| Course | AC570 Data Analytics for Accountants |
|---|---|
| Unit | Unit 6 |
| Paper type | Regression exercise |
| Length | About 580 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MS in Accounting |
| Updated | October 2026 |
Free sample paper for AC570 Unit 6
What Move-Ins and Delinquencies Should Explain: A Unit 6 Regression Exercise on Monthly Rent Credits
[Student Name]
Purdue University Global
AC570: Data Analytics for Accountants
Unit 6 Regression Exercise
[Instructor Name]
[Date]
Stowfield Storage and every figure are composite; the article, textbook and guide cited are real.
The Model
Monthly credits at each facility are modeled as a constant plus a coefficient on move-ins plus a coefficient on delinquent accounts. Promotions are given at move-in, so credits should rise with move-ins. Late-fee waivers are given to delinquent tenants who catch up, so credits should rise with delinquencies. A regression of this kind sets an expectation for each facility-month; the difference between actual and expected, the residual, is what the analysis looks at (Richardson et al., 2023).
The data are 31 facilities times 12 months, 372 facility-months, less Facility 27's March, left out because a week of activity is missing. That leaves 371 observations.
First Fit
Credits equal $1,128.49 plus $98.79 per move-in plus $18.29 per delinquent account. Both coefficients are positive and significant. The move-in coefficient is close to the average promotion credit, which makes sense. R-squared is 0.280: the two drivers explain a little over a quarter of the month-to-month variation. That is modest, and it means the expectation is a rough guide, not a precise forecast.
Diagnostics
Residuals against fitted values. The spread widens as fitted credits rise: larger facilities vary more. Comparisons will therefore use residuals scaled by each facility's own variability, not raw dollars.
Distribution of residuals. Skewed to the right: a few months have credits far above expectation, and none far below. That is the pattern a credit problem would produce, but it also violates the normality the significance tests assume, so p-values are read loosely.
Influence. Cook's distance measures how much each observation moves the fitted line (Cook, 1977). Two stand out. Facility 22 in June has a distance of 0.123. Facility 5 in August has 0.106, with credits roughly nine standard errors above its fitted value.
The documented event. Facility 5 flooded in August after a storm, and the company credited affected tenants a month's rent by policy. The credits are legitimate and unrelated to move-ins or delinquencies, so this one observation is removed from the refit and noted.
Refit Without the Flood Month
Credits equal $1,104.20 plus $97.61 per move-in plus $18.92 per delinquent account, with R-squared 0.311. The coefficients barely move, which shows the model is not driven by the flood, and the fit improves slightly.
Facility 22 in June remains influential. It is not explained by any documented event. Its June goodwill credits doubled, as the profile found, and across the year its credits exceed the model's expectation by $36,730.50.
Removing the flood month was justified by a documented cause, while Facility 22's June was kept in the model precisely because no cause has been found, since dropping unexplained points to improve a fit would hide the very exceptions the analysis exists to find.
Scoring Facilities
Each facility is scored by its summed residuals for the year and by how many of its months have positive residuals. Facility 22 leads on dollars. Facility 9 has positive residuals in 10 of 12 months, totaling $8,810.65, a smaller amount but a steady pattern. Both go forward to prioritization, with the caveat that a model explaining 31 percent of variation can flag facilities for a look but cannot prove anything about them (American Institute of Certified Public Accountants, 2017).
References
American Institute of Certified Public Accountants. (2017). Guide to audit data analytics. AICPA.
Cook, R. D. (1977). Detection of influential observation in linear regression. Technometrics, 19(1), 15-18. https://doi.org/10.1080/00401706.1977.10489493
Richardson, V. J., Teeter, R. A., & Terrell, K. L. (2023). Data analytics for accounting (3rd ed.). McGraw Hill.
