Why does a line with R squared of 0.861 overshoot by 9.6 percent within a year? This GF585 Unit 8 trend forecasting exercise traces the miss to a one-time step. Searches like "gf 585 unit 8 assignment example", "gf585 unit 8 sample" and "gf585 unit 8 example" land here.
What a finished GF585 Unit 8 trend forecasting exercise looks like
About four pages with two charts, a residual table and a forecast comparison. The series is average weekly healthcare pounds, in thousands, from 68.40 in the first quarter to 97.75 in the twentieth, with a jump from 73.85 to 93.30 when a hospital system signed. Ordinary least squares on a time index gives an intercept of 62.13 and a slope of 2.02 thousand pounds a quarter. Residuals then run positive for three quarters, negative for seven, positive for seven and negative again, and the Durbin-Watson statistic is 0.79. Adding a step variable at quarter eleven cuts the slope to 0.61, sizes the jump at 18.8 thousand and lifts R squared to 0.999. The two models forecast the next four quarters at 104.65 to 110.72 and at 99.21 to 101.04.
How a GF585 Unit 8 example is structured
Method is named before it is used: a linear trend fitted by ordinary least squares on quarter numbers, which the exercise distinguishes from moving averages and exponential smoothing, both mentioned and set aside. The fit is reported with its equation and R squared, then immediately tested. Residuals are listed and charted in time order, because the order is the evidence; a run of seven same-signed residuals is not noise. The Durbin-Watson statistic confirms what the chart shows. The cause is identified from the business record, a contract start date, not inferred from the numbers alone. A second model adds a step variable, and the exercise explains why the slope falls by about 70 percent: the first line had spread one jump across twenty quarters. Forecasts from both models are compared, and conditions under which even the step model would fail close the paper.
The method named exactly
Ordinary least squares on a quarter index is a regression on time, a point stated outright, with moving averages and smoothing set aside as different tools.
A fit that flatters
An R squared of 0.861 and a slope of 2.02 thousand pounds a quarter look strong until the residuals are read in the order they occurred.
Four runs in twenty residuals
Positive, then seven negative, then seven positive, then negative: a pattern the Durbin-Watson value of 0.79 confirms is not random scatter.
One contract, one step
The jump from 73.85 to 93.30 matches a hospital system's start date, and a step variable sized at 18.8 thousand pounds absorbs it.
Two forecasts a year apart
The first line reaches 110.72 in the fourth quarter ahead against 101.04 from the step model, an overstatement of 9.6 percent.
Where marks go in GF585 Unit 8
Reporting a high R squared as proof that the line is good is the error this exercise is most often built to catch. Graders look for residuals examined in time order and a named reason when they run in blocks; a residual plot presented without comment earns little. Calling a least-squares trend exponential smoothing, or treating a moving average as a model with a slope, costs accuracy credit, because the unit asks for methods named correctly. A structural break found in the data but never tied to an event in the business leaves the diagnosis half done. Forecasts extended from a failed line without a warning are marked down harder than the failure itself. The last credit usually goes to conditions: what future event, a lost contract or a new competitor, would break the replacement model too.
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.
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.