| Course | AC312 Fundamentals of Accounting Analytics |
|---|---|
| Unit | Unit 5 |
| Paper type | Ledger relationship analysis |
| Length | About 560 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Accounting |
| Updated | October 2026 |
Free sample paper for AC312 Unit 5
Forty-Eight Elevator-Months and One Far Below the Line: A Unit 5 Ledger Relationship Analysis
[Student Name]
Purdue University Global
AC312: Fundamentals of Accounting Analytics
Unit 5 Ledger Relationship Analysis
[Instructor Name]
[Date]
Plainsmark Cooperative and every figure are composite; the study, guide and textbook cited are real.
The Relationship
Under Plainsmark's rate card, every point of moisture over 15.5 percent costs the member 4.2 cents on each bushel. So the driver of drying revenue is not bushels alone but point-bushels: bushels times the points of moisture removed. Both figures sit in the ledger, the moisture on each scale ticket and the charge on each settlement.
The population is every corn load received in the harvest months, September through December, for two marketing years at six elevators: 48 elevator-months.
Fitting the Line
A simple regression of drying charges billed on point-bushels, leaving out the one month under question, gives a slope of 4.19 cents per point-bushel, almost exactly the rate card, an intercept near zero, and a correlation of 0.98. Including North's October 2024 lowers the correlation to 0.93. The close fit means each month's expected charge can be modeled with confidence, which is what makes a gap worth investigating (Richardson et al., 2021).
October 2024 by Elevator
Each line gives point-bushels, expected charge at 4.2 cents, amount billed and the difference.
1. North: 8,306,000; $348,852; $211,400; 39.4 percent below.
2. Bison Creek: 6,120,000; $257,040; $256,300; 0.3 percent below.
3. Elm Ford: 5,480,000; $230,160; $231,900; 0.8 percent above.
4. Ridgeway: 4,910,000; $206,220; $205,100; 0.5 percent below.
5. Haskett: 3,770,000; $158,340; $159,400; 0.7 percent above.
6. Ladd: 2,950,000; $123,900; $123,100; 0.6 percent below.
Five elevators billed within one percent of the rate card in the same wet October, so the north elevator's $137,450 shortfall cannot be explained by weather, harvest timing or the rate itself, which leaves something about North's own billing.
The Residuals
Plotted as distance from the line, 46 of the 48 elevator-months fall within 3 percent. North's November 2024 is 11 percent below, smaller but in the same direction. North's 2023 harvest months are on the line, so the problem began in the 2024 season.
Possible Explanations and What Would Test Them
1. North's dryer broke down and wet corn was trucked elsewhere for drying. Test: transfer tickets between elevators. None appear in October.
2. North billed at an old rate. Test: recompute North's charges at the prior year's rate. They still fall short, and the 2023 rate was the same.
3. Moisture readings at North were recorded lower than the grain warranted, so less drying was billed. Test: the distribution of North's moisture readings. That test belongs to Unit 6.
An analytical procedure tells the analyst where to look and how much is at stake; it does not by itself explain the difference. Field research on auditors' analytical procedures shows that the investigation of an unexpected difference, not its detection, is where most judgment is needed (Hirst & Koonce, 1996).
What the Finding Means for the Accounts
If the shortfall is real, drying revenue for the year is understated by about $137,450 for October and roughly $24,000 more for November, and members at North were overpaid by the same amounts through their settlements. Any recovery would reduce cost of grain purchased or be billed back as a receivable (American Institute of Certified Public Accountants, 2017).
References
American Institute of Certified Public Accountants. (2017). Guide to audit data analytics. AICPA.
Hirst, D. E., & Koonce, L. (1996). Audit analytical procedures: A field investigation. Contemporary Accounting Research, 13(2), 457-486. https://doi.org/10.1111/j.1911-3846.1996.tb00511.x
Richardson, V. J., Teeter, R. A., & Terrell, K. L. (2021). Data analytics for accounting (2nd ed.). McGraw Hill.
The Purdue Global AC312 Unit 5 instructions
The fifth AC312 unit asks students to measure a relationship between two accounting figures and use it to set expectations. Choose two amounts with a clear business link, and explain the driver in a sentence. Define the population and the period. Fit a simple model, such as a regression, leaving out any period under question so it does not pull the line toward itself, and report the slope, intercept and correlation both ways. Compare each period with its expected value and list the departures. Use comparable units, such as other locations in the same month, as controls. Look at residuals for patterns over time. List plausible explanations for any outlier and the evidence that would test each. Translate the finding into accounts and amounts. Cite the course text and research on analytical procedures in APA 7.