What the AC570 Unit 6 assignment asks for
Unit 6 of AC570 asks you to fit a regression that sets an expectation, test its assumptions and use its residuals. State the model and why each driver should explain the outcome. Describe the observations and any exclusions with their reasons. Report the fitted coefficients and fit, and interpret them in plain terms, including what a modest R-squared means for how the model can be used. Run diagnostics: residuals against fitted values, the distribution of residuals and influence measures such as Cook's distance. Treat influential observations with a stated rule, removing only those with a documented cause and keeping unexplained ones. Refit if needed and compare. Then use residuals to score units, and state the model's limits. Support the method with Cook's paper on influential observations, a data analytics text and the AICPA guide in APA 7.
How this AC570 Unit 6 example is built
The sample states the model: credits rise with move-ins through promotions and with delinquent accounts through fee waivers. It uses 371 facility-months after excluding Facility 27's incomplete March. The first fit gives coefficients of $98.79 and $18.29 and R-squared of 0.280. Diagnostics find widening residual spread, right skew and Cook's distances of 0.123 for Facility 22's June and 0.106 for Facility 5's August. The flood month is documented and removed; the refit gives $97.61, $18.92 and 0.311. The highlighted sentence explains why Facility 22's June stays in. Scoring ranks Facility 22 by dollars and notes Facility 9's ten positive months, with the model's limits stated.
AC570 Unit 6 rubric: what earns full points
Regression exercises in AC570 earn points for a well-reasoned model, honest diagnostics and principled handling of influential points. Graders check that drivers are justified, coefficients interpreted and fit described accurately, including what a low R-squared implies. The rubric rewards examining residual plots and influence, documenting why any observation is removed, and keeping unexplained points that may be the exceptions the analysis seeks. Comparing fits before and after a removal shows robustness. Using residuals to screen units, while stating that the model cannot prove anything, demonstrates judgment. Exercises that report coefficients without diagnostics, or drop outliers to improve fit, score poorly. Cook (1977) and the course sources should appear in APA 7.
AC570 Unit 6 help from the desk
Weak Unit 6 exercises report a regression table and stop, with no diagnostics. Others drop every outlier to raise R-squared, removing the facilities that matter most. A low R-squared is either ignored or treated as making the model useless, when it limits the model to screening. Residuals are compared in raw dollars across facilities of very different sizes. Coefficients are not interpreted, so the reader cannot tell whether they make sense. Documented events are not separated from unexplained ones. If your section's dataset or drivers differ, a writer can fit and diagnose the model on your data.
Get an AC570 Unit 6 example written to your instructions
Name the relationship your Unit 6 prompt asks you to model and send the data, the rubric and any software requirement. The fit, its diagnostics and the reasoning behind any refit come back in 24-48h, the workbook behind every coefficient included so each figure can be retraced. First custom samples are free. 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.
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AC570 Unit 6 questions, answered
What are clustered standard errors, briefly?
Standard errors that allow observations within a group to be correlated, here the twelve months of one facility. Ordinary formulas assume every row is independent, which overstates precision when a site's high months tend to follow high months. Clustering widened every interval in the sample, most visibly for delinquent accounts. Many AC570 sections accept a sentence naming the problem if the software option is unavailable.
Where can I find a free AC570 Unit 6 sample paper?
This page holds a full AC570 Unit 6 regression exercise on monthly credits at Stowfield Storage with model, diagnostics, influence, a refit, facility scoring and APA 7 references.
Why remove Facility 5's August but keep Facility 22's June?
Facility 5 flooded in August and credited affected tenants by policy, a documented cause unrelated to the model's drivers. Facility 22's June has no known cause, so dropping it would hide exactly what the analysis exists to find.
What does an R-squared of 0.280 mean for the model's use?
The two drivers explain a little over a quarter of month-to-month variation. That makes the model a rough expectation for screening facilities, not a precise forecast, so residuals flag sites for a look rather than prove anything.
How are facilities scored from the model?
By their residuals summed over the year and by how many months were above expectation. Facility 22 leads on dollars, while Facility 9 shows a steady pattern of ten positive months worth $8,810.65.