| Course | GF585 Strategic Financial Planning and Forecasting Analytics |
|---|---|
| Unit | Unit 8 |
| Paper type | Trend forecasting exercise |
| Length | About 870 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MS in Finance |
| Updated | October 2026 |
Free sample paper for GF585 Unit 8
A Good Fit That Misleads: A Unit 8 Trend Forecasting Exercise on Healthcare Pounds
[Student Name]
Purdue University Global
GF585: Strategic Financial Planning and Forecasting Analytics
Unit 8 Trend Forecasting Exercise
[Instructor Name]
[Date]
The linen service and its twenty quarters of pounds are a composite practice dataset; the statistics were computed from that dataset; the articles and book cited are real.
The Method, Named First
The exercise fits a linear trend by ordinary least squares, with average weekly healthcare pounds as the outcome and the quarter number, 1 to 20, as the only predictor. Two other common methods were considered and set aside. A moving average would smooth the series but produce no slope to project. Exponential smoothing with a trend adapts to level changes only gradually and cannot say why the level moved (Hyndman & Athanasopoulos, 2021). The question here is whether a straight line describes five years of growth, so least squares is the right first test.
The Series
Average weekly healthcare pounds, in thousands, by quarter:
1. Quarters 1 to 5: 68.18, 68.83, 69.67, 70.08, 70.73.
2. Quarters 6 to 10: 71.45, 71.90, 72.75, 73.68, 73.71.
3. Quarters 11 to 15: 93.38, 93.88, 94.44, 95.02, 95.78.
4. Quarters 16 to 20: 96.21, 97.14, 97.49, 97.71, 98.63.
The jump between quarters 10 and 11, almost 20 thousand pounds a week, is visible in the list before any calculation.
The Linear Fit
The fitted line is pounds = 62.31 + 2.02 times the quarter number, in thousands. R squared is 0.861, meaning the straight line leaves only about a seventh of the quarter-to-quarter differences unexplained. Taken alone, that figure suggests a good model and a steady gain of about 2,000 pounds a week every quarter.
Testing the Fit With Residuals
Residuals, the actual value minus the fitted value, were listed in time order, because order is the evidence:
1. Quarters 1 to 3 are positive, from 3.85 down to 1.30.
2. Quarters 4 to 10 are negative, falling steadily to -8.81.
3. Quarters 11 to 17 are positive, starting at 8.84.
4. Quarters 18 to 20 are negative again, ending at -4.11.
Random errors would change sign often. These run in long blocks of one sign, which means the line is systematically too low in some periods and too high in others. The Durbin-Watson statistic, which compares successive residuals, is 0.80. Values near 2 indicate no correlation between neighboring errors; a value this far below 2 indicates strong positive correlation, confirming what the list shows (Durbin & Watson, 1950).
The line is not describing steady growth; it is spreading one step across twenty quarters, which is why it runs below the data before the step, above it just before, and below it again just after.
The Cause, From the Business Record
The step is not inferred from the numbers alone. The company's contract file shows that a hospital system's three outpatient centers began service in the first week of quarter eleven. Their volume, about 18 to 19 thousand pounds a week, arrived at once and has stayed level since.
A Model With a Step
A second model adds a step variable equal to 0 before quarter eleven and 1 from quarter eleven onward. The fitted equation is pounds = 67.74 + 0.61 times the quarter number + 18.77 times the step. The slope falls by about 70 percent, from 2.02 to 0.61 thousand pounds a quarter, and the step itself is sized at 18.77 thousand. R squared rises to 0.9998, and the Durbin-Watson statistic is 2.03, close to the value expected from uncorrelated errors. A formal test for a break at a known date, comparing regressions before and after it, points the same way (Chow, 1960).
The underlying growth, then, is about 600 pounds a week each quarter from clinics signing gradually. The rest of what the first line called growth was one contract.
Comparing the Forecasts
For the next four quarters, the linear trend projects 104.76, 106.78, 108.80 and 110.82 thousand pounds a week. The step model projects 99.32, 99.93, 100.55 and 101.16. By the fourth quarter the two differ by almost 10,000 pounds a week, about 2 percent of the plant's one-shift capacity, enough to change a staffing decision.
The step model is preferred. It explains the history with two separate forces, gradual clinic growth and one contract, each of which the sales team can confirm, while the straight line explains it with a growth rate no one in the company recognizes. Fitting only the ten quarters after the contract gives a slope of 0.58, close to the step model's 0.61, which supports the choice while keeping all twenty quarters in the estimate.
When Even the Step Model Would Fail
The step model assumes no further large contracts and no losses. If another hospital system signs, the series will jump again and the model will understate volume until a new step is added. If the existing system leaves, the model will overstate it by about 19 thousand pounds at once. The step model is therefore paired with the sales pipeline: any contract above 5,000 pounds a week is entered as a planned step rather than left for the trend to discover.
