number |
| exceptionCount | Analytical exceptions | integer; minimum: 0 |
| exceptionIndices | Exception indices | list of number |
| followUpRequired | Follow-up required | boolean |
{
"input": {
"expectations": [
1000,
500,
200
],
"results": [
1020,
495,
240
],
"tolerances": [
25,
10,
25
]
},
"output": {
"absoluteDifferences": [
20,
5,
40
],
"exceptionCount": 1,
"exceptionIndices": [
2
],
"followUpRequired": true
}
}Limitations and assumptions
No expectation development, explanation of exceptions or review assurance is produced.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Core numeric and string lists are limited to 100,000 entries; text entries are subject to the general large-text limit. Follow any smaller calculation limits above. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
Alternative or Complementary methods
Use Preliminary Analytics Percent Change Flagger: Preliminary analytics quantify change against the prior value. A negative prior baseline changes the sign convention. For a complementary task, use Disaggregated Analytics Margin Location Fluctuation Scanner: Disaggregated analytics can reveal movements obscured by totals. A signed baseline controls the direction of percent change.
References
Similar or alternative tools
- Preliminary Analytics Percent Change Flagger
Preliminary analytics quantify change against the prior value. A negative prior baseline changes the sign convention.
- Disaggregated Analytics Margin Location Fluctuation Scanner
Disaggregated analytics can reveal movements obscured by totals. A signed baseline controls the direction of percent change.