A finished BUS-655 Topic 2 attendance driver model example, forecasting home crowds from held records, testing on a season the model never saw and converting forecasts into staffing bands. Searches like "bus 655 topic 2 assignment example", "bus655 topic 2 sample" and "bus-655 topic 2 example" land here.
What a finished BUS-655 Topic 2 attendance driver model looks like
The finished model is judged by what the front office does with it, not by its fit statistics alone. Paid attendance and turnstile scans are kept apart at the outset, since a sold ticket and an occupied seat drive different revenue. Illustrative, labeled data cover three seasons of home dates, and each driver enters with the reason it belongs: the prior season's crowd for the same opponent and month, day and start time, forecast rain, school calendars and the club's recent run of results. Promotions enter as a flag, so any apparent effect is read with everything else held steady. Coefficients are translated into fans, then into concession staff and open gate lanes. A held-back season checks the forecasts against games the model never saw, and the page reports the misses along with the hits.
How a BUS-655 Topic 2 example is structured
The model is presented as a working tool with its evidence underneath. A decision paragraph opens it, naming the two choices the forecast feeds: how many concession and gate staff to schedule, and which dates should carry the promotion budget. The data section follows, describing the held records, the seasons they cover, the definition of attendance chosen and the dates excluded, such as a rainout replayed as a doubleheader. Drivers are introduced one at a time, with a stated rationale and an expected direction for each. Estimates come next, reported in fans per game rather than raw coefficients. The holdout test follows, with forecast errors listed by date and the largest misses explained. A translation table converts the forecast into staffing levels for three demand bands. The final part names what the model cannot see, including walk-up crowds on a sudden warm evening.
Paid and scanned counted separately
Tickets sold and fans through the gate are tracked as two outcomes, because no-shows cost concession and parking revenue even when the ticket money is already banked.
Prior demand entered before anything else
Last season's crowd for the same opponent and month goes in first, since leaving it out lets a promotion or a weather term absorb what it would have explained.
Promotion kept as one flag among many
Giveaway and fireworks dates enter as a marker alongside the other drivers, so any lift credited to them is measured with opponent and weekday held steady.
A season held back for testing
The model is fitted on two seasons and checked against the third, and the forecast errors are listed game by game rather than folded into one summary figure.
Forecasts converted into staffing bands
Predicted crowds are sorted into low, middle and high bands, each with a concession and gate staffing level the operations manager can schedule a week ahead.
Where marks go in BUS-655 Topic 2
The failure that defines this course shows up here first: a model that forecasts attendance well and changes nothing the club does. A strong fit with no staffing plan, promotion schedule or pricing tier attached produces a number for the front office to admire. Leaving out last season's crowd for comparable dates, or the strength of the visiting team, costs the next block of credit, because their influence gets credited to the variables that remain, and promotion flags typically collect the surplus. Treating tickets sold as bodies in seats quietly misstates concession revenue on every heavily discounted date. Graders also look for an honest test, since fit judged only on the seasons used to build the model says nothing about next summer. Invented attendance presented as a real club's data is a defect; labeled illustrative data is expected.
Get a BUS-655 Topic 2 example written to your instructions
Send us the BUS-655 Topic 2 instructions, the rubric in your classroom and the data file or organization your section assigned. The custom example follows that rubric: attendance defined, drivers entered with reasons, a held-back season tested and the forecast turned into a staffing or promotion decision, within 24 to 48 hours. Your first one is free.
BUS-655 Topic 2 questions, answered
Which variables should an attendance model include?
Those the organization can observe before the game and that plausibly move demand: prior attendance for comparable dates, opponent, day and start time, the weather forecast, local school and holiday calendars, and recent results. Promotions belong in as well, so their effect is estimated fairly. Variables known only afterward, such as the final score, cannot help a forecast that has to be made days in advance.
How accurate does the forecast need to be?
Accurate enough for the decision it feeds. A staffing plan built on three demand bands needs the model to put games in the right band, not to predict the crowd to the last fan. Reporting errors in the decision's own terms, how often a game landed in the wrong band, tells a manager far more than a single fit statistic does.
Should the model be a regression or machine learning?
Whichever your course materials cover, as long as the result can be explained to the people using it. A regression with a handful of drivers shows which factors matter and by how much, which operations staff can check against experience. A more flexible model may forecast slightly better and explain less. The example uses regression and tests it on a held-back season either way.