A finished BU 407 Unit 3 forecast selection record: three methods run over one composite demand series, scored on shared error measures, with the choice defended. Searches like "bu 407 unit 3 assignment example", "bu407 unit 3 sample" and "bu 407 unit 3 example" land here.
What a finished BU 407 Unit 3 forecast selection record looks like
The finished record is a spreadsheet turned into a readable document. Demand history is printed once as a table and once as a line chart, so trend or seasonality is visible before any method is applied to it. Each method then gets its own block: the parameter it runs at, the period its first forecast covers, the fitted values, and the errors set beside the actual figures. One comparison table follows, holding the error measures for all three methods over an identical set of periods. A short passage says what each measure rewards and where it misleads, since a percentage measure behaves badly when demand runs near zero. The record closes on a single method, the parameter value chosen for it, and the point in the horizon at which that choice should be looked at again.
How a BU 407 Unit 3 example is structured
Charting the series comes before fitting anything, since a pattern noticed after the arithmetic tends to be the pattern the chosen method has produced. The naive forecast runs first and stays in the comparison to the last line, because it is the standard the other methods have to beat, and a record without it can only rank three methods against one another. Fitted values sit beside actuals rather than in a table of their own, which puts each error where a reader can check it against the data. Scoring covers a common set of periods for every method and says so, because a smoothing model that starts late has fewer errors to average and looks accurate for a reason unrelated to accuracy. An absolute measure and a percentage measure are reported together, and the chosen parameter closes the record with its review point.
The series charted before fitting
Trend and seasonality are read off the plotted history first, so no method is chosen to explain a pattern the arithmetic invented.
Naive forecast kept as the standard
The simplest possible method stays in the table to the end, because a model that cannot beat it has not earned the extra work.
Errors printed beside the actuals
Each fitted value carries its error in the adjacent column, which lets a reader verify any single figure without rebuilding the model.
Identical periods scored for every method
The comparison covers the same span for all three, since a method starting later averages fewer errors and looks better without being better.
Two measures rather than one
An absolute measure and a percentage measure appear together, because units matter to a manager and percentages misbehave when demand approaches zero.
Parameter value and review point named
The closing states the smoothing constant or window length settled on and when the selection should be tested again against fresh periods.
Where marks go in BU 407 Unit 3
Selection records lose points by ranking without a baseline. Three methods compared only against each other can crown a winner a naive forecast would beat, and rubrics in many sections ask for that check directly. Error measures averaged over different periods for different methods produce a comparison that is arithmetic rather than evidence. Signed errors summed into a total that cancels to nearly zero, then reported as accuracy, is the classic slip in this unit. A smoothing constant chosen with no reason attached leaves a marker guessing whether it was tuned or typed. Forecasts extended far past the data with no widening uncertainty overstate what any method offers. Sales history downloaded from an employer's system is not available for coursework, and a series invented so one method wins shows in the residuals.
Get a BU 407 Unit 3 example written to your instructions
Attach the Unit 3 instructions, the BU 407 rubric and whichever demand series your assignment supplies. We write a custom example that charts the history first, keeps a naive baseline in the comparison, scores every method over identical periods and defends the parameter it settles on. First custom sample free, returned in 24 to 48 hours.
BU 407 Unit 3 questions, answered
How do I choose the smoothing constant?
By testing rather than by taste. Run two or three values over the same history, report the error measures for each, and keep the one performing best on the periods you scored. Say what you tried. A constant near zero smooths heavily and reacts slowly, while a high one chases noise. Graders in many sections award the search itself, not only the value you end on.
Does the record need seasonal indices?
Only if the series shows seasonality and the instructions call for them. Where a repeating pattern is visible in the chart, a method ignoring it will lose to one that does not, and saying so is part of the selection. Where the history is short or flat, note that you looked and found nothing rather than leaving the question unmentioned.
Can I use sales figures from work?
Sales history sitting in an employer's system belongs to the employer, whatever access you happen to have. Where an assignment opens a door to data of your own, get permission first, remove anything identifying, or build a composite series with the shape your industry shows and note that you did. The method work scores the same either way.