Inside the AC312 Unit 5 example
The sample defines point-bushels as bushels times moisture points removed and explains why they drive drying revenue. The population is corn received in four harvest months over two years at six elevators. The fitted line leaves out one month and reports a slope of 4.19 cents, an intercept near zero and correlations of 0.98 and 0.93. A numbered list compares October 2024 across all six elevators. The highlighted sentence uses the five on-line elevators as a control to rule out weather and the rate. A residuals section notes North's November was also below and its 2023 months on the line. Three explanations are tested or deferred: dryer breakdown, old rates and low moisture readings. Hirst and Koonce's 1996 field study supports the point that explaining a difference takes more judgment than finding it. The closing section sizes the account effect.
Reading the AC312 Unit 5 grading rubric
In this AC312 unit, the business logic of the relationship is graded alongside the statistics, so the driver must make sense. The model must be fitted and reported clearly, with the reason for excluding any period. Correlation and slope should be interpreted, not just stated. Departures need to be measured against expected values with a stated threshold. Using comparable units as a control is a mark of strong analysis. Residual patterns over time add depth. Explanations must be tested against evidence or marked for later testing, since a finding without a cause is incomplete. Translating the gap into accounts and amounts connects the work to the statements. A clear table makes checking easy. Purdue Global instructors expect support from the course text and published research on analytical procedures, cited in APA 7.
Common AC312 Unit 5 mistakes, and how to avoid them
Relationship analyses often pair accounts with no business link, so the correlation means little. Students fit the line including the month in question, which shrinks the gap they are trying to measure. Correlation is described as causation. Slopes are reported without units. Departures are eyeballed rather than measured. No comparison group is used, so a weather or rate effect cannot be ruled out. Explanations are guessed instead of tested. The finding is never turned into an account and a dollar amount. If your section pairs sales with receivables, payroll with headcount or utilities with production, our writers can fit the model, test the outliers and size the account effect for your data.
Get an AC312 Unit 5 example written to your instructions
Name the two accounts your Unit 5 prompt pairs, or the ones your dataset allows, and send the monthly figures with the rubric. The table, fitted line, residual chart and findings return within 24-48h, with the calculation file attached so each figure can be checked. The first custom sample is free, whichever pair you choose. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
More AC312 papers and BS in Accounting samples
- AC312 Unit 1: What Data Can Answer Post
- AC312 Unit 2: Data Question Framing Exercise
- AC312 Unit 3: Data Cleaning Log
- AC312 Unit 4: Descriptive Analysis Exercise
- AC312 Unit 6: Exception Testing Problem
- AC312 Unit 7: Visualization Set
- AC312 Unit 8: Seminar Reflection
- AC312 Unit 9: Dashboard Build
- AC312 Unit 10: Analytics Summary Report
- AC302 Unit 5: Direct vs Indirect Method Comparison
- AC301 Unit 5: Bond Call and Repurchase Problem
- AC300 Unit 5: Present Value of a Note Problem
- AC256 Unit 5: Form 1040 Return Problem
AC312 Unit 5 questions, answered
Where can I find a free AC312 Unit 5 sample paper?
This page offers a complete AC312 Unit 5 ledger relationship analysis for a composite grain cooperative, with a fitted model, an elevator comparison, residuals, tested explanations and APA 7 references.
Why is North's October left out when fitting the AC312 Unit 5 line?
Including a month suspected of error would pull the expected line toward it and shrink the gap being measured. The sample reports the correlation both ways so a reader can judge the choice.
What are point-bushels in the AC312 Unit 5 sample?
Bushels of corn multiplied by the points of moisture above 15.5 percent. Drying charges are billed per point-bushel, so they, not bushels alone, should drive drying revenue.
How do the other elevators help in AC312 Unit 5?
Five elevators billed within one percent of the rate card in the same wet October. That rules out weather, timing and the rate itself as reasons for North's shortfall.
How large is the account effect in the AC312 Unit 5 sample?
About $137,450 of drying revenue for October and roughly $24,000 for November, with members at North overpaid by the same amounts through their settlements.