Across 371 facility-months, AC570's Unit 6 regression exercise models monthly rent credits on move-ins and delinquencies, then checks independence, variance and influence before trusting the line. Searches like "ac 570 unit 6 assignment example", "ac570 unit 6 sample" and "ac570 unit 6 example" land here.
What a finished AC570 Unit 6 regression exercise looks like
A model statement, two estimation tables, four diagnostic panels and a scoring page. The first table fits all 371 facility-months, with Facility 27's broken March left out: credits equal 1,128.49 plus 98.79 per move-in plus 18.29 per delinquent account, R-squared 0.280. Diagnostics follow. Cook's distance singles out Facility 22 in June at 0.123 and Facility 5 in August at 0.106, the latter roughly nine standard errors above its fitted value after roof-leak goodwill credits. Residuals within a facility correlate at 0.20 from month to month, and clustered standard errors by facility cut the delinquency t-statistic from 3.1 to 1.6. The second table refits 358 facility-months without the leak month and Facility 22: 105.38 per move-in, 29.34 per delinquent account, R-squared 0.474.
How a AC570 Unit 6 example is structured
The exercise states its expectation in words before any number appears, naming the business mechanism behind each predictor, so a coefficient can be judged against the rate card and the fee schedule. The first fit is reported plainly, then questioned. Influence leads the checks, because one documented event, forty goodwill credits after a roof leak, can drag a line toward itself. Independence follows: twelve months from one site are not twelve unrelated observations, so standard errors are recomputed with clustering by facility. Constant variance is tested with Breusch-Pagan, which lands at 6.04 in the refit, just past its 5.99 critical value, as residual spread rises from 856 to 1,047 across fitted terciles. The refit excludes the leak month for a stated reason and Facility 22 because it is under question, and the scoring page then applies the refit to that facility.
An expectation before a fit
Each promotional move-in should cost roughly a month of street rent less a dollar, and each delinquent account a share of waived fees. Coefficients are read against those prices.
The first line, and its pull
R-squared of 0.280 across 371 facility-months. Two points carry unusual weight: Facility 22 in June and Facility 5 in August, whose residual exceeds 12,000 dollars.
Twelve months are not twelve observations
Residuals correlate 0.20 month to month within a site. Clustered errors widen every interval, and the delinquency effect stops looking reliable in the first fit.
A refit with reasons
Leak month out, documented; Facility 22 out, under review. On 358 facility-months the move-in coefficient is 105.38, clustered t 9.6, and delinquency returns at 29.34, t 4.1.
Scoring the facility left out
Against the refit, Facility 22 books 36,730.50 more in credits than its move-ins and delinquencies predict, above expectation in 11 of 12 months.
Where marks go in AC570 Unit 6
Graders in AC570 look first at whether the fitted line is interrogated or simply reported, and an exercise that stops at coefficients and R-squared forfeits the assumption criterion the unit is built to test. Standard errors computed as if facility-months were independent overstate precision; the delinquency effect in this sample looks firm until clustering halves its t-statistic. Deleting the leak month without the maintenance record that justifies it looks like tuning. Refitting without Facility 22 and then never scoring it wastes the point of excluding it. Coefficients interpreted with causal language, as though move-ins produce credits by themselves, draw comments even where the mechanism is plausible. A residual review naming no observation, or diagnostics pasted without a sentence on what each one changed, earns little.
Get a 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.
AC570 Unit 6 questions, answered
Why exclude an observation instead of keeping every row?
Only with a documented reason, and with both fits shown. The August leak month at Facility 5 holds forty goodwill credits tied to a maintenance record, so it describes an event the model was never meant to predict. The AC570 sample reports coefficients with and without it; the move-in effect shifts from 98.79 to 96.35, which tells a reader how much the exclusion mattered.
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.
Is an R-squared of 0.474 good enough?
It depends on the use. The model is an expectation for spotting facilities that credit more than their activity explains, not a forecast of next month's credits. For that purpose a stable coefficient with a clear business meaning matters more than a high R-squared. The sample says what the unexplained variation likely contains, manual adjustments no ledger driver captures, rather than labeling the figure good or bad.