References
Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591-605. https://doi.org/10.2307/1910133
Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3-4), 409-428. https://doi.org/10.1093/biomet/37.3-4.409
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/
The Purdue Global GF585 Unit 8 instructions
The eighth GF585 unit asks you to fit a trend to a time series, judge whether it can be trusted and forecast from the better model. Name the method before using it and say briefly why alternatives such as moving averages or smoothing were set aside. Present the series in time order. Report the fitted equation and R squared, then test the fit by listing residuals in order and computing the Durbin-Watson statistic. If the residuals run in blocks, find the cause in the business record rather than in the numbers alone. Refit with a term that captures the cause, such as a step at a known date, and explain what changed in the slope. Compare forecasts from both models and say when even the better one would fail. Cite statistical sources in APA 7.
How this GF585 Unit 8 example is built
The method section names least squares on quarter numbers and sets aside moving averages and smoothing, citing Hyndman and Athanasopoulos. The twenty values follow in four rows of five. The linear fit is reported with its equation and an R squared of 0.861. Residuals are listed in four blocks, and the Durbin-Watson value of 0.80 is explained, citing Durbin and Watson. The highlighted sentence interprets the pattern as one step spread across twenty quarters. The contract file dates the step to quarter eleven. A step model gives a slope of 0.61, a jump of 18.77 and a Durbin-Watson of 2.03, citing Chow on known-date break tests. Forecasts are compared, a post-break fit with a slope of 0.58 supports the choice, and failure conditions close the exercise.
GF585 Unit 8 rubric: what earns full points
Points in GF585 trend exercises start with a named method and a correct fitted equation, but the larger share rewards what comes after the fit. Graders look for residuals examined in time order and a Durbin-Watson statistic interpreted, not just reported. Identifying the cause of a pattern from the business record, such as a contract date, earns more than speculating from the chart. A refit that captures the cause, with the change in slope explained in plain words, shows the core understanding the exercise is after. Instructors in the MS in Finance program value forecasts from both models set side by side, the difference measured in terms that matter operationally, and conditions under which the chosen model would break. Durbin and Watson, Chow and Hyndman and Athanasopoulos in APA 7 meet the source requirement.
GF585 Unit 8 help from the desk
The usual GF585 Unit 8 weakness is stopping at R squared, as if a high value proved the line is right. Residuals get charted but not read, or are sorted by size so the time pattern disappears. A Durbin-Watson value appears with no interpretation. Students who notice a jump sometimes delete the early quarters without saying so, or blame noise. Forecasts from the original line are presented as final. When a refit is done, the drop in slope is not explained. Whatever series your case supplies, keep the order: method named, data in time order, equation and fit, residuals in sequence with the Durbin-Watson statistic, the cause from records, a refit, forecasts compared and the conditions that would undo the model.
Get a GF585 Unit 8 example written to your instructions
The series your GF585 Unit 8 prompt supplies, any events recorded alongside it and the rubric are all that is needed. A custom exercise names the method precisely, reports the fit, reads residuals in time order, ties any break to a business event and says when the replacement would fail. The first carries no charge and generally lands within 24-48h. 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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GF585 Unit 8 questions, answered
What Durbin-Watson value signals a problem?
Values near 2 suggest residuals are not correlated from one period to the next; values well below 2 suggest positive correlation, meaning the line is missing a pattern. Exact cutoffs depend on sample size and the number of predictors, so the sample cites a critical-value table rather than a rule of thumb. At 0.79 with twenty observations, the evidence against the simple line is strong.
Could exponential smoothing have handled the jump?
A smoothing method with a trend component adapts after a jump, but only gradually, and it cannot say why the level moved. The sample names smoothing as an alternative and sets it aside because the break has a known cause and date, which a step variable represents directly. If your data show drift rather than a single jump, smoothing may be the better choice.
Should the line be refit on data after the break only?
That is a reasonable alternative, and the sample reports it: ten quarters after the contract give a slope of 0.58, close to the step model's 0.61. The cost is discarding half the history. A step variable keeps all twenty quarters while respecting the break, the reason the sample prefers it, but either choice can be defended if it is explained.
Where can I find a free GF585 Unit 8 sample paper?
A complete GF585 Unit 8 trend forecasting exercise is on this page, testing a linear fit to twenty quarters of healthcare pounds, adding a step for a contract, comparing forecasts, with APA 7 references.
Why did the slope fall from 2.02 to 0.61?
The first line had to spread one sudden jump of about 18.8 thousand pounds across all twenty quarters, which inflated the slope. Once a step variable captures the jump, the slope measures only the gradual growth from clinics.